MLOps Landscape in 2024: Top Tools and Platforms

As you delve into the landscape of MLOps in 2024, you will find a plethora of tools and platforms that have gained traction and are shaping the way models are developed, deployed, and monitored. To provide you with a comprehensive overview, this article explores the key players in the MLOps and FMOps (or LLMOps) ecosystems, encompassing both open-source and closed-source tools, with a focus on highlighting their key features and contributions.

MLOps landscape

One of the defining characteristics of the MLOps landscape in 2024 is the coexistence of both open-source and closed-source solutions. Open-source MLOps tools have gained significant traction due to their flexibility, community support, and adaptability to various workflows. On the other hand, closed-source platforms often provide enterprise-grade features, enhanced security, and dedicated user support.

Here’s an overview diagram of what the landscape looks like in 2024:

The rest of this article will focus on highlighting over 90 MLOps tools and platforms on the market in 2024 in the following categories:

• End-to-end Machine Learning Operations (MLOps) platforms
• Experiment tracking, model metadata storage and management
• Dataset labeling and annotation
• Data storage and versioning
• Data quality monitoring and management
• Feature stores
• Model hubs
• Model quality testing
• Workflow orchestration and pipelining tools
• Model deployment and serving
• Model observability
• Responsible AI
• Compute and infrastructure
• GPU Cloud Servers
• [NEW] Serverless GPUs
• [NEW] Vector databases and data retrieval
• [NEW] Foundation model training frameworks

By providing an inclusive overview of the LLMOps and MLOps tools and MLOps platforms that emerged in 2024, this article will equip you with a better understanding of the diverse tooling landscape, enabling you to make informed decisions in your MLOps journey.

How to evaluate MLOps tools and platforms

Like every software solution, evaluating MLOps (Machine Learning Operations) tools and platforms can be a complex task as it requires consideration of varying factors. Below, you will find some key factors to consider when assessing MLOps tools and platforms, depending on your needs and preferences.

Cloud and technology strategy

Choose an MLOps tool that aligns with your cloud provider or technology stack and supports the frameworks and languages you use for ML development. For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services.

Alignment to other tools in the organization’s tech stack

Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. For example, neptune.ai as an experiment tracker, integrates with over 30 MLOps tools and platforms.

Commercial details

Consider the commercial details when evaluating MLOps tools and platforms. Assess the pricing models, including any hidden costs, and ensure they fit your budget and scaling requirements. Review vendor support and maintenance terms (SLAs and SLOs), contractual agreements, and negotiation flexibility to align with your organization’s needs. Free trials or proof of concepts (PoCs) can help you evaluate the tool’s value before committing to a commercial agreement.

Knowledge and skills in the organization

Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., and Pandas or Apache Spark DataFrames.

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Key use cases and/or user journeys

Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively. For example, if your team works on recommender systems or natural language processing applications, you may want an MLOps tool that has built-in algorithms or templates for these use cases.

User support arrangements

Consider the availability and quality of support from the provider or vendor, including documentation, tutorials, forums, customer service, etc. Also, check the frequency and stability of updates and improvements to the tool.

Active user community and future roadmap

Consider a tool that has a strong and active community of users and developers who can provide feedback, insights, and best practices. In addition to considering the vendor’s reputation, ensure you can be positioned to receive updates, see the roadmap of the tool, and see how they align with your goal.

End-to-end MLOps platforms

End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. 

Core features of end-to-end MLOps platforms

End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include:

• Data management and preprocessing: Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation. This includes features for data labeling, data versioning, data augmentation, and integration with popular data storage systems.
• Experimentation and model development: Platforms should offer features for you to design and run experiments, explore different algorithms and architectures, and optimize model performance. This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics.
• Model deployment and serving: Enable seamless model deployment and serving by providing features for containerization, API management, and scalable serving infrastructure.
• Model monitoring and performance tracking: Platforms should include capabilities to monitor and track the performance of deployed ML models in real-time. This includes features for logging, monitoring model metrics, detecting anomalies, and alerting, allowing you to ensure the reliability, stability, and optimal performance of your models.
• Collaboration and version control: Support collaboration among data and ML teams, allowing them to share code, models, and experiments. They should also offer version control capabilities to manage the changes and revisions of ML artifacts, ensuring reproducibility and facilitating effective teamwork.
• Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines. This includes features for dependency management, task scheduling, and error handling, simplifying the management and execution of ML workflows.
• Model governance and compliance: They should address model governance and compliance requirements, so you can implement ethical considerations, privacy safeguards, and regulatory compliance into your ML solutions. This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking.
• Integration with ML tools and libraries: Provide you with flexibility and extensibility. This allows you to leverage your preferred ML tools and access a wide range of resources, enhancing productivity and enabling the use of cutting-edge techniques.

Amazon SageMaker

Amazon SageMaker provides a unified interface for data preprocessing, model training, and experimentation, allowing data scientists to collaborate and share code easily. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

Microsoft Azure ML Platform

The Azure Machine Learning platform provides a collaborative workspace that supports various programming languages and frameworks. With Azure Machine Learning, data scientists can leverage pre-built models, automate machine learning tasks, and seamlessly integrate with other Azure services, making it an efficient and scalable solution for machine learning projects in the cloud.

Google Cloud Vertex AI

Google Cloud Vertex AI provides a unified environment for both automated model development with AutoML and custom model training using popular frameworks. With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale.

Modelbit

Modelbit is an MLOps platform that covers model training, deployment, and lifecycle management. It provides auto-scaling infrastructure, allowing users to access CPU and GPU resources as needed while abstracting the complex GPU/CUDA setup.

Modelbit’s endpoints support request aliasing, splitting, and mirroring, allowing for complex rollout and testing patterns. The platform comes with built-in dataset management and feature store capabilities and integrates with dbt, Snowflake, Amazon Redshift, and Amazon Athena.

Domino Enterprise MLOps Platform

The Domino Enterprise MLOps Platform provides:

• A system of record for reproducible and reusable workflows.
• An integrated model factory to develop, deploy, and monitor models in one place using your preferred tools and languages.
• A self-service infrastructure portal for infrastructure and governance.

Databricks

Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. The platform gives you a unified set of tools for enterprise‑grade solutions for everything you need to do with data, including building, deploying, sharing, and maintaining solutions that have to do with data.

DataRobot

DataRobot MLOps offers features such as automated model deployment, monitoring, and governance. DataRobot MLOps facilitates collaboration between data scientists, data engineers, and IT operations, ensuring smooth integration of models into the production environment.

W&B (Weights & Biases)

W&B is a machine learning platform for your data science teams to track experiments, version and iterate on datasets, evaluate model performance, reproduce models, visualize results, spot regressions, and share findings with colleagues. The platform also offers features for hyperparameter optimization, automating model training workflows, model management, prompt engineering, and no-code ML app development.

Valohai

Valohai provides a collaborative environment for managing and automating machine learning projects. With Valohai, you can define pipelines, track changes, and run experiments on cloud resources or your own infrastructure. It simplifies the machine learning workflow and offers features for version control, data management, and scalability.

TrueFoundry

TrueFoundry is a cloud-native ML training and deployment PaaS on top of Kubernetes that enables ML teams to train and Deploy models at speed of Big Tech with 100% reliability and scalability – allowing them to save cost and release Models to production faster.

It abstracts Kubernetes from Data Scientists and enable them to work with infrastructure comfortably. It also allows teams to deploy and fine-tune large language models seamlessly with full security and cost optimization.

TrueFoundry is open-ended, API-driven and integrates with the internal systems. You can also deploy it on a company’s internal infrastructure and ensures complete Data Privacy and DevSecOps practices. Take a look at this introductory article to learn more about TrueFoundry.

Kubeflow

Kubeflow is an open-source machine learning platform built for running scalable and portable ML workloads on Kubernetes. It provides tools and components to facilitate end-to-end ML workflows, including data preprocessing, training, serving, and monitoring. 

Kubeflow integrates with popular ML frameworks, supports versioning and collaboration, and simplifies the deployment and management of ML pipelines on Kubernetes clusters. Check out the Kubeflow documentation.

Metaflow

Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects. It provides a high-level API that makes it easy to define and execute data science workflows. It also provides a number of features that help improve the reproducibility and reliability of data science projects. Netflix runs hundreds to thousands of ML projects on Metaflow—that’s how scalable it is.

You can use Metaflow for research, development, and production and integrate it with a variety of other tools and services. Check out the Metaflow Docs.

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Experiment tracking, model metadata storage, and management

Experiment tracking and model metadata management tools provide you with the ability to track experiment parameters, metrics, and visualizations, ensuring reproducibility and facilitating collaboration. 

When thinking about a tool for metadata storage and management, you should consider:

• General business-related items: Pricing model, security, and support.
• Setup: How much infrastructure is needed, and how easy is it to plug into your workflow?
• Flexibility, speed, and accessibility: can you customize the metadata structure? Is it accessible from your language/framework/infrastructure, framework, or infrastructure? Is it fast and reliable enough for your workflow?
• Model versioning, lineage, and packaging: Can you version and reproduce models and experiments? Can you see the complete model lineage with data/models/experiments used downstream?
• Log and display of metadata: what metadata types are supported in the API and UI? Can you render audio/video? What do you get out of the box for your frameworks?
• Comparing and visualizing experiments and models: what visualizations are supported, and does it have parallel coordinate plots? Can you compare images? Can you debug system information?
• Organizing and searching experiments, models, and related metadata: can you manage your workflow in a clean way in the tool? Can you customize the UI to your needs? Can you find experiments and models easily?
• Model review, collaboration, and sharing: can you approve models automatically and manually before moving to production? Can you comment and discuss experiments with your team?
• CI/CD/CT compatibility: how well does it work with CI/CD tools? Does it support continuous training/testing (CT)?
• Integrations and support: does it integrate with your model training frameworks? Can you use it inside orchestration and pipeline tools?

Depending on whether your model metadata problems are on the side of research or productization, you may want to compare and choose a more specific solution: 

• Experiment tracking tools
• Model registry
• ML metadata store

MLflow

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides experiment tracking, versioning, and deployment capabilities. With MLflow, data science teams can easily log and compare experiments, track metrics, and organize their models and artifacts.

neptune.ai

neptune.ai is an ML metadata store that was built for research and production teams that run many experiments. It allows teams to log and visualize experiments, track hyperparameters, metrics, and output files. Neptune provides collaboration features, such as sharing experiments and results, making it easier for teams to work together. It has 20+ integrations with MLOps tools and libraries you are likely already using. 

Might be useful

Unlike manual, homegrown, or open-source solutions, neptune.ai is a scalable full-fledged component with user access management, developer-friendly UX, and collaboration features.

That’s especially valuable for ML teams. Here’s an example of how Neptune helped AI teams at Waabi optimize their experiment tracking workflow.

The product has been very helpful for our experimentation workflows. Almost all the projects in our company are now using Neptune for experiment tracking, and it seems to satisfy all our current needs. It’s also great that all these experiments are available to view for everyone in the organization, making it very easy to reference experimental runs and share results. James Tu, Research Scientist at Waabi

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Comet ML

Comet ML is a cloud-based experiment tracking and optimization platform. It enables data scientists to log, compare, and visualize experiments, track code, hyperparameters, metrics, and outputs. Comet offers interactive visualizations, collaboration features, and integration with popular ML libraries, making it a comprehensive solution for experiment tracking.

AimStack

AimStack is an open-source AI metadata tracking tool designed to handle thousands of tracked metadata sequences. It provides a performant and intuitive UI for exploring and comparing training runs, prompt sessions, and more. It can help you track the progress of your experiments, compare different approaches, and identify areas for improvement.

Dataset labeling and annotation

Dataset labeling and annotation tools form a critical component of machine learning (ML) systems, enabling you to prepare high-quality training data for their models. These tools provide a streamlined workflow for annotating data, ensuring accurate and consistent labeling that fuels model training and evaluation. 

Core features of dataset labeling and annotation tools

Dataset labeling and annotation tools should include: 

• Support for your data modalities: Support for multiple data types, including audio, parquet, video, text data, and special dataset types like sensor readings and 3D magnetic resonance imaging (MRI) medical datasets.
• Efficient collaboration: They must facilitate seamless collaboration among annotators, enabling multiple users to work simultaneously, track progress, assign tasks, and communicate effectively, ensuring efficient annotation workflows.
• Robust and customizable annotation interfaces: User-friendly and customizable annotation interfaces empower annotators to easily label and annotate data, offering features like bounding boxes, polygons, keypoints, and text labels, enhancing the accuracy and consistency of annotations.
• Integration with ML frameworks: Seamless integration with popular ML frameworks allows annotated datasets to be directly used for model training and evaluation, eliminating data transformation complexities and enhancing the ML development workflow.
• Versioning and auditing: Provide features to track and manage different versions of annotations, along with comprehensive auditing capabilities, ensuring transparency, reproducibility, and accountability throughout the annotation process.
• Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations.
• Seamless data export: Dataset labeling and annotation tools should support the seamless export of annotated data in various formats (e.g., JSON, CSV, TFRecord) compatible with downstream ML pipelines, facilitating the integration of annotated datasets into ML workflows.

The alternatives for data labeling in 2024 range from tools and services that support expert labelers to crowdsourcing services, third-party annotators, and programmatic labeling.

Labelbox

Labelbox is a data labeling platform that provides a range of features and capabilities to streamline the data labeling process and ensure high-quality annotations, such as collaborative annotation, quality control, and automation capabilities.

Amazon SageMaker Ground Truth

SageMaker Ground Truth is a fully managed data labeling service designed to help you efficiently label and annotate your training data with high-quality annotations. Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on.

Scale AI

Scale AI is a data annotation platform that provides various annotation tools for image, video, and text data, including object detection, semantic segmentation, and natural language processing. Scale AI combines human annotators and machine learning algorithms to deliver efficient and reliable annotations for your team.

SuperAnnotate

SuperAnnotate specializes in image and video annotation tasks. The platform provides a comprehensive set of annotation tools, including object detection, segmentation, and classification. 

With features like collaborative annotation, quality control, and customizable workflows, SuperAnnotate empowers data science and machine learning teams to efficiently annotate their training data with high accuracy and precision. 

Snorkel Flow

Snorkel Flow is a data-centric AI platform for automated data labeling, integrated model training and analysis, and enhanced domain expert collaboration. The platform’s labeling capabilities include flexible label function creation, auto-labeling, active learning, and so on.

Kili

Kili is a cloud-based platform that can be accessed from anywhere for data scientists, machine learning engineers, and business users to label data more efficiently and effectively. It provides a variety of features that can help improve the quality and accuracy of labeled data, including:

• Labeling tools.
• Quality control.
• Collaboration.
• Reporting.

Encord Annotate

Encord Annotate is an automated annotation platform that performs AI-assisted image annotation, video annotation, and dataset management. It is part of the Encord suite of products alongside Encord Active. The key features of Encord Annotate include:

• Support for all annotation types.
• Auto-annotation tools such as Meta’s Segment Anything Model and other AI-assisted labeling techniques.
• MLOps workflows for computer vision and ML teams
• Use-case-centric annotations.
• Easy collaboration, annotator management, and QA workflows.
• Robust security functionality.

Superb AI

Superb AI provides a suite of tools to help data practitioners and ML Engineers effortlessly label, manage, and curate training datasets for computer vision. The labelling component supports and extensive range of annotation types and collaborative features for all stakeholders involved in the process (SMEs, data practitioners, engineers, ec cetera). You can also automate the process using a no-code UI and limited ground truth data with the help of models fine-tuned to your use case.

Other features let yoy manage your team and datasets with quality control tools, project analytics, and user reports to ensure accurate and consistent annotations. Here you can learn more about Superb AI.

Data storage and versioning 

You need data storage and versioning tools to maintain data integrity, enable collaboration, facilitate the reproducibility of experiments and analyses, and ensure accurate ML model development and deployment. Versioning allows you to trace and compare different iterations of datasets.

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Core features of dataset storage and versioning tools

Robust dataset storage and versioning tools should provide: 

• Secure and scalable storage: Dataset storage and versioning tools should provide a secure and scalable infrastructure to store large volumes of data, ensuring data privacy and availability for you to access and manage datasets.
• Dataset version control: The ability to track, manage, and version datasets is crucial for reproducibility and experimentation. Tools should allow you to easily create, update, compare, and revert dataset versions, enabling efficient management of dataset changes throughout the ML development process.
• Metadata management: Robust metadata management capabilities enable you to associate relevant information, such as dataset descriptions, annotations, preprocessing steps, and licensing details, with the datasets, facilitating better organization and understanding of the data.
• Collaborative workflows: Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among ML engineers, data scientists, and other stakeholders.
• Data Integrity and consistency: These tools should ensure data integrity by implementing checksums or hash functions to detect and prevent data corruption, maintaining the consistency and reliability of the datasets over time.
• Integration with ML frameworks: Seamless integration with popular ML frameworks allows you to directly access and utilize the stored datasets within your ML pipelines, simplifying data loading, preprocessing, and model training processes.

DVC

DVC is an open-source tool for versioning datasets and models. It integrates with Git and provides a Git-like interface for data versioning, allowing you to track changes, manage branches, and collaborate with data teams effectively.

Dolt

Dolt is an open-source relational database system built on Git. It combines the capabilities of a traditional database with the versioning and collaboration features of Git. Dolt allows you to version (integration with DVC) and manage structured data, making tracking changes, collaborating, and maintaining data integrity easier.

LakeFS

LakeFS is an open-source platform that provides data lake versioning and management capabilities. It sits between the data lake and cloud object storage, allowing you to version and control changes to data lakes at scale. LakeFS facilitates data reproducibility, collaboration, and data governance within the data lake environment.

Pachyderm

Pachyderm is an open-source data versioning and lineage tool focusing on large-scale data processing and versioning. It provides data lineage tracking, versioning, and reproducibility features, making it suitable for managing complex data science workflows.

Delta Lake

Delta Lake is an open-source storage layer that provides reliability, ACID transactions, and data versioning for big data processing frameworks such as Apache Spark. Your data team can manage large-scale, structured, and unstructured data with high performance and durability. Delta Lake helps ensure data consistency and enables efficient versioning and management within big data workflows.

Data quality monitoring and management

You may want to continuously observe data quality, consistency, and distribution to identify anomalies or shifts that may impact model performance. Data monitoring tools help monitor the quality of the data. Data management encompasses organizing, storing, and governing data assets effectively, ensuring accessibility, security, and compliance. 

These practices are vital for maintaining data integrity, enabling collaboration, facilitating reproducibility, and supporting reliable and accurate machine learning model development and deployment.

Core features of data quality monitoring and management tools

Data quality monitoring and management offer capabilities such as:

• Data profiling: Tools should provide comprehensive data profiling capabilities, allowing you to analyze and understand the characteristics, statistics, and distributions of your datasets, enabling better insights into data quality issues.
• Anomaly detection: Effective anomaly detection mechanisms can enable you to identify and flag outliers, missing values, and other data anomalies that could impact the accuracy and performance of ML models.
• Data validation: Tools should facilitate data validation by allowing you to define validation rules and perform checks to ensure that the dataset adheres to predefined criteria and standards.
• Data cleansing: The ability to detect and correct data errors, inconsistencies, and outliers is crucial for maintaining high-quality datasets. Tools should offer features for data cleansing, including data imputation, outlier removal, and noise reduction techniques.
• Integration with ML workflows: Integration with ML workflows and pipelines can enable you to incorporate data quality monitoring and management processes into your overall ML development workflow, ensuring ongoing monitoring and improvement of data quality.
• Automation and alerting: Tools should provide automation capabilities to streamline data quality monitoring tasks, along with alerting mechanisms to notify you of potential data quality issues, facilitating timely remediation.
• Documentation and auditing: The availability of documentation and auditing features allows ML engineers to track data quality changes over time, ensuring transparency, reproducibility, and compliance with data governance policies.

Great Expectations

Great Expectations is an open-source library for data quality validation and monitoring. You can define expectations about data quality, track data drift, and monitor changes in data distributions over time. Great Expectations provides data profiling, anomaly detection, and validation features, ensuring high-quality data for machine learning workflows.

Talend Data Quality

Talend Data Quality is a comprehensive data quality management tool with data profiling, cleansing, and monitoring features. With Talend, you can assess data quality, identify anomalies, and implement data cleansing processes.

Monte Carlo

Monte Carlo is a popular data observability platform that provides real-time monitoring and alerting for data quality issues. It could help you detect and prevent data pipeline failures, data drift, and anomalies. Montecarlo offers data quality checks, profiling, and monitoring capabilities to ensure high-quality and reliable data for machine learning and analytics.

Soda Core

Soda Core is an open-source data quality management framework for SQL, Spark, and Pandas-accessible data. You can define and validate data quality checks, monitor data pipelines, and identify anomalies in real-time.

Metaplane

Metaplane is a data quality monitoring and management platform offering features for data profiling, quality checks, and lineage. It provides visibility into data pipelines, monitors data quality in real-time, and can help you identify and address data issues. Metaplane supports collaboration, anomaly detection, and data quality rule management.

Databand

Databand is a data pipeline observability platform that monitors and manages data workflows. It offers features for data lineage, data quality monitoring, and data pipeline orchestration. You can track data quality, identify performance bottlenecks, and improve the reliability of their data pipelines.

Feature stores

Feature stores provide a centralized repository for storing, managing, and serving ML features, enabling you to find and share feature values for both model training and serving.

Core features of feature stores

Robust feature store tools should offer capabilities such as:

• Feature engineering pipelines: Effective feature store tools allow you to define and manage feature engineering pipelines that include data transformation and feature extraction steps to generate high-quality ML features.
• Feature serving: Feature store tools should offer efficient serving capabilities, so you can retrieve and serve ML features for model training, inference, and real-time predictions.
• Scalability and performance: Feature store tools should provide scalability and performance optimizations to handle large volumes of data and support real-time feature retrieval, ensuring efficient and responsive ML workflows.
• Feature versioning: Tools should support versioning of ML features, allowing you to track changes, compare different versions, and ensure features processing techniques are consistent for training and serving ML models.
• Feature validation: Tools should provide mechanisms for validating the quality and integrity of ML features, enabling you to detect data inconsistencies, missing values, and outliers that may impact the accuracy and performance of ML models.
• Feature metadata management: Tools should support managing metadata associated with ML features, including descriptions, data sources, transformation logic, and statistical properties, to enhance transparency and documentation.
• Integration with ML workflows: Integration with ML workflows and pipelines facilitate the integration of feature engineering and feature serving processes into the overall ML development lifecycle. This can help you make model development workflows reproducible.

More companies started building feature stores and self-serve feature platforms to allow sharing and discovery of features across teams and projects.

Feast

Feast is an open-source feature store with a centralized and scalable platform for managing, serving, and discovering features in MLOps workflows. You can define, store, and serve features for training and inference in machine learning models. Feast supports batch and real-time feature serving, enabling teams to efficiently access and reuse features across different stages of the ML lifecycle.

Tecton

Tecton is a feature platform designed to manage the end-to-end lifecycle of features. It integrates with existing data stores and provides components for feature engineering, feature storage, serving, and monitoring, helping your team improve productivity and operationalize their ML pipelines.

Hopsworks Feature Store

Hopsworks Feature Store is an open-source feature platform for data-intensive ML workloads. You can use Hopsworks Feature Store to build, manage, and serve features for machine learning models while ensuring data lineage, governance, and collaboration. This provides end-to-end support for data engineering and MLOps workflows.

Featureform

Featureform is an open-source virtual feature store that can be used with any data infrastructure. It can help data science teams:

• Break feature engineering silos,
• Manage features over time through versioning.
• Share features across the organization.
• Provide tools for managing feature quality, including data profiling, feature drift detection, and feature impact analysis.

Databricks Feature Stores

Databricks Feature Store is a centralized and scalable solution for managing features in machine learning workflows. You can leverage its unified repository to store, discover, and serve features, eliminating duplication and promoting code reusability. Integration with Apache Spark and Delta Lake enables efficient data processing and ensures data integrity and versioning. It offers offline (primarily for batch inference) and online stores (low-latency DB for real-time scoring).

With features like versioning, metadata management, point-in-time lookups, and data lineage, Databricks Feature Store enhances collaboration, improves productivity, and allows your data scientists to focus on model development rather than repetitive feature engineering tasks.

Google Cloud Vertex AI Feature Store

Vertex AI Feature Store is a feature management service that can provide your team with the capabilities for storing, discovering, and serving features for machine learning workloads.

With the Vertex AI Feature Store, your data scientists can access and reuse features across projects, leverage versioning and metadata management capabilities, and integrate seamlessly with other Google Cloud services to streamline their MLOps pipelines.

Model hubs

Model hubs provide a centralized platform for managing, sharing, and deploying ML models. They empower you to streamline model management, foster collaboration, and accelerate the deployment of ML models.

Core features of model hubs

Model hubs should offer features such as:

• Model discovery: Model hub tools offer search and discovery functionalities to explore and find relevant models based on criteria such as performance metrics, domain, architecture, or specific requirements.
• Model sharing: Tools should provide mechanisms for sharing ML models with other team members or across the organization, fostering collaboration, knowledge sharing, and reuse of pre-trained models.
• Model metadata management: Tools should support the management of metadata associated with ML models, including descriptions, the kinds of tasks they solve, performance metrics, training configurations, and version history, facilitating model documentation and reproducibility.
• Integration with ML workflows: Integration with ML workflows and pipelines allows you to incorporate model hub functionalities into your ML development lifecycle, simplifying model training, evaluation, and deployment processes.
• Model governance and access control: Model hub tools should provide governance features to set access controls, usage licenses, permissions, and sharing policies to ensure data privacy, security, and compliance with regulatory requirements. A good implementation of this can be the inclusion of model cards.
• Model deployment: Model hub tools should provide inference APIs to test the model’s capabilities and enable seamless deployment of ML models to various environments, including cloud platforms, edge devices, or on-premises infrastructure.
• Model versioning: Tools should support versioning of ML models within the model hub to track changes, compare different versions, and ensure reproducibility when training and deploying ML models.

Hugging Face Models Hubs

The Hugging Face Model Hub is a popular platform and ecosystem for sharing, discovering, and utilizing pre-trained models for different ML tasks. Members of the Hugging Face community can host all of their model checkpoints for simple storage, discovery, and sharing. It offers a vast collection of models, including cutting-edge architectures like transformers, for tasks such as text classification, sentiment analysis, and question-answering. 

With extensive language support and integration with major deep learning frameworks, the Model Hub simplifies the integration of pre-trained models and libraries into existing workflows, making it a valuable resource for researchers, developers, and data scientists.

Kaggle Models

Kaggle Models enable your data scientists to search and discover hundreds of trained, ready-to-deploy machine learning models in Kaggle and share pre-trained models from competitions. They can use pre-trained models to quickly and easily build machine learning models.

Tensorflow Hub

TensorFlow Hub is a repository of machine learning models that have been trained on specific datasets, or you can even contribute models that have been created for your use case. It enables transfer learning by making various ML models freely available as libraries or web API calls. The entire model can be downloaded to your source code’s runtime with a single line of code.

The problem domains are broken down into: 

• Text: language modelings, texts retrieval, question answering, text generation, and summarization.
• Images: classification, object detection, and style transfer, among several others,
• Video: video classification, generation, audio, and text, 
• Audio: speech-to-text embeddings and speech synthesis, amongst others.

Hyperparameter optimization

The hyperparameter optimization tooling landscape so far hasn’t changed much. The usual suspects are still the top tools in the industry. 

Optuna

Optuna is an open-source hyperparameter optimization framework in Python. It offers a flexible and scalable solution for automating the search for optimal hyperparameter configurations. Optuna supports various optimization algorithms, including tree-structured Parzen estimators (TPE) and grid search, and provides a user-friendly interface for defining search spaces and objective functions.

Hyperopt

Hyperopt is another open-source library for hyperparameter optimization. It employs a combination of random search, tree of Parzen estimators (TPE), and other optimization algorithms. Hyperopt provides a simple interface for defining search spaces and objective functions and is particularly suitable for optimizing complex hyperparameter configurations. 

SigOpt

SigOpt is a commercial hyperparameter optimization platform designed to help data science and machine learning teams optimize their models. It offers a range of optimization algorithms, including Bayesian optimization, to efficiently explore the hyperparameter space. 

The platform integrates well with popular machine learning libraries and frameworks, enabling easy incorporation into existing workflows. One notable feature of SigOpt is its ability to handle “black box” optimization, making it suitable for optimizing models with proprietary or sensitive architectures.

Model quality testing

Model quality testing tools provide features to ensure the reliability, robustness, and accuracy of ML models.

Core features of model quality testing tools

Model quality testing tools should offer capabilities such as:

• Model evaluation techniques: Evaluation methodologies to assess the performance of ML models, including metrics such as accuracy, precision, recall, F1-score, and area under the curve (AUC) to objectively assess model effectiveness.
• Performance metrics: Tools should offer a range of performance metrics to evaluate model quality across different domains and tasks and measure model performance specific to their use cases. Metrics such as AUC, F1-scores for classification problems, mean average precision (mAP) for object detection, and perplexity for language models.
• Error analysis: Model quality testing tools should facilitate error analysis to understand and identify the types of errors made by ML models, helping you gain insights into model weaknesses and prioritize areas for improvement.
• Model versioning and comparison: Model quality testing tools should support model versioning and comparison to compare the performance of different model versions and track the impact of changes on model quality over time.
• Documentation and reporting: The tools should provide features for documenting model quality testing processes, capturing experimental configurations, and generating reports, facilitating transparency, reproducibility, and collaboration.
• Integration with ML workflows: Integration with ML workflows and pipelines to incorporate model quality testing processes into your overall ML development lifecycle, ensuring continuous testing and improvement of model quality.
• Fairness testing: In the context of ethical AI, tools should provide capabilities for fairness testing to evaluate and mitigate biases and disparities in model predictions across different demographic groups or sensitive attributes.

Deepchecks

Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort. This includes checks related to various issues, such as model performance, data integrity, distribution mismatches, and more.

Truera

Truera is a model intelligence platform designed to enable the trust and transparency of machine learning models. It focuses on model quality assurance and helps data science teams identify and mitigate model risks. Truera offers capabilities such as model debugging, explainability, and fairness assessment to gain insights into model behavior and identify potential issues or biases. Learn more from the documentation.

Kolena

Kolena is a platform for rigorous testing and debugging to build team alignment and trust. It also includes an online platform to log the results and insights. Kolena focuses mostly on the ML unit testing and validation process at scale. It provides:

• Data Studio to search for testing scenarios in your project and identify edge cases
• Test Case Manager to manage and control test suites and cases and provide visibility into test coverage.
• Debugger to analyze model errors and identify new testing scenarios.

You interface with it through the web at app.kolena.io and programmatically via the Kolena Python client.

Workflow orchestration and pipelining tools

Workflow orchestration and pipelining tools are essential components for streamlining and automating complex ML workflows. 

Core features of workflow orchestration and pipelining tools

Workflow orchestration and pipelining tools should provide:

• Task scheduling and dependency management: Workflow orchestration and pipelining tools should provide robust scheduling capabilities to define dependencies between tasks and automatically execute them in the correct order, ensuring smooth workflow execution.
• Workflow monitoring and visualization: Workflow orchestration and pipelining tools should offer monitoring and visualization features to track the progress of workflows, monitor resource usage, and visualize workflow dependencies for better insights and troubleshooting.
• Reproducibility and versioning: Workflow orchestration and pipelining tools should support reproducibility by capturing the entire workflow configuration, including code versions, datasets, and dependencies. This will help you track past executions for reproducibility and debugging purposes.
• Integration with ML frameworks: Integration with popular ML frameworks so you can leverage your preferred ML libraries and tools within the workflow orchestration and pipelining system, ensuring compatibility and flexibility in model development.
• Error handling and retry mechanisms: The tools should provide robust error handling and retry mechanisms to handle failures, retry failed tasks, and exceptional cases gracefully, ensuring the reliability and resilience of ML workflows.
• Distributed computing and scalability: Have distributed computing capabilities to handle large-scale ML workflows, so you can leverage distributed computing frameworks or cloud infrastructure to scale your workflows and process massive amounts of data.

ZenML

ZenML is an extensible, open-source MLOps framework for building portable, production-ready MLOps pipelines. It’s built for data scientists and MLOps engineers to collaborate as they develop for production. Learn more about the core concepts of ZenML in their documentation.

Kedro Pipelines

Kedro is a Python library for building modular data science pipelines. Kedro assists you in creating data science workflows composed of reusable components, each with a “single responsibility,” to speed up data pipelining, improve data science prototyping, and promote pipeline reproducibility. Check out the Kedro’s Docs.

Flyte

Flyte is a platform for orchestrating ML pipelines at scale. You can use Flyte for deployment, maintenance, lifecycle management, version control, and training. You can integrate it with platforms like Feast and packages like PyTorch, TensorFlow, and Whylogs to do tasks for the whole model lifecycle.

This article by Samhita Alla, a software engineer and tech evangelist at Union.ai, provides a simplified walkthrough of the applications of Flyte in MLOps. Check out the documentation to get started.

Prefect

Prefect is an open-source workflow management system that simplifies the orchestration of data pipelines and complex workflows. It offers features like task scheduling, dependency management, and error handling, ensuring efficient and reliable execution of data workflows. 

With its Python-based infrastructure and user-friendly dashboard compared to Airflow, Prefect enhances productivity and reproducibility for data engineering and data science teams.

Mage AI

Mage is an open-source tool to build, run, and manage data pipelines for transforming and integrating data. The features include:

• Orchestration to schedule and manage data pipelines with observability.
• Notebook for interactive Python, SQL, and R editors for coding data pipelines.
• Data integrations allow you to sync data from third-party sources to your internal destinations.
• Streaming pipelines to ingest and transform real-time data.
• Integration with dbt to build, run, and manage DBT models.

Model deployment and serving

Model deployment and model serving tools enable you to deploy trained models into production environments and serve predictions to end-users or downstream systems.

Core features of model deployment and serving tools

Model deployment and serving tools should offer capabilities such as:

• Integration with deployment platforms: Compatibility and integration with deployment platforms, such as cloud services or container orchestration frameworks, allow you to deploy and manage ML models on your preferred infrastructure.
• Model versioning and management: Have robust versioning and management capabilities to deploy and serve different versions of ML models, track model performance, and roll back to previous versions if needed.
• API and endpoint management: Include API and endpoint management features to define and manage endpoints, handle authentication and authorization, and provide a convenient interface for accessing the deployed ML models.
• Automated scaling and load balancing: Provide automated scaling and load balancing capabilities to handle varying workloads and distribute incoming requests efficiently across multiple instances of deployed models.
• Model configuration and runtime flexibility: Include flexibility in model configuration and runtime environments, so you can customize model settings, adjust resource allocation, and choose the runtime environment that best suits their deployment requirements.
• Support different deployment patterns: The tool should support batch processing, real-time (streaming) inference, and inference processors (in the form of REST APIs or function calls).

BentoML

BentoML is an open platform for machine learning in production. It simplifies model packaging and model management, optimizes model serving workloads to run at production scale, and accelerates the creation, deployment, and monitoring of prediction services.

Seldon Core

Seldon Core is an open-source platform with a framework that makes deploying your machine learning models and experiments at scale on Kubernetes easier and faster.

It’s a cloud-agnostic, secure, reliable, and robust system maintained through a consistent security and update policy.

Seldon Core summary:

• Easy way to containerize ML models using our pre-packaged inference servers, custom servers, or language wrappers.
• Powerful and rich inference graphs of predictors, transformers, routers, combiners, and more.
• Metadata provenance to ensure each model can be traced back to its respective training system, data, and metrics.
• Advanced and customizable metrics with integration to Prometheus and Grafana.
• Full auditing through model input-output request (logging integration with Elasticsearch).

NVIDIA Triton Inference Server

NVIDIA Triton Inference Server is open-source software that provides a unified management and serving interface for deep learning models. You can deploy and scale machine learning models in production, and it supports a wide variety of deep learning frameworks, including TensorFlow, PyTorch, and ONNX.

Triton Inference Server is a valuable tool for data scientists and machine learning engineers because it can help them:

• Deploy machine learning models in production quickly and easily.
• Scale machine learning models to meet demand.
• Manage multiple machine learning models from a single interface.
• Monitor the performance of machine learning models.

NVIDIA TensorRT

NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications. You can use it to speed up the inference of deep learning models on NVIDIA GPUs.

TensorRT is relevant to data scientists and machine learning engineers because it can help them to:

• Improve the inference performance of their models. TensorRT can optimize deep learning models for inference on NVIDIA GPUs, which can lead to significant performance improvements.
• Reduce the size of their models. TensorRT can also reduce the size of deep learning models, which can make them easier to deploy and use.
• Make their models more efficient. TensorRT can make deep learning models more efficient by optimizing them for specific hardware platforms.

OctoML

OctoML is a machine learning acceleration platform that helps engineers quickly deploy machine learning models on any hardware, cloud provider, or edge device. It is built on top of the open-source Apache TVM compiler framework project.

OctoML provides several features that make it a good choice for engineers who want to deploy machine learning models. These features include:

• A unified model format that makes it easy to deploy models on different hardware and cloud providers.
• A pre-trained model repository so you can find and deploy pre-trained models.
• A model deployment pipeline to ease deploying models to production.
• A model monitoring dashboard to monitor the performance of deployed models.

Model observability

Model observability tools can allow you to gain insights into the behavior, performance, and health of your deployed ML models. 

Core features of model observability tools

Model observability tools should offer capabilities such as:

• Logging and monitoring: Enable logging and monitoring of key metrics, events, and system behavior related to the deployed ML models, facilitating real-time visibility into model performance, resource usage, and predictions.
• Model performance tracking: Track and analyze model performance over time, including metrics such as accuracy, precision, recall, or custom-defined metrics, providing a comprehensive view of model effectiveness.
• Data drift and concept drift detection: Include capabilities to detect and monitor data drift (changes in the input data distribution) and concept drift (changes in the relationship between inputs and outputs), so you can identify and address issues related to changing data patterns.
• Alerting and anomaly detection: Tools should provide alerting mechanisms to notify ML engineers of critical events, performance deviations, or anomalies in model behavior, enabling timely response and troubleshooting.
• Visualization and dashboards: Offer visualization capabilities and customizable dashboards to create informative and interactive visual representations of model behavior, performance trends, or feature importance.
• Model debugging and root cause analysis: Facilitate model debugging and root cause analysis by providing tools to investigate and diagnose issues related to model performance, predictions, or input data.
• Compliance and regulatory requirements: Provide features to address compliance and regulatory requirements, such as data privacy, explainability, or fairness, to ensure that deployed models adhere to ethical and legal standards.
• Integration with ML workflow and deployment pipeline: This enables you to incorporate model observability processes into the development lifecycle, ensuring continuous monitoring and improvement of deployed ML models.

WhyLabs

WhyLabs is an AI observability platform that helps data scientists and machine learning engineers monitor the health of their AI models and the data pipelines that fuel them. It provides various tools for monitoring model performance, detecting drift, and identifying issues with data quality.

WhyLabs is relevant to data scientists and machine learning engineers because it can help them:

• Ensure the quality and accuracy of their models.
• Detect data drift.
• Identify issues with data quality.

Arize AI

Arize AI is a machine learning observability platform that helps data scientists and machine learning engineers monitor and troubleshoot their models in production. It provides various tools for monitoring model performance, detecting drift, and identifying issues with data quality.

Mona

Mona provides data scientists and machine learning engineers with an end-to-end monitoring solution that boosts visibility in their AI systems. It starts with ensuring a single source of information for the systems’ behavior over time. It continues with ongoing tracking of key performance indicators and proactive insights about pockets of misbehavior – enabling teams to take preemptive, efficient corrective measures. 

By providing real-time insights, Mona enables teams to detect issues weeks or months before they come to the surface, allowing them to troubleshoot and resolve the anomalies quickly.

Superwise

Superwise is a model observability platform that helps data scientists and machine learning engineers monitor and troubleshoot their models in production. It provides various tools for monitoring model performance, detecting drift, and identifying issues with data quality.

Superwise is a powerful tool that can help your data scientists and machine learning engineers ensure the quality and accuracy of their AI models.

Evidently AI

Evidently AI is an open-source ML model monitoring system. It helps analyze machine learning models during development, validation, or production monitoring. The tool generates interactive reports from Pandas DataFrame. 

Aporia

Aporia is a platform for machine learning observability. Data science and machine learning teams from various industries use Aporia to monitor model behavior, guarantee peak model performance, and easily scale production ML. It supports all machine learning use cases and model types by allowing you to completely customize your ML observability experience.

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Responsible AI

You can use responsible AI tools to deploy ML models through ethical, fair, and accountable techniques.

Core features of responsible AI tools

Responsible AI tools should provide capabilities such as:

• Fairness assessment: Capabilities to assess and measure the fairness of ML models, identifying potential biases and discriminatory behavior across different demographic groups or sensitive attributes.
• Explainability and interpretability: Features that enable you to explain and interpret the decisions made by ML models.
• Transparency and auditing: Facilitate transparency and auditing of ML models, enabling you to track and document the entire model development and deployment process.
• Robustness and security: Address the robustness and security of ML models, including techniques to defend against adversarial attacks or model tampering, safeguarding ML systems from malicious exploitation or unintended vulnerabilities.
• Regulatory compliance: Help you adhere to regulatory requirements and industry standards, such as data protection regulations (e.g., GDPR), industry-specific guidelines, or fairness regulations.
• Ethics and governance: Provide guidelines and frameworks for you to incorporate ethical considerations and governance practices into your ML systems. 
• Bias mitigation: Include techniques and algorithms to mitigate biases in ML models so you can address and reduce unwanted biases that may be present in your training data or model predictions.

Arthur AI

Arthur AI is a machine learning explainability platform that helps data scientists and machine learning engineers understand how their models work. It provides a variety of tools for explaining model predictions, including:

• Feature importance to show how important each feature is in a model’s prediction.
• Sensitivity analysis to show how a model’s prediction changes when a single feature is changed.
• Counterfactual explanations to show what changes would need to be made to an input in order to change a model’s prediction.

Fiddler AI

Fiddler AI is a model monitoring and explainable AI platform that helps data scientists and machine learning engineers understand how their models work. It provides a variety of tools for explaining model predictions, including:

• Feature importance to show how important each feature is in a model’s prediction.
• Sensitivity analysis to show how a model’s prediction changes when a single feature is changed.
• Counterfactual explanation to show what changes would need to be made to input in order to change a model’s prediction.

Infrastructure: compute, tools, and technologies

The compute and infrastructure component is a vital aspect of machine learning (ML) systems, providing the necessary resources and environment to train, deploy, and run ML models at scale. 

Core features of compute and infrastructure tools

Infrastructure tools should provide capabilities such as:

• Resource management: Offer capabilities for efficient resource management, allowing you to allocate and provision computing resources such as CPUs, GPUs, or TPUs based on the requirements of their ML workloads. This ensures optimal resource utilization and cost efficiency.
• Distributed computing: Support distributed computing frameworks and technologies to leverage parallel processing, distributed training, or data partitioning for model training and inference.
• Monitoring and performance optimization: Provide monitoring and performance optimization features to track the performance of ML workloads, monitor resource usage, detect compute bottlenecks, and optimize the overall performance of ML systems.
• High availability and fault tolerance: Ensure high availability and fault tolerance by providing mechanisms to handle hardware failures, network disruptions, or system crashes. This helps maintain the reliability and uninterrupted operation of ML systems.
• Integration with cloud and on-premises infrastructure: Integrate with cloud platforms, on-premises infrastructure, or hybrid environments to leverage the advantages of different deployment models and infrastructure options based on their specific needs and preferences.
• Security and data privacy: Incorporate security measures and data privacy safeguards, including encryption, access controls, and compliance with data protection regulations. This ensures the confidentiality and integrity of data during ML operations.
• Containerization and virtualization: Facilitate containerization and virtualization technologies, enabling you to package your ML models, dependencies, and runtime environments into portable containers.
• Scalability and elasticity: Provide scalability and elasticity features, enabling you to easily scale up or down your computing resources based on the demand of your ML workloads. 

Ray Open Source

Anyscale is the developer of Ray, a unified compute framework for scalable computing. Ray Open Source is an open-source, unified, and distributed framework for scaling AI and Python applications. You can effortlessly scale any workload or application from a laptop to the cloud without the cost or expertise required to build complex infrastructure.

Nuclio

Nuclio is a high-performance “serverless” framework focused on data, I/O, and compute intensive workloads. It is well integrated with popular data science tools, such as Jupyter and Kubeflow; supports a variety of data and streaming sources, and supports execution over CPUs and GPUs.

Run:ai

Run.ai optimizes and orchestrates GPU compute resources for AI and deep learning workloads. It builds a virtualization layer for AI workloads by abstracting workloads from the underlying infrastructure, creating a shared pool of resources that can be provisioned on the fly, enabling full utilization of expensive GPUs to compute. 

You retain control and gain real-time visibility—including seeing and provisioning run-time, queuing, and GPU utilization—from a single web-based UI. 

MosaicML Platform

The MosaicML platform provides you with the following key benefits when you want to fine-tune LLMs:

• Multiple cloud providers to leverage GPUs from different cloud providers without the overhead of setting up an account and all of the required integrations.
• LLM training configurations. The composer library has a number of well-tuned configurations for training a variety of models and for different types of training objectives.
• Managed infrastructure for orchestration, efficiency optimizations, and fault tolerance (i.e., recovery from node failures).

GPU cloud servers

GPU Cloud vendors have also exploded in popularity. The vendor offerings are divided into two classes:

• GPU Cloud Servers are long-running (but possibly pre-emptible) machines.
• Severless GPUs are machines that scale-to-zero in the absence of traffic.

Paperspace

Paperspace is a high-performance cloud computing platform that provides GPU-accelerated virtual machines for building, training, and deploying models. It offers pre-configured instances with popular frameworks and tools, simplifying the setup process for data scientists. 

With its user-friendly interface and flexible pricing options, Paperspace enables easy access to powerful GPU resources, facilitating faster training and inference of machine learning models in the cloud.

Lambda

Lambda GPU Cloud is a cloud-based platform from Lambda Labs that offers GPU-accelerated virtual machines for machine learning and deep learning tasks. It provides pre-installed frameworks, a user-friendly interface, and flexible pricing options. With Lambda GPU Cloud, you can easily access powerful GPU resources in the cloud, simplifying the development and deployment of machine learning models.

Serverless GPUs

Modal

Modal is a platform that provides a solution for cloud-based encryption. You can write and run code in the cloud and launch custom containers. You can either define a container environment in their code or leverage the pre-built backend.

Baseten

Baseten is a serverless backend for building ML-powered applications with auto-scaling, GPU access, CRON jobs, and serverless functions. It is agnostic to model training workflows and will work with any model trained using any framework.

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Vector databases and data retrieval

Vector databases are a new category of a database management system designed to search across images, video, text, audio, and other forms of unstructured data via their content rather than human-generated labels or tags. There are a few open-source and paid solutions that have exploded in usage by data and software teams over the past few years.

Pinecone

Pinecone is a vector database built on top of the open-source Lucene library that makes it easy to build high-performance vector search applications. It provides a simple API that makes it easy to index and search vectors, and it also supports a variety of advanced features, such as fuzzy search and autocomplete.

Qdrant

Qdrant is a vector similarity search engine and vector database written in Rust. It provides a production-ready service with a convenient API to store, search, and manage embeddings. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.

Weviate

Weaviate is an open-source vector database that stores both objects and vectors. It enables you to combine vector search with structured filtering while leveraging the fault-tolerance and scalability of a cloud-native database, all of which are accessible via GraphQL, REST, and various language clients.

Chroma

Chroma is an open source vector store and embeddings database designed to make it easy to build AI applications with embeddings. It is fully-typed, integrates with programming frameworks like LangChain and LlamaIndex, and provides a single API to develop, test, and run your production AI applications.

Activeloop

Activeloop’s Deep Lake is a vector database that powers foundational model training and integrates with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. It can: 

• Use multi-modal datasets to fine-tune your LLMs,
• Store both the embeddings and the original data with automatic version control, so no embedding re-computation is needed.

Milvus

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible and provides a consistent user experience regardless of the deployment environment.

LLMOps and foundation model training frameworks 

Apart from the “traditional” model training frameworks like PyTorch 2.0, TensorFlow 2, and other model training tools that have remained consistent in the landscape over the past decade, some new tools have emerged in 2024 for training and fine-tuning foundation models.

Guardrails

Guardrails is an open-source Python package that lets your data scientist add structure, type, and quality guarantees to the outputs of large language models (LLMs). Guardrails:

– Does pydantic-style validation of LLM outputs. This includes semantic validation such as checking for bias in generated text, checking for bugs in generated code, etc.

– Takes corrective actions (e.g. asking LLM again) when validation fails,

– Enforces structure and type guarantees (e.g., JSON).

LangChain

LangChain is an open-source framework for building applications that use large language models (LLMs). It provides a number of features that make it easy to use LLMs, including:

• An API for interacting with LLMs.
• Out-of-the-box pre-trained LLMs.
• Tools for fine-tuning LLMs for specific tasks.
• Example applications that use LLMs.

LLamaIndex

LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion:

• Data connectors to your existing data sources and data formats (API’s, PDF’s, docs, SQL, etc.)
• Indices over your unstructured and structured data for use with LLM’s. These indices help to abstract away common boilerplate and pain points for in-context learning:
  • Storing context in an easy-to-access format for prompt insertion.
  • Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when context is too big.
  • Dealing with text splitting.
• An interface for users to query the index (feed in an input prompt) and obtain a knowledge-augmented output.
• A comprehensive toolset, trading off cost and performance.

DUST

Dust is designed to provide a flexible framework to define and deploy large language model apps without having to write any execution code. It is specifically intended to ease:

• Working on multiple examples simultaneously while designing a large language model app.
• Introspecting model outputs produced by intermediary steps of large language model apps.
• Iterating on the design of large language model apps by providing a granular and automated versioning system.

Conclusion

The MLOps and LLMOps landscape featured a diverse array of tools and platforms aimed at enabling organizations and individuals to effectively manage either part or the full end-to-end machine learning lifecycle. The dynamic ecosystem encompassed both open-source and commercial offerings, addressing various stages of the ML workflow. The field was rapidly evolving, giving practitioners plenty of choices to operationalize machine learning effectively.

MLOps tools and platforms FAQ

What devops tools are used in machine learning in 2024?

Some of the popular DevOps tools in the machine learning space include:

• Continuous integration and deployment (CI/CD) tools like Jenkins, GitLab CI/CD, and CircleCI are gaining more adoption to enable automated testing, integration, and deployment of machine learning models.
• Containerization tools such as Docker and Kubernetes used to package machine learning models, dependencies, and infrastructure configurations are still dominating.
• Configuration management tools like Ansible, Puppet, and Chef used to automate the configuration and provisioning of infrastructure, are seeing lesser uptake as more operable and maintainable MLOps platforms emerge. 

What MLOps frameworks work with sensitive data?

There are several MLOps frameworks that prioritize data privacy and can be used with sensitive data. Some of these frameworks include:

TensorFlow Privacy provides tools and techniques for training models on sensitive data in TensorFlow while incorporating privacy safeguards like differential privacy and federated learning.

PySyft enables secure and private machine learning by implementing techniques such as federated learning, homomorphic encryption, and secure multi-party computation (MPC).
Intel OpenVINO (Open Visual Inference and Neural Network Optimization) toolkit provides optimizations for running machine learning models on Intel hardware. It includes features for enhancing privacy and security, such as model encryption, tamper-resistant model execution, and secure inference.