MLOps Open Source: Top Platforms and Frameworks Compared

MLOps open source has come a long way, offering a wide range of platforms and frameworks to choose from.

One of the most popular platforms is TensorFlow, developed by Google, which provides a robust set of tools for building and deploying machine learning models. TensorFlow is widely used and has a large community of developers who contribute to its growth.

Another notable platform is PyTorch, developed by Facebook, which is known for its ease of use and flexibility. PyTorch is particularly useful for rapid prototyping and has a strong focus on dynamic computation graphs.

TensorFlow and PyTorch are both widely used and have a large following, but they have different strengths and weaknesses, making it essential to choose the right one for your project.

Let's start by exploring the open-source platforms for MLOps. These platforms are a great place to begin your machine learning journey.

You can start by looking at full-fledged MLOps open source platforms, which contain tools for all stages of the machine learning workflow.

In practice, it depends on the needs of your project and personal preferences. Ideally, once you get a full-fledged tool, you won’t have to set up any other tools.

Some examples of custom tools you might find in a full-fledged platform include a Custom TensorFlow job operator.

You can also use these platforms to monitor model performance.

Workflow frameworks are a crucial part of MLOps, providing a structural approach to streamline the different phases of your MLOps applications. They allow you to manage your workflow with ease and integrate platforms such as S3 and Azure Blob Storage to store your metadata.

Some popular workflow frameworks include Argo Workflow, Kubeflow, and Apache Airflow. Argo Workflow is a Kubernetes-based orchestration tool that provides support to a wide range of ecosystems, including Kedro, Kubeflow Pipelines, and SQLFlow. It also offers features such as a user interface for managing workflows, scheduling jobs using cron, and integrating with various storage platforms.

Here are some key features of popular workflow frameworks:

If you're looking for Kubeflow alternatives, you might want to consider open source solutions that offer more modularity.

Being "all-encompassing" can be a major drawback for enterprise platforms, making it harder to swap out individual components. This is why open source software is often more granular and focused on integrating with other platforms.

Open source solutions are like playing with Legos - if one part is giving you trouble, you can detach it and switch in an alternative. This level of flexibility is hard to find in monolithic enterprise platforms.

MLOps open source platforms are a great place to start your search for Kubeflow alternatives.

Frameworks are an essential part of workflow management in MLOps applications. They provide a structural approach to streamline different phases of your application.

Kubeflow is a popular open-source framework that simplifies working with ML in Kubernetes. It has all the advantages of this orchestration tool, from the ability to deploy on any infrastructure to managing loosely-coupled microservices, and on-demand scaling.

Kedro is another valuable framework that helps machine learning engineers and data scientists create reproducible and maintainable code. It provides a standard template, helps with data loading and storage, and promotes a data-driven approach to ML development.

Apache Spark is an open-source unified analytics engine for big data processing. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

Here are some key features of the frameworks mentioned:

Workflow frameworks are essential tools for streamlining the different phases of your MLOps applications. They provide a structural approach to manage the complexity of machine learning workflows.

Argo Workflow is a Kubernetes-based orchestration tool that's lightweight and easy to use. It's implemented as a Kubernetes CRD and is open-sourced, trusted by a large community.

Some of the key features of Argo Workflow include support for a wide range of ecosystems, such as Kedro, Kubeflow Pipelines, and Seldon. It also provides a user interface for managing workflows, integrates with platforms like S3 and Azure Blob Storage, and allows for scheduling of ML workflows using cron.

Kubeflow is another popular workflow framework that's designed to make the orchestration and deployment of machine learning workflows easier. It provides dedicated services and integration for various phases of machine learning, including training, pipeline creation, and management of Jupyter notebooks.

Here are some of the key features of Kubeflow:

Kedro is an open-source Python framework for creating reproducible, maintainable, and modular data science code. It's designed to standardize the development workflow for data scientists and provides a pipeline abstraction for modular code, data catalog for data versioning and management, and seamless integration with ML frameworks.

MLFlow is an open-source platform that manages the ML lifecycle, including experimentation, reproducibility, and deployment. It's designed to work with any ML library, algorithm, and deployment tool and provides four main components: tracking, projects, models, and registry.

Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. It's widely used for orchestrating complex ML workflows and data pipelines and provides dynamic pipeline generation using Python, robust scheduling and monitoring, integration with a variety of data sources and ML frameworks, and extensibility through a rich ecosystem of plugins.

TensorFlow Extended (TFX) is a range of powerful features for effective machine learning operations, including scalable ML pipelines, integration with TensorFlow, and versatile inference support.

Proprietary platforms are incentivized to constantly sell upgrades and new features to existing customers, often by removing key features or adding limits to cripple your workflow until you pay for the next upgrade.

They might convince you that a low or mid-level plan suits your needs perfectly during sales, but only after buying in do you realize their limitations.

Proprietary platforms can raise their pricing with next-to-no notice, leaving your team to scramble to rewrite everything or pay more than you budgeted.

This can be a huge financial burden, especially for small teams or businesses on a tight budget.

In many cases, proprietary platforms are not upfront about their limitations, making it difficult to make an informed decision about whether to invest in them.

Model serving and deployment are crucial steps in the MLOps workflow. Seldon Core is an open-source platform that helps deploy, scale, and manage thousands of ML models on Kubernetes, providing features like Kubernetes-native model deployment and scalable inference graphs.

Seldon Core supports a variety of ML frameworks, including TensorFlow, PyTorch, and H2O. It also offers advanced monitoring and metrics, making it easier to track model performance and troubleshoot issues.

For those looking for a more comprehensive solution, BentoML is a framework that allows you to build, deploy, and scale any machine learning application. It provides a way to bundle your trained models, along with any preprocessing, post-processing, and custom code, into a containerized format. BentoML supports high-throughput serving, making it suitable for machine learning applications that require efficient and scalable model deployments.

Here are some key features of BentoML:

Additionally, TorchServe is an open-source model serving framework for PyTorch, providing tools for deploying and scaling PyTorch models in production environments. It offers features like scalable model serving, support for multiple model formats, and monitoring and logging.

Deployment Frameworks are the backbone of Model Serving and Deployment, allowing you to deploy and manage your models in production environments. They provide a standardized way to package, deploy, and monitor your models, making it easier to manage complex ML pipelines.

Seldon Core is an open-source MLOps framework that streamlines Machine Learning workflows with logging, advanced metrics, testing, scaling, and conversion of models into production microservices. It offers features like Kubernetes-native model deployment, scalable inference graphs, and advanced monitoring and metrics.

BentoML is an open-source platform for high-performance ML model serving, providing tools to package and deploy ML models as RESTful APIs. It supports framework-agnostic model packaging, automated API generation, scalable deployment with Docker and Kubernetes, and monitoring and logging for deployed models.

TorchServe is an open-source model serving tool, made by Facebook AI, that simplifies the deployment and management of PyTorch models. It offers a robust Model Management API, support for both REST and gRPC protocols, and batched inference for optimizing the prediction process.

MLRun is an open-source framework that simplifies the development, deployment, and management of ML models in production. It integrates seamlessly with popular ML frameworks and provides robust orchestration for end-to-end ML pipelines.

Here are some key features of popular deployment frameworks:

Streamlit is a game-changer for data scientists and engineers who want to showcase their work in an accessible and engaging way. It's an open-source Python library that allows you to create interactive web applications with minimal effort.

Streamlit's user-friendly API makes it easy to create interactive widgets with just a few lines of code. This means you can focus on building and deploying your models, rather than spending hours on web development.

Streamlit supports integration with popular data visualization libraries like Matplotlib, Plotly, and Altair, enabling you to display charts and graphs in your web application. This makes it perfect for creating simple and interactive data visualization tools or prototypes.

You can customize the appearance and layout of your apps using CSS styling and additional layout components. This gives you the flexibility to create a unique and professional-looking interface for your model.

Streamlit's widgets and features allow users to interact with data, adjust parameters, and see real-time updates in the app's visualizations. This makes it an ideal tool for exploratory data analysis and model tuning.

Here are some key features of Streamlit:

Experiment and model management is a crucial aspect of MLOps, and there are several open-source tools that can help you streamline this process. Metaflow, for instance, automatically versions and tracks all your machine learning experiments, so you don't have to worry about losing important data.

MLReef is another platform that provides tools for experiment tracking, as well as a fully-versioned data hosting and processing infrastructure. This means you can easily manage and compare different iterations of your project.

Guild AI is an open-source toolkit that streamlines and enhances the efficiency of machine learning experiments. It lets you run original training scripts, capturing unique experiment results and provides tools for analysis, visualization, and comparison.

Some of the key features of these tools include:

These features can help you optimize your machine learning workflows, reduce waste, and make the reuse of code simpler. By using these tools, you can focus on building and improving your models, rather than worrying about the underlying infrastructure.

Continuous Machine Learning is a library for automating some of the work of machine learning engineers, including training experiments, model evaluation, datasets, and their additions. It's designed to simplify the process of developing and implementing machine learning models into products.

CML was developed by the creators of DVC, an open-source library for versioning machine learning models and machine learning experiments. This collaboration enables seamless integration with external services like cloud platforms: AWS, Azure, GCP, and others.

Some of the key features of CML include automating pipeline building, sending reports, and publishing data. It also provides a wide range of functionality, from distributing cloud resources for a project to hiding complex details of using external services.

Continuous Machine Learning is all about automating the process of developing and implementing machine learning models into products. CML, or Continuous Machine Learning, is a library that was developed by the creators of DVC, an open-source library for versioning machine learning models and experiments.

CML is designed to automate some of the work of machine learning engineers, including training experiments, model evaluation, datasets, and their additions. This can save a lot of time and effort, allowing data scientists to focus on more complex tasks.

One of the key benefits of CML is its ability to automate pipeline building. This means that data scientists can create and manage the entire lifecycle of their machine learning models using Git, without having to worry about the underlying infrastructure.

Here are some of the key features of CML:

CML is flexible and provides a wide range of functionality, from sending reports and publishing data to distributing cloud resources for a project. This makes it a powerful tool for data scientists who want to streamline their workflow and focus on more complex tasks.

Open source tools like TensorFlow and Kubernetes provide transferable skills, making it easier to switch companies or find expert consultants.

Engineers want to learn these tools because they can use this knowledge at other companies too, avoiding being trapped in their current job.

Using valuable tools like TensorFlow and Kubernetes makes hiring easier because they're widely used in the industry.

Your team can benefit from these transferable skills by being able to find community-sourced help if you use the same tools as everyone else.

Training your team on tools like TensorFlow and Kubernetes can make some people nervous, but it's the only sustainable way to attract top engineering talent.

Pipeline management and orchestration are crucial aspects of MLOps. Kubeflow is an open-source platform that simplifies working with ML in Kubernetes, providing a comprehensive suite of tools for model training, serving, and monitoring.

Kubeflow integrates with various frameworks such as Istio and handles TensorFlow training jobs easily. It has over 10.3k stars and 222 contributors on GitHub, giving it the top spot on this list.

Flyte is another open-source MLOps platform used for tracking and maintaining, and automating Kubernetes-native Machine Learning workflows. It ensures that the execution of Machine Learning models is reproducible by tracking changes to the model, versioning it, and containerizing the model alongside its dependencies.

Flyte is written in Python and is designed to support complex ML workflows written in Python, Java, and Scala. It has 1.4k stars and over 38 contributors on GitHub.

Apache Airflow emerges as an open-source platform tailored for the development, scheduling, and vigilant monitoring of batch-centric workflows. Airflow’s expansive Python foundation empowers you to forge intricate workflows, seamlessly bridging connections with a diverse spectrum of technologies.

Apache Airflow is a versatile addition to any machine learning stack, offering dynamic workflow orchestration that adapts to changing data and requirements. With its flexibility, extensive connectivity, and scalability, Airflow allows machine learning practitioners to build custom workflows as code while integrating various technologies.

Here are some of the key features of pipeline management and orchestration tools:

Kedro is an open-source Python framework for creating reproducible, maintainable, and modular data science code. It’s designed to standardize the development workflow for data scientists. Kedro provides a standardized project structure, enables users to create organized, modular, and easily maintainable codebases, and offers a built-in data catalog that manages and abstracts access to various data sources and storage systems.