5 Best End-to-End Open Source MLOps Tools

Image by Author

Due to the popularity of 7 End-to-End MLOps Platforms You Must Try in 2024 blog, I am writing another list of end-to-end MLOPs tools that are open source. 

The open-source tools provide privacy and more control over your data and model. On the other hand, you have to manage these tools on your own, deploy them, and then hire more people to maintain them. Also, you will be responsible for security and any service outage. 

In short, both paid MLOps platforms and open-source tools have advantages and disadvantages; you just have to pick what works for you.

In this blog, we will learn about 5 end-to-end open-source MLOps tools for training, tracking, deploying, and monitoring models in production. 

1. Kubeflow

The kubeflow/kubeflow makes all machine learning operations simple, portable, and scalable on Kubernetes. It is a cloud-native framework that allows you to create machine learning pipelines, and train and deploy the model in production. 

Image from Kubeflow

Kubeflow is compatible with cloud services (AWS, GCP, Azure) and self-hosted services. It allows machine learning engineers to integrate all kinds of AI frameworks for training, finetuning, scheduling, and deploying the models. Moreover, it provided a centralized dashboard for monitoring and managing the pipelines, editing the code using Jupyter Notebook, experiment tracking, model registry, and artifact storage. 

2. MLflow

The mlflow/mlflow is generally used for experiment tracking and logging. However, with time, it has become an end-to-end MLOps tool for all kinds of machine learning models, including LLMs (Large Language Models).

Image from MLflow

The MLFlow has 6 core components:

1. Tracking: version and store parameters, code, metrics, and output files. It also comes with interactive metric and parametric visualizations. 
2. Projects: packaging data science source code for reusability and reproducibility.
3. Models: store machine learning models and metadata in a standard format that can be used later by the downstream tools. It also provides model serving and deployment options. 
4. Model Registry: a centralized model store for managing the life cycle of MLflow Models. It provides versioning, model lineage, model aliasing, model tagging, and annotations.
5. Recipes (Pipelines): machine learning pipelines that let you quickly train high-quality models and deploy them to production.
6. LLMs: provide support for LLMs evaluation, prompt engineering, tracking, and deployment. 

You can manage the entire machine learning ecosystem using CLI, Python, R, Java, and REST API.

3. Metaflow

The Netflix/metaflow allows data scientists and machine learning engineers to build and manage machine learning / AI projects quickly. 

Metaflow was initially developed at Netflix to increase the productivity of data scientists. It has now been made open source, so everyone can benefit from it. 

Image from Metaflow Docs

Metaflow provides a unified API for data management, versioning, orchestration, mode training and deployment, and computing. It is compatible with major Cloud providers and machine learning frameworks. 

4. Seldon Core V2

The SeldonIO/seldon-core is another popular end-to-end MLOps tool that lets you package, train, deploy, and monitor thousands of machine learning models in production. 

Image from seldon-core

Key features of Seldon Core:

1. Deploy models locally with Docker or to a Kubernetes cluster.
2. Tracking model and system metrics. 
3. Deploy drift and outlier detectors alongside models.
4. Supports most machine learning frameworks such as TensorFlow, PyTorch, Scikit-Learn, ONNX.
5. Data-centric MLOPs approach.
6. CLI is used to manage workflows, inferencing, and debugging.
7. Save costs by deploying multiple models transparently.

Seldon core converts your machine learning models into REST/GRPC microservices. I can easily scale and manage thousands of machine learning models and provide additional capabilities for metrics tracking, request logging, explainers, outlier detectors, A/B Tests, canaries, and more.

5. MLRun

The mlrun/mlrun framework allows for easy building and management of machine learning applications in production. It streamlines the production data ingestion, machine learning pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources.

Image from MLRun

The core components of MLRun:

1. Project Management: a centralized hub that manages various project assets such as data, functions, jobs, workflows, secrets, and more.
2. Data and Artifacts: connect various data sources, manage metadata, catalog, and version the artifacts.
3. Feature Store: store, prepare, catalog, and serve model features for training and deployment.
4. Batch Runs and Workflows: runs one or more functions and collects, tracks, and compares all their results and artifacts.
5. Real-Time Serving Pipeline: fast deployment of scalable data and machine learning pipelines.
6. Real-time monitoring: monitors data, models, resources, and production components.

Conclusion

Instead of using one tool for each step in the MLOps pipeline, you can use only one to do them all. With just one end-to-end MLOPs tool, you can train, track, store, version, deploy, and monitor machine learning models. All you have to do is deploy them locally using Docker or on the Cloud. 

Using open-source tools is suitable for having more control and privacy, but it comes with the challenges of managing them, updating them, and dealing with security issues and downtime. If you are starting as an MLOps engineer, I suggest you focus on open-source tools and then move to managed services like Databricks, AWS, Iguazio, etc. 

I hope you like my content on MLOps. If you want to read more of them, please mention it in a comment or reach out to me on LinkedIn.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.