Welcome to the Fuzzy Labs guide on open source MLOps! Here, we will dive into the essentials of machine learning operations (MLOps) and introduce some amazing free tools that can help you succeed in this rapidly evolving field. Let’s explore!

What is MLOps, anyway?

MLOps (machine learning operations) is a discipline that aids in training, deploying, and successfully running machine learning models in production environments. As a new and continuously developing field, MLOps boasts a plethora of tools that are being updated and added all the time.

What counts as open source?

To understand what qualifies as open source, we follow three key criteria:

• Fits the definition: We adhere to the Open Source Initiative’s definition of open source, which outlines clear criteria for classifying software.
• Open source license: Only OSI-approved licenses, such as Apache, GPL, and BSD, qualify for listing. Certain licenses like the Server Side Public License are excluded.
• Batteries included: We only consider tools that are fully functional on their own without pushing for proprietary solutions.

Data Annotation

Data annotation tools are crucial for creating labeled training data for supervised learning. Think of these tools as your personal assistants in a bustling kitchen, chopping and preparing ingredients before the main course can be served—your model training.

Here are some popular data annotation tools:

• Label Studio (Apache 2.0) – A versatile tool for various data types.
• doccano (MIT) – A popular framework for text annotation.
• labelme (GPL-3) – An image annotation tool for segmentation tasks.
• CVAT (MIT) – For video and image annotation.
• Praat (GPL-3) – Focused on speech analysis and phonetics.

Data Validation

The golden rule in machine learning is that a model is only as good as its training data. Data validation ensures the data’s accuracy and consistency. Picture it as a quality control inspector ensuring that only the best ingredients make their way into your culinary masterpiece.

• Great Expectations (Apache 2.0) – A tool with over 280 assertions for maintaining data quality.
• Data Validation Tool (DVT) (Apache 2.0) – A customizable Python tool for query validation.
• data-diff (MIT) – Compares rows across different databases.
• Cerberus (ISC) – A lightweight yet powerful validation library for Python.
• deequ (Apache 2.0) – For unit tests on large datasets.

Troubleshooting Tips

While exploring the world of open-source MLOps, you may encounter some challenges. Here are a few troubleshooting ideas:

• Make sure you have the correct version of all the dependencies if tools fail to install or run.
• Consult the documentation for setup instructions specific to each tool.
• If you’re experiencing issues, check GitHub issues on the tool’s repository for similar problems and fixes.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Data Version Control

Just like code, data evolves over time, necessitating data versioning tools. Think of it as a historian keeping track of recipe modifications—each new version tells a story of its evolution!

• DVC (Apache 2.0) – A popular tool for versioning datasets.
• Delta Lake (Apache 2.0) – Specialized for data warehousing.
• LakeFS (Apache 2.0) – Turns object storage into a Git-like repository.
• Git LFS (MIT) – Not specialized for ML, but useful for large files.

Model Deployment and Serving

Model serving is akin to presenting a crafted dish to guests after a long period of preparation. Each model needs to be accessible so that other software can enjoy its savory predictions.

• Seldon Core (Apache 2.0) – Turns models into microservices on Kubernetes.
• BentoML (Apache 2.0) – A tool for easy model serving.
• Bodywork (AGPL-3.0) – An additional option for model servers.

Model Monitoring

Monitoring ensures that deployed models not only function correctly but also yield reasonable results. Imagine having a sous-chef checking every dish that leaves the kitchen. It’s vital to catch any drift or bias in model predictions.

• Evidently (Apache 2.0) – For model monitoring.
• Boxkite ML (Apache 2.0) – A monitoring tool for your models.
• Alibi Detect (Apache 2.0) – Focuses on model monitoring.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.