Use Cases

We provide short articles on common data science scenarios that MLEM can help with or improve. You can combine different scenarios for even more awesomeness.

Our use cases are not written to be run end-to-end like tutorials. For more general, hands-on experience with MLEM, please see Get Started instead.

Why MLEM?

Even with all the success we've seen today in machine learning, data scientists and machine learning engineers still lack a simple way to deploy their models in fast and easily manageable way. This is a critical challenge: while ML algorithms and methods are no longer tribal knowledge, they are still difficult to serve, scale and maintain in production.

Basic uses of MLEM

If you train Machine Learning models and you want to

• save machine learning models along with all meta-information that is required to run them;
• build your models into ready-to-use format like Python packages or Docker Images;
• deploy your models, easily switching between different providers when you need to;
• adopt engineering tools and best practices in data science projects;

MLEM is for you!

We keep reviewing our docs and will include interesting scenarios that surface in the community. Please contact us if you need help or have suggestions!

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