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💡 ML Concept of the Day: Model Packaging 

Continuing with our series about MLOps, we would like to discuss one of the ML pipeline's most important and often overlooked elements. In the current state of MLOps stacks, containers have become the main mechanism to serve ML models to production. However, before ML models are deployed in containers, they need to be packaged in formats that contain all the metadata and dependencies required to operate the model in a given environment. This is where packaging comes to play →learn more

🔎 ML Research You Should Know: Typed Features at LinkedIn 

The scale of the machine learning problems that an organization like LinkedIn deals with results incomprehensible for data scientists. Building and maintaining a single, effective machine learning model is hard enough so imagine coordinating the execution of thousands of machine learning programs to achieve a cohesive experience. Such a scale requires representing features in a flexible and easy to interpret manner that can be reused across different infrastructures such as Spark, Hadoop, database systems, and others →let’s overview the latest version of LinkedIn’s feature architecture

🤖 ML Technology to Follow: ONNX is a Key Framework for ML Interoperability 

ONNX is an important framework to enable interoperability between different deep learning stacks →what should I know about it?

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