Best of MLOps — June 2024

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·2y

   4 Ways to Test ML Models in Production

   Testing ML models in production is crucial to ensure reliability and performance on real-world data. Four common strategies are A/B testing, canary testing, interleaved testing, and shadow testing. A/B testing distributes requests non-uniformly between models, while canary testing gradually rolls out the candidate model to a subset of users. Interleaved testing mixes predictions from both models, and shadow testing logs outputs without affecting user experience. These techniques help mitigate risks and validate the model effectively.

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2. 2
   Article

   Community Picks·2y

   10 Open Source Tools for Building MLOps Pipelines

   This post explores 10 open source MLOps tools for building an MLOps pipeline, including KitOps, Hydra, Data Version Control (DVC), Airflow, Continuous Machine Learning (CML), Hyperopt, Weights and Biases, MLflow, NannyML, and Metaflow.

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3. 3
   Article

   Medium·2y

   Architect scalable LLM & RAG inference pipelines

   This post discusses the architecting of scalable and cost-effective LLM and RAG inference pipelines. It explains the difference between monolithic and microservice architectures, and showcases the implementation of the RAG business module and the LLM microservice. The post also provides details on deploying and running the inference pipeline on the Qwak AI platform.

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