Proper release and monitoring of machine learning systems in production.


Full Stack Deep Learning
Full Stack Deep Learning helps you bridge the gap from training machine learning models to deploying AI systems in the real world.
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Why Data Quality is Key to Successful ML Ops
A look at ML Ops and highlight how and why data quality is key to ML Ops workflows.
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ML in Production - Deployment Series
A multi-part blog series on deploying machine learning models in an automated, reproducible, and auditable manner.
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Getting Machine Learning to Production
Machine learning is hard and there are a lot, a lot of moving pieces.
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Curated papers & articles on DS & ML in production
Learn how organizations & business solved machine learning problems, including problem statement, research, methodology, and results.
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Machine Learning Deployment: Shadow Mode
“How do I test my new model in production?” One answer, and a method I often employ when initially deploying models, is shadow mode.
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Data Science Meets Devops: MLOps with Jupyter, Git, & Kubernetes
An end-to-end example of deploying a machine learning product using Jupyter, Papermill, Tekton, GitOps and Kubeflow.
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Rules of Machine Learning: Best Practices for ML Engineering
A basic knowledge of machine learning get the benefit of best practices in machine learning from around Google.
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You Trained a Machine Learning Model, Now What?
Three often overlooked parts of this process occur after the model is actually built: model evaluation, deployment, and monitoring.
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How to Plan and Execute Your ML and DL Projects
This tutorial presents ways to structure ML and DL projects in a systematic manner.
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Battle-Tested Techniques for Scoping Machine Learning Projects
One of the challenges of managing an ML project is project scoping. Even small changes in data or architecture can create huge differences in model ...
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