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1
Intro
2
About Superwise
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Why Continuous Training
4
Why MLOps is Complex
5
Google Envelopes
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Scope
7
Notebook Setup
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Pipeline Overview
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Pipeline Explanation
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Pipeline Use Case
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Train Model
12
Evaluation
13
Next Steps
14
Prerequisites
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Component Functions
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Flask
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Service Account
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Predict Instance
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Docker Build
Description:
Explore a comprehensive guide to building a continuous MLOps stack in this 1 hour 20 minute conference talk from MLOps World: Machine Learning in Production. Dive into MLOps CI/CD pipeline automation using GCP, Superwise, and retraining/auto-resolution notebooks. Learn how to construct a continuous ML pipeline for training, deploying, and monitoring models in the first part of the workshop. Discover automations and production-first insights for continuous issue detection and resolution in the second part. Gain insights from speaker Itay Ben Haim, an ML Engineer at Superwise, as he covers topics such as the importance of continuous training, the complexities of MLOps, Google Envelopes, pipeline overview and explanation, model training and evaluation, prerequisites, component functions, Flask service accounts, predict instances, and Docker builds. Enhance your understanding of MLOps and learn practical techniques for implementing efficient machine learning pipelines in production environments. Read more

A Guide to Building a Continuous MLOps Stack with GCP and Superwise

MLOps World: Machine Learning in Production
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