MLOps Without Much Ops - Building Efficient Machine Learning Systems

Overview

Discover modern, no-nonsense data pipelines for efficient machine learning systems. Learn PaaS advantages and explore real-world applications with open-source code. Gain insights on ML's future for organizations of all sizes.

Syllabus

- Introduction to MLOps -- Understanding MLOps and its Importance -- Overview of People, Process, and Technology in MLOps - Modern Data Pipelines -- Components of a Data Pipeline -- Designing Efficient Workflows -- Integrating Data Sources - Machine Learning Systems without Heavy Ops -- Introduction to Platform-as-a-Service (PaaS) -- Benefits and Trade-offs of PaaS Solutions for ML -- Case Studies: PaaS in Action - Building and Deploying Models -- Assessment of Open-Source Tools for ML -- Hands-on Workshop: Model Deployment with PaaS -- Automating Deployment: CI/CD for ML Models - Real-World Applications and Code Exploration -- Source Code Walkthroughs -- Common Pitfalls in MLOps Solutions -- Success Stories from Different Industries - Maintaining and Monitoring ML Systems -- Best Practices for Model Monitoring -- Feedback Loops and Model Retraining -- Handling Drifts and Anomalies - Future of ML in Organizations -- Emerging Trends in MLOps -- Scaling MLOps Practices for Large Enterprises -- Implications for Small to Mid-size Organizations - Course Review and Capstone Project -- Project Guidelines and Objectives -- Developing a Comprehensive MLOps Strategy -- Presentation and Feedback Session

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