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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Subjects

Data Science