Oreilly – Managing Machine Learning Projects, Video Edition 2023-7

Managing Machine Learning Projects, Video Edition. This course is a comprehensive guide to successfully leading ML projects from design to implementation, without requiring technical expertise in machine learning. This course teaches you how to analyze project requirements, set up infrastructure, manage team resources, and collaborate effectively with stakeholders. It also covers methods for managing the model lifecycle, evaluating algorithms, selecting the best models, and integrating them into production systems. With an emphasis on ethical considerations and data privacy, this course ensures that your projects are protected from common pitfalls such as model biases. Through practical case studies and the use of proven tools, you will learn how to manage the unique challenges of ML projects and deliver efficient, timely, and cost-effective solutions. This course will help you avoid common failures in implementing machine learning projects and create real value for the business.

What you will learn:

• Setting up infrastructure and allocating resources to the team
• Importing data sources into a project
• Accurate estimate of time and effort required
• Assessing which models to choose for delivery
• Integrating models into effective applications

Who is this course suitable for?

• Anyone interested in better managing Machine Learning projects.
• No technical skills required.

Course details: Managing Machine Learning Projects, Video Edition

• Publisher: Oreilly
• Instructor: Simon Thompson
• Training level: Beginner to advanced
• Training duration: 10 hours and 18 minutes

Course headings

• Chapter 1. Introduction: Delivering machine learning projects is hard; let’s do it better
• Chapter 1. Why is ML important?
• Chapter 1. Other machine learning methodologies
• Chapter 1. Understanding this book
• Chapter 1. Case study: The Bike Shop
• Chapter 1. Summary
• Chapter 2. Pre-project: From opportunity to requirements
• Chapter 2. Project management infrastructure
• Chapter 2. Project requirements
• Chapter 2. Data
• Chapter 2. Security and privacy
• Chapter 2. Corporate responsibility, regulation, and ethical considerations
• Chapter 2. Development architecture and process
• Chapter 2. Summary
• Chapter 3. Pre-project: From requirements to proposal
• Chapter 3. Create an estimate
• Chapter 3. Pre-sales/pre-project administration
• Chapter 3. Pre-project/pre-sales checklist
• Chapter 3. The Bike Shop pre-sales
• Chapter 3. Pre-project postscript
• Chapter 3. Summary
• Chapter 4. Getting started
• Chapter 4. Finalize team design and resourcing
• Chapter 4. A way of working
• Chapter 4. Infrastructure plan
• Chapter 4. The data story
• Chapter 4. Privacy, security, and an ethics plan
• Chapter 4. Project roadmap
• Chapter 4. Sprint 0 checklist
• Chapter 4. Bike Shop: project setup
• Chapter 4. Summary
• Chapter 5. Diving into the problem
• Chapter 5. Understanding the data
• Chapter 5. Business problem refinement, UX, and application design
• Chapter 5. Building data pipelines
• Chapter 5. Model repository and model versioning
• Chapter 5. Summary
• Chapter 6. EDA, ethics, and baseline evaluations
• Chapter 6. Ethics checkpoint
• Chapter 6. Baseline models and performance
• Chapter 6. What if there are problems?
• Chapter 6. Pre-modeling checklist
• Chapter 6. The Bike Shop: Pre-modelling
• Chapter 6. Summary
• Chapter 7. Making useful models with ML
• Chapter 7. Feature engineering and data augmentation
• Chapter 7. Model design
• Chapter 7. Making models with ML
• Chapter 7. Stinky, dirty, no good, smelly models
• Chapter 7. Summary
• Chapter 8. Testing and selection
• Chapter 8. Testing processes
• Chapter 8. Model selection
• Chapter 8. Post modeling checklist
• Chapter 8. The Bike Shop: sprint 2
• Chapter 8. Summary
• Chapter 9. Sprint 3: system building and production
• Chapter 9. Types of ML implementations
• Chapter 9. Nonfunctional review
• Chapter 9. Implementing the production system
• Chapter 9. Logging, monitoring, management, feedback, and documentation
• Chapter 9. Pre-release testing
• Chapter 9. Ethics review
• Chapter 9. Promotion to production
• Chapter 9. You aren’t done yet
• Chapter 9. The Bike Shop sprint 3
• Chapter 9. Summary
• Chapter 10. Post project (sprint Ω)
• Chapter 10. Off your hands and into production?
• Chapter 10. Team post-project review
• Chapter 10. Improving practice
• Chapter 10. New technology adoption
• Chapter 10. Case study
• Chapter 10. Goodbye and good luck
• Chapter 10. Summary

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