Machine Learning Engineering in Action, Video Edition - Ben Wilson contains tips and recommendations from Ben Wilson that can help overcome the main challenges of machine learning projects, from their experimentation to their presence in the production environment. From years of industry experience working as the Principal Resident Solutions Architect at Databricks, Wilson has a checklist that offers field-proven best practices, techniques, and architectural patterns.
This video edition focuses on critical areas like how to scope or define projects right, making the right technology choices, and software engineering principles to make the codebase more readable, modifiable, and easier to test. Even in an Agile development environment, Wilson stresses the Agile methodologies as far as the rapid prototyping and the stakeholders’ involvement are concerned, and he also underlines the role of stringent planning of the developmental processes for the achievement of successful development campaigns. Leveraging what is found in peer-to-peer architecture and backing it up with production-grade source code samples, Machine Learning Engineering in Action, Video Edition - Ben Wilson offers specific recommendations on how to design for robustness and how to minimize conflicts across the teams.
From the data science problem assessment to the automation of diagnostics and logging processes, Machine Learning Engineering in Action, Video Edition - Ben Wilson provides the necessary instruments and approaches for coordinating the creation and application of machine learning applications.
Machine Learning Engineering in Action, Video Edition - Ben Wilson Table of Contents:
- Part 1. An introduction to machine learning engineering
- Chapter 1. What is a machine learning engineer?
- Chapter 2. Your data science could use some engineering
- Chapter 3. Before you model: Planning and scoping a project
- Chapter 4. Before you model: Communication and logistics of projects
- Chapter 5. Experimentation in action: Planning and researching an ML project
- Chapter 6. Experimentation in action: Testing and evaluating a project
- Chapter 7. Experimentation in action: Moving from prototype to MVP
- Chapter 8. Experimentation in action: Finalizing an MVP with MLflow and runtime optimization
- Part 2. Preparing for production: Creating maintainable ML
- Chapter 9. Modularity for ML: Writing testable and legible code
- Chapter 10. Standards of coding and creating maintainable ML code
- Chapter 11. Model measurement and why it’s so important
- Chapter 12. Holding on to your gains by watching for drift
- Chapter 13. ML development hubris
- Part 3. Developing production machine learning code
- Chapter 14. Writing production code
- Chapter 15. Quality and acceptance testing
- Chapter 16. Production infrastructure
- Appendix A. Big O(no) and how to think about runtime performance
- Appendix B. Setting up a development environment
Who is this course for?
- Basically, it is looking for data scientists who are familiar with machine learning algorithms and object-oriented programming languages.
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