Скачать или смотреть Software Quality and Best Practices in Machine Learning Systems

This video dives deep into the crucial topic of Software Quality and Best Practices in Machine Learning (ML) Systems. Moving an ML model from a research environment (like a Jupyter notebook) into a robust, reliable production system requires adopting rigorous software engineering standards.
We'll cover the essential MLOps practices necessary to ensure the quality, maintainability, and scalability of your machine learning applications.
What you'll learn:
• Testing Strategies unique to ML, including data validation, model validation, and integration testing.
• Implementing Continuous Integration/Continuous Delivery (CI/CD) pipelines for automated ML workflows.
• Best practices for Code Structure and Documentation in ML projects.
• Monitoring and Observability techniques for data drift, model decay, and performance issues in production.
• The role of Version Control for code, data, and models (Model Registry).
• Security and Compliance considerations for production ML.
Whether you're a Data Scientist, ML Engineer, or Software Developer, these principles are critical to building trustworthy and high-performing ML systems. Stop deploying brittle models and start building enterprise-grade AI!