Oreilly – Effective Data Science Infrastructure, Video Edition 2022-8

Effective Data Science Infrastructure, Video Edition. This hands-on course teaches you how to optimize your data mining and machine learning infrastructure so that data scientists can move from idea to product quickly and efficiently. Using techniques and tools used at companies like Netflix, you’ll learn how to create a scalable and reliable infrastructure for your data mining projects.

What you will learn:

• Efficient Infrastructure Design: Learn to design infrastructure that increases data mining productivity.
• Cloud computing management: Managing computing resources and coordinating them in a cloud environment.
• Deploying machine learning models: Deploying machine learning models in a production environment.
• Performance monitoring and management: Monitor the performance of models and manage results.
• Cloud tool integration: Combining different cloud tools to create a unified data mining environment.
• Develop repeatable projects: Develop repeatable data mining projects using tools like Metaflow, Conda, and Docker.
• Designing complex applications: Designing complex applications for diverse teams and large data sets.
• Infrastructure customization and development: Customization and development of data mining infrastructure to suit the needs of the organization.

Who is this course suitable for?

• This course is suitable for infrastructure engineers and data scientists who are proficient in the Python programming language.

Course details: Effective Data Science Infrastructure, Video Edition

• Publisher: Oreilly
• Instructor: Ville Tuulos
• Training level: Beginner to advanced
• Training duration: 11 hours and 28 minutes

Course headings

• Chapter 1. Introducing data science infrastructure
• Chapter 1. What is data science infrastructure?
• Chapter 1. Why good infrastructure matters
• Chapter 1. Human-centric infrastructure
• Chapter 1. Summary
• Chapter 2. The toolchain of data science
• Chapter 2. Introducing workflows
• Chapter 2. Summary
• Chapter 3. Introducing Metaflow
• Chapter 3. Branching and merging
• Chapter 3. Metaflow in Action
• Chapter 3. Summary
• Chapter 4. Scaling with the compute layer
• Chapter 4. The compute layer
• Chapter 4. The compute layer in Metaflow
• Chapter 4. Handling failures
• Chapter 4. Summary
• Chapter 5. Practicing scalability and performance
• Chapter 5. Practicing horizontal scalability
• Chapter 5. Practicing performance optimization
• Chapter 5. Summary
• Chapter 6. Going to production
• Chapter 6. Stable execution environments
• Chapter 6. Stable operations
• Chapter 6. Summary
• Chapter 7. Data processing
• Chapter 7. Interfacing with data infrastructure
• Chapter 7. From data to features
• Chapter 7. Summary
• Chapter 8. Using and operating models
• Chapter 8. Summary
• Chapter 9. Machine learning with the full stack
• Chapter 9. Deep regression model
• Chapter 9. Summarizing lessons learned
• Chapter 9. Summary

Course images

Sample course video

Download link

Download Part 1 – 1 GB

Download Part 2 – 538 MB

File(s) password: www.downloadly.ir

File size

1.5 GB