Oreilly – MLOps Engineering at Scale, Video Edition 2022-2

MLOps Engineering at Scale course, Video Edition. This course teaches you how to efficiently produce machine learning models using pre-built services on AWS and other cloud providers. You’ll learn how to quickly build flexible and scalable machine learning systems without getting involved in time-consuming operational tasks or incurring high physical hardware costs. Following a real example of taxi fare calculation, you will design an MLOps pipeline for a PyTorch model using AWS serverless capabilities.

What you will learn:

• Data extraction, conversion and loading
• Query data with SQL
• Automatic differential comprehension in PyTorch
• Deploy model training pipelines as a service endpoint
• Pipeline life cycle monitoring and management
• Measuring performance improvement

This course is suitable for people who:

• They are familiar with Python, SQL and the basics of machine learning.
• They seek to increase the speed and efficiency in the implementation of machine learning systems.
• They want to take advantage of cloud computing to develop their models.

Course details

• Publisher:  Oreilly
• Lecturer: Carl Osipov
• Training level: beginner to advanced
• Training duration: 8 hours and 5 minutes

Course headings

• Part 1. Mastering the data set
  Chapter 1. Introduction to serverless machine learning
  Chapter 1. Challenges when designing a machine learning platform
  Chapter 1. Public clouds for machine learning platforms
  Chapter 1. What is serverless machine learning?
  Chapter 1. Why serverless machine learning?
  Chapter 1. Who is this book for?
  Chapter 1. How does this book teach?
  Chapter 1. When is this book not for you?
  Chapter 1. Conclusions
  Chapter 1. Summary
• Chapter 2. Getting started with the data set
  Chapter 2. Starting with object storage for the data set
  Chapter 2. Discovering the schema for the data set
  Chapter 2. Migrating to columnar storage for more efficient analytics
  Chapter 2. Summary
• Chapter 3. Exploring and preparing the data set
  Chapter 3. Getting started with data quality
  Chapter 3. Applying VACUUM to the DC taxi data
  Chapter 3. Implementing VACUUM in a PySpark job
  Chapter 3. Summary
• Chapter 4. More exploratory data analysis and data preparation
  Chapter 4. Summary
• Part 2. PyTorch for serverless machine learning
  Chapter 5. Introducing PyTorch: Tensor basics
  Chapter 5. Getting started with PyTorch tensor creation operations
  Chapter 5. Creating PyTorch tensors of pseudorandom and interval values
  ​​Chapter 5. PyTorch tensor operations and broadcasting
  Chapter 5. PyTorch tensors vs. native Python lists
  Chapter 5. Summary
• Chapter 6. Core PyTorch: Autograd, optimizers, and utilities
  Chapter 6. Linear regression using PyTorch automatic differentiation
  Chapter 6. Transitioning to PyTorch optimizers for gradient descent
  Chapter 6. Getting started with data set batches for gradient descent
  Chapter 6. Data set batches with PyTorch Dataset and DataLoader
  Chapter 6. Dataset and DataLoader classes for gradient descent with batches
  Chapter 6. Summary
• Chapter 7. Serverless machine learning at scale
  Chapter 7. Using IterableDataset and ObjectStorageDataset
  Chapter 7. Gradient descent with out-of-memory data sets
  Chapter 7. Faster PyTorch tensor operations with GPUs
  Chapter 7. Scaling up to use GPU cores
  Chapter 7. Summary
• Chapter 8. Scaling out with distributed training
  Chapter 8. Parameter server approach to gradient accumulation
  Chapter 8. Introducing logical ring-based gradient descent
  Chapter 8. Understanding ring-based distributed gradient descent
  Chapter 8. Phase 1: Reduce-scatter
  Chapter 8. Phase 2: All-gather
  Chapter 8. Summary
• Part 3. Serverless machine learning pipeline
  Chapter 9. Feature selection
  Chapter 9. Feature selection case studies
  Chapter 9. Feature selection using guiding principles
  Chapter 9. Selecting features for the DC taxi data set
  Chapter 9. Summary
• Chapter 10. Adopting PyTorch Lightning
  Chapter 10. Summary
• Chapter 11. Hyperparameter optimization
  Chapter 11. Neural network layers configuration as a hyperparameter
  Chapter 11. Experimenting with the batch normalization hyperparameter
  Chapter 11. Summary
• Chapter 12. Machine learning pipeline
  Chapter 12. Enabling PyTorch-distributed training support with Kaen
  Chapter 12. Unit testing model training in a local Kaen container
  Chapter 12. Hyperparameter optimization with Optuna
  Chapter 12. Summary
• Appendix A. Introduction to machine learning
  Appendix A. Machine learning at first glance
  Appendix A. Machine learning with structured data sets
  Appendix A. Regression with structured data sets
  Appendix A. Classification with structured data sets
  Appendix A. Training a supervised machine learning model

Images of the MLOps Engineering at Scale course, Video Edition

Sample video of the course

download link

Download part 1 – 1 GB

Download part 2 – 206 MB

File(s) password: www.downloadly.ir

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1.2 GB