Hands-On Machine Learning Using Amazon SageMaker

Hands-On Machine Learning Using Amazon SageMaker
Hands-On Machine Learning Using Amazon SageMaker

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 57m | 626 MB
eLearning | Skill level: All Levels

Convert your Machine Learning project ideas into highly scalable solutions instantly with Amazon SageMaker

The biggest challenge facing a Machine Learning professional is to train, tune, and deploy Machine Learning on the cloud. AWS SageMaker offers a powerful infrastructure to experiment with Machine Learning models. You probably have an existing ML project that uses TensorFlow, Keras, CNTK, scikit-learn, or some other library.

This practical course will teach you to run your new or existing ML project on SageMaker. You will train, tune, and deploy your models in an easy and scalable manner by abstracting many low-level engineering tasks. You will see how to run experiments on SageMaker Jupyter notebooks and code training and prediction workflows by working on real-world ML problems.
By the end of this course, you’ll be proficient on using SageMaker for your Machine Learning applications, thus spending more time on modeling than engineering.

Using realistic examples, this hands-on course will show you how to run your existing or new Machine Learning pipelines on SageMaker. More specifically, the step-by-step instructions will help you to train, deploy, and evaluate your Machine Learning/Deep Learning models on SageMaker.

What You Will Learn

  • Build reliable, testable, and reproducible Machine Learning/Deep Learning workflows on SageMaker
  • Migrate existing ML projects to SageMaker to minimize the time taken turning an idea into an actual model in production
  • Data exploration and ML modeling on Jupyter Notebooks hosted on SageMaker
  • Train and deploy your custom Machine Learning/Deep Learning model on the cloud, via SageMaker
  • Conduct hyperparameter optimization on SageMaker in an easy and consistent way
  • Evaluate your models online by running A/B tests on SageMake
+ Table of Contents

Your First Machine Learning Model on SageMaker
1 The Course Overview
2 AWS Setup
3 What Problem You Will Solve
4 Train the Model on SageMaker
5 Deploy the Model as a REST Service on SageMaker

Train Your Existing Machine Learning Model on SageMaker
6 Introduction
7 Train ML Model Locally
8 Enable Model Training on SageMaker
9 Train the Model on SageMaker

Deploy Your Existing Machine Learning Model on SageMaker
10 Deploy the Model Locally
11 Enable Model Deployment on SageMaker
12 Deploy the Model on SageMaker

Hyperparameter Optimization
13 Exploring Hyperparameter Optimization Methods
14 Hyperparameter Optimization in SageMaker
15 Tune Your Model

Offline and Online Model Evaluation
16 Why Online Evaluation
17 Build an Offline Evaluation Pipeline
18 Build an Online Evaluation Pipeline via A B Testing on SageMaker

Natural Language Processing Application
19 NLP Problem Definition
20 Modeling Approach
21 Train and Evaluate NLP Model on SageMaker
22 Deploy NLP Model on SageMaker