Hands-On Machine Learning Using Amazon SageMaker

Hands-On Machine Learning Using Amazon SageMaker

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

Bestseller
15.87 9.52

About This Course

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.

The code bundle for this video course is available at- https://github.com/PacktPublishing/Hands-On-Machine-Learning-Using-Amazon-SageMaker-v-

Other Information

  • Certificate will provided in this course on Completion
  • Full lifetime access
  • Available on Mobile & Laptop

What Students Will Learn In Your Course?

  • 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 SageMaker

Are There Any Course Requirements Or Prerequisites?

This course is designed for Machine Learning practitioners who have a working knowledge of Machine Learning and are keen to build, train, and deploy models on Amazon SageMaker.

Who Are Your Target Students?

 

 

Course Content

  • 22 lectures
  • 02:57:01
  • The Course Overview
    00:02:44
  • AWS Setup
    00:04:03
  • What Problem You Will Solve
    00:01:26
  • Train the Model on SageMaker
    00:16:55
  • Deploy the Model as a REST Service on SageMaker
    00:08:52
  • Introduction
    00:03:09
  • Train ML Model Locally
    00:14:49
  • Enable Model Training on SageMaker
    00:07:25
  • Train the Model on SageMaker
    00:05:48
  • Deploy the Model Locally
    00:02:53
  • Enable Model Deployment on SageMaker
    00:07:44
  • Deploy the Model on SageMaker
    00:06:36
  • Exploring Hyperparameter Optimization Methods
    00:04:53
  • Hyperparameter Optimization in SageMaker
    00:16:01
  • Tune Your Model
    00:08:50
  • Why Online Evaluation
    00:02:52
  • Build an Offline Evaluation Pipeline
    00:04:17
  • Build an Online Evaluation Pipeline via A/B Testing on SageMaker
    00:11:18
  • NLP Problem Definition
    00:09:37
  • Modeling Approach
    00:06:43
  • Train and Evaluate NLP Model on SageMaker
    00:18:50
  • Deploy NLP Model on SageMaker
    00:11:16
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Packt Publication

  • 4.48 (10)
  • 13 Reviews
  • 10 Students
  • 935 Courses