Deep Learning Using TensorFlow and Apache MXNet on AWS Sagemaker

This course is an in-depth introduction to SageMaker and the support it offers to train and deploy machine learning models in a distributed environment.
Course info
Rating
(10)
Level
Intermediate
Updated
Apr 4, 2018
Duration
2h 22m
Table of contents
Course Overview
Machine Learning on the Cloud with AWS SageMaker
Using Built-in Algorithms in SageMaker
Using Custom Code, Models, and Containers in SageMaker
Implementing Distributed Training and Autoscaling on SageMaker
Description
Course info
Rating
(10)
Level
Intermediate
Updated
Apr 4, 2018
Duration
2h 22m
Description

SageMaker is a fully managed machine learning (ML) platform on AWS which makes prototyping, building, training, and hosting ML models very simple indeed. In this course, Deep Learning Using TensorFlow and Apache MXNet on AWS SageMaker, you'll be shown how to use the built-in algorithms, such as the linear learner and PCA, hosted on SageMaker containers. The only code you need to write is to prepare your data. You'll then see the 3 different ways in which you build your own custom model on SageMaker. You'll bring your own pre-trained model and host it on SageMaker's first party containers. You'll then work on building your model using Apache MXNet and finally bring a custom container to be trained on SageMaker. When you have finished with this course, you will also know how you can connect to other AWS services such as S3 and Redshift to access your training data, run training in a distributed manner, and autoscale your model variants.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on Deep Learning Using TensorFlow and Apache MXNet on AWS SageMaker. A little about myself, I have a master degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on realtime collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. SageMaker is a fully-managed machine-learning platform on AWS, which makes prototyping, building, training, and hosting ML models very simple indeed. SageMaker uses Notebook instances, which hold Jupyter Notebooks to prototype and prepare your data for training your model. There are a number of different ways in which you can build and train models on AWS. SageMaker offers built-in algorithms that the only code you need to write is to prepare your data. The actual code for the model is hosted in AWS containers. If you prefer to build your own custom model, you can do it using the TensorFlow or the Apache MXNet Deep Learning Frameworks. You can also bring your own pre-trained model, and host it on AWS' first-party containers. SageMaker also gives you the option to bring your custom code in your own custom container. It has built-in support for Docker containers, which can host your ML model. The examples in this course also cover how you can connect to other AWS services such as S3 buckets and the Redshift data warehouse. At the end of this course, you should be very comfortable building, training, and hosting your ML models on the SageMaker platform.