Hands-on Machine Learning with AWS and NVIDIA
- Offered byCoursera
Hands-on Machine Learning with AWS and NVIDIA at Coursera Overview
Duration | 23 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Hands-on Machine Learning with AWS and NVIDIA at Coursera Highlights
- Earn a Certificate upon completion from Amazon web services
Hands-on Machine Learning with AWS and NVIDIA at Coursera Course details
- Machine learning (ML) projects can be complex, tedious, and time consuming. AWS and NVIDIA solve this challenge with fast, effective, and easy-to-use capabilities for your ML project
- This course is designed for ML practitioners, including data scientists and developers, who have a working knowledge of machine learning workflows
- In this course, you will gain hands-on experience on building, training, and deploying scalable machine learning models with Amazon SageMaker and Amazon EC2 instances powered by NVIDIA GPUs
- Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML
- Amazon EC2 instances powered by NVIDIA GPUs along with NVIDIA software offer high performance GPU-optimized instances in the cloud for efficient model training and cost effective model inference hosting
- In this course, you will first get an overview of Amazon SageMaker and NVIDIA GPUs. Then, you will get hands-on, by running a GPU powered Amazon SageMaker notebook instance
- Learn how to prepare a dataset for model training, build a model, execute model training, and deploy and optimize the ML model
- You will also learn, hands-on, how to apply this workflow for computer vision (CV) and natural language processing (NLP) use cases
- After completing this course, you will be able to build, train, deploy, and optimize ML workflows with GPU acceleration in Amazon SageMaker and understand the key Amazon SageMaker services applicable to computer vision and NLP ML tasks
Hands-on Machine Learning with AWS and NVIDIA at Coursera Curriculum
Introduction to Amazon SageMaker and NVIDIA GPUs
Course Introduction
Module Introduction
Introduction to ML on AWS and NVIDIA and Modern day ML
Amazon SageMaker basics
GPUs in the cloud
Introduction to Amazon SageMaker Studio
Amazon SageMaker Studio Features
ML in Amazon SageMaker - Prepare
ML in Amazon SageMaker - Build
ML in Amazon SageMaker - Train and Tune
ML in Amazon SageMaker - Deploy and Manage
Overview of NVIDIA GPUs
Optimizing GPU Performance
NGC catalog: GPU-Optimized Building Blocks for ML
Industry Solutions and the AWS Marketplace
Module Outro
Pre-Course Survey
Note: Discussion forums are not moderated
Supplemental Reading
Supplemental Reading
Supplemental Reading
Supplemental Reading
Supplemental Reading
Additional NVIDIA Deep Learning Institute Courses to Explore
Mid-Course Survey
Graded Quiz
GPU Accelerated Machine Learning Workflows with RAPIDS and Amazon SageMaker
Module Introduction
Overview of ML and RAPIDS
Initial Amazon SageMaker Setup and RAPIDS Exploration
Dataset Acquisition - Multisource, Highly Dimensional Data
Data ETL - Extract, Transform, and Load
Data Visualization - Exploring Visually and with Statistics
Model Design & Training
Model Inference & Deployment
AutoML and HPO
Module Outro
Supplemental Reading
Supplemental Reading
Supplemental Reading
Supplemental Reading
Supplemental Reading
Supplemental Reading
Additional NVIDIA Deep Learning Institute Courses to Explore
Graded Quiz
Computer Vision
Module Introduction
What are Common CV tasks?
Working with Image Data
Dive into Object Detection
Bounding Boxes
The Object Detection Dataset
Introduction to R-CNN and Evolution of Object Detection
R-CNN Family
Overview of an End-to-End CV Pipeline
Fine-tune an End-to-End Model
Deploy and Inference on Amazon SageMaker
Accelerating Deployment with Amazon SageMaker and NVIDIA NGC
Module Outro
Supplemental Reading
Supplemental Reading
Supplemental Reading
Additional NVIDIA Deep Learning Institute Courses to Explore
Graded Quiz
Natural Language Processing
Module Introduction
What is NLP?
What are the common NLP tasks?
Introduction to BERT
BERT Architecture
Using the BERT Model
Downloading and Pretraining a BERT Model
Overview of BERT Fine-tuning Dataset
Accelerated Pretraining and Fine-tuning with Mixed Precision
Fine-tuning BERT Model for Question Answering & Deploying on Amazon SageMaker
Running Triton Inference Server with Amazon SageMaker
Module Wrap-up
Course Wrap Up
Supplemental Reading
Supplemental Reading
Supplemental Reading
Supplemental Reading
Supplemental Reading
Supplemental Reading
Additional NVIDIA Deep Learning Institute Courses to Explore
Post-Course Survey
Graded Quiz
Final Graded Quiz
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