AWS and NVIDIA are delighted to reveal the new Hands-on Machine Learning with Amazon SageMaker and NVIDIA GPUs course. The course has four parts, and is created to assist artificial intelligence (ML) lovers quickly learn how to carry out modern ML in the AWS Cloud. Register for the course today on Coursera.
AWS and NVIDIA offer the fastest, most reliable, and easy-to-use ML tools to jump-start your ML job. This course is developed for ML practitioners, consisting of data scientists and designers, who have a working understanding of ML workflows.
This course assists information scientists and developers prepare, develop, train, and deploy premium ML models rapidly by uniting a broad set of capabilities purpose-built for ML within Amazon SageMaker. EC2 instances powered by NVIDIA GPUs offer the highest-performing GPU-based training circumstances in the cloud for efficient model training and affordable model reasoning hosting. In the course, you have hands-on labs and tests developed particularly for this course and hosted by AWS Partner Vocareum.
Youre very first given a top-level introduction of modern-day maker learning. In the laboratories, you will dive right in and get you up and running with a GPU-powered SageMaker instance. You will learn how to prepare your dataset for model training utilizing GPU-accelerated data prep with the RAPIDS library, how to build a GPU sped up tree-based design, how to perform training of this model, and how to enhance the design and deploy for GPU powered inference. You will get hands-on knowing of how to likewise build, train, and release deep learning designs for computer vision (CV) and natural language processing (NLP) use cases. After completing this course, you will have the understanding to build, train, deploy, and optimize ML workflows with GPU acceleration in SageMaker and comprehend the crucial SageMaker services applicable to tabular, computer system vision, and language ML tasks.
In the very first module, you will find out the fundamentals of Amazon SageMaker, GPUs in the cloud, and how to spin up an Amazon SageMaker note pad circumstances. You get a trip Amazon SageMaker Studio, the very first completely incorporated advancement environment (IDE) for device learning, which offers you access to all the abilities of Amazon SageMaker. This is followed by an intro to the NVIDIA GPU Cloud or NGC Catalog, and how it can help you accelerate and simplify ML workflows.
In the 2nd module, you utilize the knowledge from module 1 and discover how to deal with large datasets to develop ML models with the NVIDIA RAPIDS structure. In the hands-on laboratory, you download the Airline Service Quality Performance dataset, and run GPU sped up information prep, model training, and model release.
In the 3rd module, you get a quick history of how computer system vision (CV) has evolved, find out how to work with image information, and find out how to construct end-to-end CV applications using Amazon SageMaker. In the hands-on lab, you download the CUB_200 dataset, and then train and release an object detection model on SageMaker.
In the fourth module, you discover about the application of deep learning for natural language processing (NLP). What is the BERT language design, and why are such language designs utilized in lots of popular services like search, workplace productivity software, and voice representatives? Are NVIDIA GPUs the fastest and the most cost-efficient platform to train and deploy NLP designs?
Hands-on Machine Learning with AWS and NVIDIA is a terrific method to accomplish toolsets required for modern-day ML in the cloud. With this course, you can move jobs from conceptual stages to production phases much faster by leaving the undifferentiated heavy lifting of building facilities to AWS and NVIDIA GPUs, and apply your brand-new found understanding to resolve new difficulties with AI and ML.
Improve your ML abilities in the cloud, and begin using them to your own service challenges by enrolling today at Coursera!
About the Authors
Pavan Kumar Sunder is a Solutions Architect Leader with the Envision Engineering team at Amazon Web Services. He provides technical guidance and assists customers accelerate their ability to innovate through showing the art of the possible on AWS. He has developed numerous models and recyclable solutions around AI/ML, IoT, and robotics for our consumers.
Isaac Privitera is a Senior Data Scientist at the Amazon Machine Learning Solutions Lab, where he establishes custom artificial intelligence and deep learning services to address customers company issues. He works primarily in the computer system vision space, focusing on enabling AWS clients with dispersed training and active learning.
Cameron Peron is Senior Marketing Manager for AWS AI/ML Education and the AWS AI/ML neighborhood. He evangelizes how AI/ML development resolves complicated challenges dealing with neighborhood, business, and startups alike. Out of the office, he enjoys staying active with kettlebell-sport and spending quality time with his friends and family, and is a devoted fan of Euro-league basketball.
The course has four parts, and is designed to help maker learning (ML) enthusiasts quickly discover how to perform modern-day ML in the AWS Cloud. AWS and NVIDIA provide the fastest, most reliable, and user friendly ML tools to jump-start your ML task. This course is created for ML professionals, consisting of data scientists and developers, who have a working knowledge of ML workflows. This course helps data researchers and designers prepare, construct, train, and deploy premium ML designs quickly by bringing together a broad set of capabilities purpose-built for ML within Amazon SageMaker. After completing this course, you will have the knowledge to construct, train, deploy, and enhance ML workflows with GPU acceleration in SageMaker and understand the essential SageMaker services applicable to tabular, computer system vision, and language ML jobs.