Vertically Integrated AI - Managing Machine Learning Systems for Hardware/IoT

PYTH-214 (2 Days)
Request Pricing for Vertically Integrated AI - Managing Machine Learning Systems for Hardware/IoT

Vertical Integrated AI Training Overview

This Vertically Integrated AI (Artificial Intelligence) - Managing Machine Learning Systems for Hardware/IoT (Internet of Things) training teaches attendees the fundamentals of hardware-based machine learning systems and how to build both AWS SageMaker production machine learning models and IoT Core ML2 (Modular Layer 2) Applications.

Location and Pricing

Accelebrate courses are taught as private, customized training for groups of 3 or more at your site. In addition, we offer live, private online training for teams who may be in multiple locations or wish to save on travel costs. To receive a customized proposal and price quote for private on-site or online training, please contact us.

Vertical Integrated AI Training Objectives

All students will:

  • Understand how to use hardware-embedded AI in real products
  • Learn how to choose managed machine learning systems such as Google AutoML, AWS SageMaker, and Azure ML Studio
  • Build TPUs (Tensor Processing Units) on AWS, production Machine Learning models with AWS SageMaker, and IoT Core ML2 Applications with Swift Playgrounds

Vertical Integrated AI Training Outline

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Introduction
  • Introductory Concepts in Hardware AI and IoT (Internet of Things)
  • The current state of Hardware AI chips and IoT technology
  • What TPUs are and why they are important
  • Using GPUs to accelerate Machine Learning workloads on both AWS (Amazon Web Services) and GCP
  • FPGAs (Field Programmable Gate Arrays) on AWS
  • Using TPUs to classify handwriting samples using Google Cloud Platform (GCP) and TensorFlow
  • Dedicated neural networks in Apple’s A11/A12 chip for the iPhone
  • AWS IoT programming fundamentals
  • AWS Greengrass IoT Framework
  • AWS DeepLens SDK including custom SageMaker model deployment
  • Example project using Python and AWS Lambda on IoT devices
IoT Fundamentals and Hands-on Labs
  • IoT Core ML Fundamentals with Swift Playgrounds
  • Getting, converting, and integrating Core ML Models
  • Classifying Images with Vision and Core ML
  • Analyzing natural language text with Core ML
AWS AI for Hardware
  • Overview
  • Using AWS SageMaker Notebooks to do data science and machine learning
  • Training AWS Sagemaker custom Model and use automatic hyperparameter auto-tuning
  • Training and hosting models by building Scikit Docker container
  • Building production ML models with AWS SageMaker
  • Scaling up AWS SageMaker training and hosting
  • ML Models with AWS SageMaker
  • Hosting endpoints for AWS SageMaker models
  • Chalice/AWS Lambda for Production Deployments
  • Model A/B testing for AWS SageMaker models
  • AutoML solutions with AWS Machine Learning service
  • Training a model to predict responses to a Marketing offer
  • Using the ML Model to generate predictions
  • The AutoML features of AWS Machine Learning services: Feature Transformations with Data Recipes, hyperparameter tuning and more
Google Cloud Platform (GCP) and Azure - AI for Hardware
  • Google (GCP) Managed and AutoML Machine Learning Solutions
  • Machine Learning with Big Query datasets
  • GCP AutoML service
  • GCP Machine Learning Engine with scikit-learn and XGBoost
  • Classifying handwriting with TPUs and the Tensorflow framework
  • Microsoft Azure Machine Learning Studio
  • Drag and drop visual workspaces with Azure Machine Learning Studio
  • Training an ML model using Automobile Price History
  • Predictions using Azure Machine Learning Studio model
Conclusion
Request Pricing for Vertically Integrated AI - Managing Machine Learning Systems for Hardware/IoT

Lecture percentage

50%

Lecture/Demo

Lab percentage

50%

Lab

Course Number:

PYTH-214

Duration:

2 Days

Prerequisites:

All AI training attendees should have prior programming experience in Python and an understanding of basic statistics.

Training Materials:

All students receive comprehensive courseware.

Software Requirements:

  • Google Chrome or Mozilla Firefox
  • GitHub Account
  • AWS Free Account
  • GCP Free Account
  • Azure Free Account
  • Locally installed development software: Python 3.6 or Higher, Jupyter notebook (python), XCode 9.4 or Higher

Contact Us:

Accelebrate’s training classes are available for private groups of 3 or more people at your site or online anywhere worldwide.

Don't settle for a "one size fits all" public class! Have Accelebrate deliver exactly the training you want, privately at your site or online, for less than the cost of a public class.

For pricing and to learn more, please contact us.

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