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

PYTH-214 (2 Days)

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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 iOS Core ML2 (Modular Layer 2) Applications.

Location and Pricing

Most Accelebrate courses are delivered as private, customized, on-site training at our clients' locations worldwide for groups of 3 or more attendees and are custom tailored to their specific needs. Please visit our client list to see organizations for whom we have delivered private in-house training. These courses can also be delivered as live, private online classes for groups that are geographically dispersed or wish to save on the instructor's or students' travel expenses. To receive a customized proposal and price quote for private training at your site or online, 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 iOS 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
iOS Fundamentals and Hands-on Labs
 iOS 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
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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.

Contact Us

Toll-free in US/Canada:
877 849 1850
International:
+1 678 648 3113

Toll-free in US/Canada:
866 566 1228
International:
+1 404 420 2491

925B Peachtree Street, NE
PMB 378
Atlanta, GA 30309-3918
USA

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