EncartaLabs

Machine Learning Systems for Hardware/IoT

( Duration: 3 Days )

The Machine Learning Systems for Hardware/IoT training course teaches delegates 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.

By attending Machine Learning Systems for Hardware/IoT workshop, delegates 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

Experience in Python and an understanding of basic statistics.

COURSE AGENDA

1

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
2

IoT Fundamentals

  • 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
3

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
4

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

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 4,000 Modules on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting http://encartalabs.com/course-catalogue-all.php

Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.

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