Duration
3 days
Introduction
This 3 day course is designed to give practical exposure to participants about -
- How to develop Machine Learning models using Tensorflow Framework
- How to deploy the solutions on AI Platform Google Cloud Platform
Initial part of the course provides a high level overview about Machine Learning and it’s Pipelines, key ways to implement Machine Learning projects, Google Cloud AI Platform etc.
Second part of this course is oriented towards providing code walks of Machine Learning Models and deploying the same on Google Cloud Platform.
Pre-requisites
- Python Basics
Course Outline
Applied Machine Learning Overview
- Quick introduction to Machine Learning
- Ways to perform Machine Learning
- Machine Learning Use cases and Demos
- Common Concepts and Terms
- Typical Machine Learning Pipeline
- Challenges in Deployment of Machine Learning Models
Key Environments for Experimentation
- Anaconda
- Colab
- Google AI Platform
- AWS SageMaker
- Azure ML Studio
AI Platform Overview
- Why Google Cloud Platform (GCP) for AI?
- Types of Processing offered by GCP
- Introduction to AI Platform
- Demo
Keras API
- What is Keras?
- Why Keras?
- Keras Basics
- Doing Deep Learning using Keras
- Hands-on Exercises
Feature Engineering
- What is Feature Engineering?
- Why Feature Engineering?
- How to apply Feature Engineering?
- Discussions on various scenarios
- Hands-on Exercises
End to End Tensorflow Model Development, Tuning and Deployment
- Development Process
- Key Tuning Strategies
- Preparing, Versioning, and Testing for Deployment
- Hands-on Exercises
Programme scope, delivery arrangements and suitability are confirmed during enquiry.