Introduction

This 5-day intensive professional course is designed to provide participants with a comprehensive understanding of the top cloud platforms used for artificial intelligence (AI) and data science: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Participants will gain hands-on experience and the necessary skills to effectively utilize these platforms to advance their projects and career in AI and data science.

Objectives:

• Understand the foundational concepts of cloud computing and its application in AI and data science.
• Gain proficiency in using AWS, Azure, and GCP for AI and data-oriented solutions.
• Learn to deploy, manage, and optimize AI models and data workflows in the cloud.
• Develop an integrated strategy for utilizing cloud services across different platforms.
• Enhance problem-solving skills through real-world case studies and hands-on projects.

Course Outlines

Day 1: Introduction to Cloud Computing for AI and Data Science

• Overview of Cloud Computing and its impact on AI
• Comparison of AWS, Azure, and GCP for AI and Data Science
• Understanding Cloud Services and Deployment Models
• Data Security and Compliance in the Cloud
• Setting up Accounts and Access Management

Day 2: Amazon Web Services (AWS) for AI and Data Science

• Introduction to AWS AI and Machine Learning Services
• Building AI Models using Amazon Sagemaker
• Data Storage and Processing with AWS S3, RDS, and Redshift
• Serverless Architecture: Lambda and API Gateway
• Real-World Applications: Hands-on Lab

Day 3: Microsoft Azure for AI and Data Science

• Overview of Azure AI and Machine Learning Tools
• Deploying Machine Learning Models with Azure Machine Learning
• Data Management using Azure Storage and Cosmos DB
• Understanding Azure Cognitive Services
• Hands-on Session: Implementing a Data Science Solution

Day 4: Google Cloud Platform (GCP) for AI and Data Science

• Getting Started with GCP and AI Platform
• Data Engineering using BigQuery and Google Cloud Storage
• Model Deployment and Monitoring in GCP AI Platform
• Using TensorFlow and AI Hub for Model Development
• Practical Exercise: Analyzing Datasets on GCP

Day 5: Integrating and Optimizing Cloud Services for AI

• Strategy for Multi-Cloud AI and Data Science Solutions
• Advanced Topics: Machine Learning Ops (MLOps)
• Optimizing Cost and Performance in Cloud Platforms
• Case Studies: Success Stories and Best Practices
• Final Project: Designing a Comprehensive Cloud-Based AI Application