Cloud AI Platforms: AWS, Azure & Google Cloud Integration Training Course

Introduction

In the era of large-scale Artificial Intelligence (AI) and Machine Learning (ML), cloud platforms have become indispensable, providing the scalable infrastructure and specialized services required to build, train, and deploy intelligent applications. This Cloud AI Platforms: AWS, Azure & Google Cloud Integration Training Course is specifically designed for AI engineers, data scientists, developers, and architects who need to master the leading cloud ecosystems: Amazon Web Services (AWS) AI/ML, Microsoft Azure AI, and Google Cloud AI. Participants will gain comprehensive expertise in leveraging the diverse suite of AI services offered by these platforms, enabling them to design and implement robust, enterprise-grade AI solutions.

This immersive program will delve into the practical aspects of integrating and utilizing cloud-native AI services, covering everything from managed machine learning platforms like AWS SageMaker, Azure Machine Learning, and Google Cloud Vertex AI, to specialized cognitive services for vision, speech, and natural language. You will learn to navigate the unique strengths of each platform, focusing on model deployment, MLOps, cost optimization, scalability, and security within a cloud environment. By the end of this course, you will be proficient in selecting, configuring, and integrating the most appropriate cloud AI tools to accelerate your AI development lifecycle and drive innovation.

Target Audience

• AI/ML Engineers and Data Scientists planning to deploy models in the cloud.
• Cloud Architects designing AI-powered solutions.
• DevOps and MLOps Engineers managing AI workflows in cloud environments.
• Software Developers integrating AI services into applications.
• IT Professionals seeking to understand cloud-based AI infrastructure.
• Technical Managers overseeing cloud and AI initiatives.

Duration

10 days

Course Objectives

1. Understand the core concepts of cloud computing and its relevance to AI/ML workloads.
2. Gain proficiency in key AI and Machine Learning services across AWS, Azure, and Google Cloud.
3. Learn to choose the optimal cloud AI platform and services for specific use cases.
4. Master techniques for data management, preparation, and storage in multi-cloud AI environments.
5. Implement strategies for training, deploying, and managing ML models on cloud platforms.
6. Explore specialized cognitive services for computer vision, natural language processing, and speech.
7. Develop skills in securing AI workloads, managing access, and ensuring compliance in the cloud.
8. Optimize cloud AI solutions for performance, scalability, and cost-efficiency.

Course Content

Module 1. Introduction to Cloud Computing for AI/ML

• Fundamentals of cloud computing: IaaS, PaaS, SaaS models
• Benefits of cloud for AI/ML: Scalability, accessibility, cost-efficiency, managed services
• Overview of AWS, Azure, and Google Cloud Platform (GCP) ecosystems
• Key differences and strengths of each major cloud AI platform
• Designing cloud-agnostic and hybrid cloud AI strategies

Module 2. AWS AI & Machine Learning Services

• Introduction to Amazon SageMaker: End-to-end ML platform
• Using AWS Comprehend for Natural Language Processing
• Leveraging AWS Rekognition for Computer Vision
• Speech services: Amazon Polly (Text-to-Speech) and Amazon Transcribe (Speech-to-Text)
• Implementing AI with AWS Lambda and API Gateway for serverless deployments

Module 3. Microsoft Azure AI Services

• Overview of Azure Machine Learning Studio: Building, training, and deploying models
• Azure Cognitive Services: Vision, Speech, Language, Web Search APIs
• Utilizing Azure Bot Service for conversational AI
• Azure Databricks for collaborative data science and ML
• Integrating Azure AI services with Azure Functions and Logic Apps

Module 4. Google Cloud AI Services

• Introduction to Google Cloud Vertex AI: Unified ML platform
• Leveraging Google Cloud Vision AI for image analysis
• Natural Language AI: Entity extraction, sentiment analysis, translation
• Google Cloud Speech-to-Text and Text-to-Speech APIs
• Building AI applications with Google Cloud Functions and Cloud Run

Module 5. Data Management and Storage for Cloud AI

• Scalable data storage: Amazon S3, Azure Blob Storage, Google Cloud Storage
• Data warehousing for analytics: AWS Redshift, Azure Synapse Analytics, Google BigQuery
• Data lakes and analytics services across platforms
• Data ingestion and ETL/ELT pipelines in the cloud
• Best practices for securing and managing large datasets for AI workloads

Module 6. Model Training and Experimentation in the Cloud

• Setting up development environments: Cloud-based notebooks, managed instances
• Managing experiments and tracking model metrics on each platform
• Distributed training strategies for large models
• Hyperparameter tuning services (e.g., SageMaker Automatic Model Tuning, Vertex AI Hyperparameter Tuning)
• Cost optimization for training computationally intensive models

Module 7. Model Deployment and MLOps in the Cloud

• Deploying ML models as real-time endpoints and batch predictions
• Versioning and managing ML models in model registries
• Monitoring model performance, drift, and bias in production
• CI/CD pipelines for ML (MLOps) across cloud platforms
• Strategies for rolling updates and A/B testing of models

8. Cloud AI for Computer Vision

• Pre-trained computer vision APIs: Object detection, image classification, facial recognition
• Custom image model training on cloud platforms
• Video analysis and processing services
• Applications: Quality control, security monitoring, content moderation
• Ethical considerations in deploying computer vision systems

9. Cloud AI for Natural Language Processing (NLP)

• Pre-trained NLP APIs: Sentiment analysis, entity recognition, text summarization
• Building custom text classification and generation models
• Machine translation services and their applications
• Conversational AI: Building chatbots and virtual assistants with cloud services
• LLM integration and fine-tuning on cloud platforms

10. Security and Governance in Cloud AI

• Identity and Access Management (IAM) for AI services
• Data encryption at rest and in transit for AI data
• Compliance with industry regulations (GDPR, HIPAA) in cloud AI deployments
• Auditing and logging for AI workload transparency
• Best practices for securing AI models and data in the cloud

11. Performance and Cost Optimization for Cloud AI

• Right-sizing compute resources for training and inference
• Utilizing serverless AI services for cost efficiency
• Strategies for optimizing storage costs for large datasets
• Performance monitoring and troubleshooting cloud AI solutions
• Reserved instances, spot instances, and pricing models across platforms

12. Multi-Cloud AI Strategy and Integration

• Developing a multi-cloud or hybrid cloud AI strategy
• Data integration patterns across different cloud providers
• Cross-cloud MLOps considerations
• Leveraging managed services for seamless integration
• Future trends and challenges in multi-cloud AI environments

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank