Course Details

Google Vertex AI, Microsoft Azure AI, AWS AI Tools Training Course

Introduction

The rapid acceleration of Artificial Intelligence adoption across industries has made cloud-based AI platforms indispensable for organizations seeking to build, deploy, and scale intelligent applications. The three major cloud providers—Google Cloud (with Vertex AI), Microsoft Azure (with its comprehensive Azure AI services), and Amazon Web Services (AWS) (with its extensive suite of AI/ML tools)—offer a vast array of services for every stage of the AI lifecycle, from data preparation and model training to deployment and monitoring. Each platform possesses unique strengths, services, and ecosystems, making it crucial for professionals to understand their respective offerings to select the most appropriate tools for their specific AI initiatives. Without a comprehensive understanding of these leading platforms, organizations risk vendor lock-in, inefficient resource utilization, and an inability to leverage the full spectrum of cutting-edge AI capabilities available in the cloud. Many businesses face the challenge of navigating the sheer volume and complexity of AI services offered by these providers, making it difficult to identify the right tools for their needs, manage costs, and ensure interoperability. Conversely, mastering these platforms empowers data scientists, machine learning engineers, and cloud architects to design scalable, cost-effective, and highly performant AI solutions that drive significant business value. Ignoring the strategic importance of multi-cloud AI expertise means limiting an organization's AI potential and competitive agility. Our intensive 5-day "Google Vertex AI, Microsoft Azure AI, and AWS AI Tools" training course is meticulously designed to equip data scientists, machine learning engineers, cloud architects, developers, and IT professionals with the essential knowledge and practical skills required to effectively utilize, compare, and integrate AI services across these leading cloud platforms.

This comprehensive program will delve into the core AI/ML offerings of Google Vertex AI, Azure AI, and AWS AI, exploring their capabilities for machine learning, deep learning, natural language processing, computer vision, and generative AI. Participants will gain actionable insights and practical tools for data management, model development, deployment strategies, MLOps, cost optimization, and security considerations unique to each platform, empowering them to make informed decisions and build robust, scalable AI solutions in a multi-cloud environment. By the end of this course, you will be proficient in leveraging the strengths of each platform, selecting the optimal tools for diverse AI projects, and effectively managing AI initiatives in the cloud.

Duration

5 Days

Target Audience

The "Google Vertex AI, Microsoft Azure AI, and AWS AI Tools" training course is crucial for a wide range of technical professionals involved in designing, developing, deploying, and managing AI and Machine Learning solutions in cloud environments. This includes:

• Data Scientists: To train, evaluate, and deploy models efficiently on cloud platforms.
• Machine Learning Engineers: To build, manage, and scale ML pipelines and MLOps workflows.
• Cloud Architects: To design and implement AI-ready cloud infrastructure.
• Software Developers: To integrate AI capabilities into their applications using cloud APIs.
• DevOps and MLOps Engineers: To automate AI model deployment, monitoring, and management.
• AI/ML Consultants: To advise clients on optimal cloud AI strategies.
• IT Managers and Decision-Makers: To understand the capabilities and implications of AI services on different cloud platforms.
• Data Engineers: To prepare and manage data for AI workloads in the cloud.
• Technical Project Managers: To oversee cloud-based AI projects.
• Anyone seeking in-depth practical knowledge of leading cloud AI platforms.

Course Objectives

Upon successful completion of the "Google Vertex AI, Microsoft Azure AI, and AWS AI Tools" training course, participants will be able to:

• Understand the architectural differences and core AI/ML service offerings of Google Vertex AI, Microsoft Azure AI, and AWS AI.
• Compare and contrast the strengths and weaknesses of each platform for various AI use cases.
• Perform data preparation, model training, and deployment for different machine learning tasks on each cloud platform.
• Leverage pre-built AI services (e.g., for NLP, Computer Vision, Speech) available on Google, Azure, and AWS.
• Implement MLOps best practices, including model versioning, monitoring, and pipeline automation, across the cloud platforms.
• Identify key considerations for cost optimization, security, and governance when deploying AI in a multi-cloud environment.
• Evaluate and select the most appropriate cloud AI tools for specific business problems and technical requirements.
• Develop a strategic approach for leveraging hybrid and multi-cloud AI solutions.

 Course Modules

Module 1: Cloud AI Ecosystems: An Overview

• Introduction to Cloud AI: Benefits, challenges, and strategic importance.
• Overview of Google Cloud's AI offerings: Focus on Vertex AI.
• Overview of Microsoft Azure's AI offerings: Focus on Azure AI Services and Azure Machine Learning.
• Overview of AWS's AI offerings: Focus on Amazon SageMaker and pre-trained AI services.
• High-level comparison of the three platforms' core philosophy and strengths.

Module 2: Data Management and Preparation for Cloud AI

• Strategies for data ingestion and storage across Google Cloud Storage, Azure Blob Storage, and AWS S3.
• Data labeling and annotation services (e.g., Vertex AI Workbench, Azure Machine Learning data labeling, AWS SageMaker Ground Truth).
• Data transformation and feature engineering using cloud-native tools.
• Data versioning and lineage for AI/ML workflows.
• Best practices for data security and compliance in a multi-cloud AI context.

Module 3: Machine Learning Model Development & Training

• Google Vertex AI: Using Vertex AI Workbench, Custom Training, and AutoML for model development.
• Microsoft Azure AI: Utilizing Azure Machine Learning workspaces, notebooks, and automated ML.
• AWS AI: Developing and training models with Amazon SageMaker Studio and various built-in algorithms.
• Distributed training concepts and leveraging GPUs/TPUs on each platform.
• Managing experiments and tracking model metrics across cloud environments.

Module 4: Pre-built AI Services for Specialized Tasks

• Natural Language Processing (NLP): Exploring services like Google Cloud Natural Language API, Azure AI Language, and Amazon Comprehend/Translate/Lex.
• Computer Vision (CV): Utilizing Google Cloud Vision AI, Azure AI Vision, and Amazon Rekognition/Textract.
• Speech Services: Comparing Google Cloud Speech-to-Text/Text-to-Speech, Azure AI Speech, and Amazon Transcribe/Polly.
• Generative AI Foundation Models: Overview of capabilities and access to models via Vertex AI (Gemini, PaLM), Azure OpenAI Service (GPT, DALL-E), and Amazon Bedrock (Titan, third-party FMs).
• Integrating these pre-built services into applications via APIs and SDKs.

Module 5: Model Deployment and Inference

• Strategies for deploying custom and pre-trained models on Google Vertex AI Endpoints, Azure ML Endpoints, and AWS SageMaker Endpoints.
• Online (real-time) vs. Batch inference patterns.
• Managing model versions and A/B testing for deployed models.
• Scalability and auto-scaling configurations for inference.
• Cost optimization strategies for model serving across platforms.

Module 6: MLOps and Lifecycle Management

• Introduction to MLOps principles: Automation, monitoring, governance.
• Building and orchestrating ML pipelines with Vertex AI Pipelines, Azure ML Pipelines, and AWS SageMaker Pipelines.
• Model monitoring for drift detection, performance degradation, and data quality issues.
• Model governance: Registry, lineage tracking, and responsible AI practices.
• Integrating MLOps tools with CI/CD workflows for continuous integration and deployment of AI models.

Module 7: Advanced Topics & Hybrid/Multi-Cloud AI

• Federated learning concepts and edge AI capabilities on cloud platforms.
• Leveraging specialized hardware (e.g., custom chips like TPUs, inferentia) for AI workloads.
• Hybrid cloud AI deployments: Integrating on-premises data and models with cloud services.
• Strategies for multi-cloud AI architectures and vendor diversification.
• Cost management and optimization strategies across all three major cloud providers.

Module 8: Strategic Selection and Future Trends

• Comparative analysis: When to choose Google Vertex AI, Microsoft Azure AI, or AWS AI for specific projects.
• Decision frameworks based on existing infrastructure, team skill sets, data locality, and compliance needs.
• Emerging trends in cloud AI: Responsible AI tools, AI governance, hyper-personalization, and AI agents.
• Developing an AI cloud strategy roadmap for your organization.
• Hands-on exercises and case studies applying comparative analysis.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

• Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com