AI Tools and Platforms provides professionals with practical knowledge of today’s leading AI technologies, enabling them to select the right platforms, automate workflows, enhance productivity, and integrate artificial intelligence into business and technical operations.
Duration 3 days – 21 hrs
Overview
This course provides participants with hands-on experience using popular AI development platforms and tools, including Google AI Platform, AWS AI Services, and Azure AI Studio. Participants will also explore cloud-based coding environments like Google Colab and Jupyter Notebooks to build, test, and deploy basic AI models. The course is ideal for those looking to understand how enterprise-grade AI is developed and deployed on leading platforms.
Objectives
- Navigate major cloud-based AI platforms: Google Cloud AI, AWS AI, and Microsoft Azure AI
- Use Jupyter Notebooks and Google Colab for Python-based AI development
- Understand the core services offered by each cloud provider for machine learning and AI
- Run basic model training, evaluation, and deployment workflows on cloud platforms
- Compare platform features to choose the right tool for different AI project needs
Audience
- Data analysts, developers, and AI enthusiasts looking to use cloud-based AI services
- IT professionals and project managers evaluating AI tools for team adoption
- ML engineers transitioning from local to cloud-based development environments
- Students or professionals working on practical AI projects across different platforms
Prerequisites
- Basic Python programming experience
- Familiarity with machine learning workflows (data loading, training, prediction)
- Prior exposure to any AI or ML tool (e.g., scikit-learn, TensorFlow) is helpful
Course Content
Day 1: AI Platforms & Development Environments
Session 1: Cloud AI Platform Overview
- Introduction to cloud AI: key features and benefits
- Comparing Google AI Platform, AWS AI Services, and Azure AI Studio
- Common services: AutoML, vision APIs, NLP tools, and deployment workflows
Session 2: AI Development Tools – Colab and Jupyter
- Setting up Jupyter Notebooks and Google Colab
- Writing and running Python code in the cloud
- Data integration from cloud storage and APIs
- Hands-on: Build a basic classification model in Colab using scikit-learn
Day 2: Running AI Projects on Cloud Platforms
Session 3: Google Cloud AI Platform
- Using Vertex AI and BigQuery ML for model building
- Deploying and monitoring models
- Hands-on: Use a pretrained model for image or text processing
Session 4: AWS AI Services and Azure AI Studio
- Overview of AWS SageMaker and Rekognition / Comprehend
- Azure ML Studio and AI Builder in Power Platform
- Hands-on: Try AutoML or drag-and-drop modeling
Session 5: Comparison, Deployment, and Next Steps
- Choosing the right platform for the right use case
- Deployment and cost considerations
- Final activity: Design a cloud-based AI workflow using one platform
