Kenya School of Artificial Intelligence
Lesson 3.4: Introduction to Docker for ML Deployment
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Module 0: Foundations of Applied AI Engineering
Module 1: Programming & Data Foundations
Module 2: Data Science & Machine Learning Foundations
Module 3: API Development & Deployment Fundamentals
Module 4: Advanced Machine Learning Engineering
Module 5: Cloud Infrastructure for AI Systems
Module 6: Monitoring & Reliability in AI Systems
Module 7: Building User-Facing AI Applications
Module 8: Building ML Platforms at Scale
Module 9: Specialization Tracks
Module 10: AI Business, Communication, and Leadership
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Kenya School of Artificial Intelligence
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Module 0: Foundations of Applied AI Engineering
Module 0: Foundations of Applied AI Engineering
Lesson 0.1: The Engineering Workshop
Lesson 0.2: Designing Technical Architecture
Lesson 0.3: Version Control & Packaging Concepts
Lesson 0.4: Command Line & Developer Tooling
Module 1: Programming & Data Foundations
Module 1: Programming & Data Foundations
Lesson 1.1: Python Programming for AI
Lesson 1.2: Data Manipulation with Pandas
Lesson 1.3: Working with APIs and Web Data
Lesson 1.4: Databases and SQL for Developers
Module 2: Data Science & Machine Learning Foundations
Module 2: Data Science & Machine Learning Foundations
Lesson 2.1: Exploratory Data Analysis
Lesson 2.2: Building a First Machine Learning Model
Lesson 2.3: Model Evaluation & Performance Metrics
Lesson 2.4: Model Selection and Validation
Module 3: API Development & Deployment Fundamentals
Module 3: API Development & Deployment Fundamentals
Lesson 3.1: Understanding HTTP and Web Communication
Lesson 3.2: Building APIs with FastAPI
Lesson 3.3: Deploying ML Models as APIs
Lesson 3.4: Introduction to Docker for ML Deployment
Module 4: Advanced Machine Learning Engineering
Module 4: Advanced Machine Learning Engineering
Lesson 4.1: Introduction to Deep Learning
Lesson 4.2: Continuous Integration in ML Projects
Lesson 4.3: Continuous Deployment & Automation
Module 5: Cloud Infrastructure for AI Systems
Module 5: Cloud Infrastructure for AI Systems
Lesson 5.1: Cloud Storage with AWS S3
Lesson 5.2: Compute Infrastructure with EC2
Lesson 5.3: Infrastructure as Code with Terraform
Module 6: Monitoring & Reliability in AI Systems
Module 6: Monitoring & Reliability in AI Systems
Lesson 6.1: Monitoring AI Systems
Lesson 6.2: Detecting and Handling Data/Model Drift
Lesson 6.3: Logging and Observability
Module 7: Building User-Facing AI Applications
Module 7: Building User-Facing AI Applications
Lesson 7.1: Rapid Prototyping with Streamlit
Lesson 7.2: Connecting Streamlit to APIs
Lesson 7.3: Designing User Experiences for AI Apps
Module 8: Building ML Platforms at Scale
Module 8: Building ML Platforms at Scale
Lesson 8.1: Model Pipelines and Continuous Training
Lesson 8.2: Scalable Infrastructure with Kubernetes
Lesson 8.3: Feature Stores with Feast
Module 9: Specialization Tracks
Module 9: Specialization Tracks
Lesson 9.1: Career Paths in AI Engineering
Lesson 9.2: Natural Language Processing Foundations
Lesson 9.3: Computer Vision Foundations
Module 10: AI Business, Communication, and Leadership
Module 10: AI Business, Communication, and Leadership
Lesson 10.1: Pitching AI Solutions to Stakeholders
Lesson 10.2: Understanding the Economics of AI
Lesson 10.3: Building Technical Communities & Audience
Lesson 10.4: Capstone Presentation & Defense
KSAI WhatsApp Community
Lesson 3.4: Introduction to Docker for ML Deployment
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