AI-assisted Test Automation,MLOps for Developers

• 3 days

Software requirement

• Python
• VS Code

Day 1: The Evolution of QA in the AI Era

• Introduction to Software Development Life Cycle (SDLC)
• Shift-Left vs. Shift-Right
  • Integrating AI into the SDLC
• Manual to Automation
• Identifying "Automation Debt" and how AI reduces maintenance
• AI-Driven Test Generation & Execution
  • Generative AI for Test Cases: Using LLMs to transform User Stories into Playwright/Selenium scripts
  • Self-Healing Tests: Implementing intelligent object recognition to fix brittle UI tests automatically
  • Autonomous Exploration: AI agents for "monkey testing" and edge-case discovery
• Intelligent Defect Detection
  • Pattern recognition in log files to identify root causes
  • Visual Regression: Using Computer Vision to detect pixel-perfect UI anomalies
• Workshop
  • Build a "Self-Healing" test suite that adapts to UI changes without manual script updates

Day 2: MLOps Foundations & Pipeline Engineering

• Shift focus to the "Ops" of AI, learning to build resilient pipelines for model training and deployment
• The MLOps Lifecycle & Architecture
  • Bridging the Gap: Data Scientists (Experimentation) vs. Developers (Production)
  • Core Pillars: Data Versioning, Model Registry, and Feature Stores
• Experiment Tracking & Versioning
  • Tracking hyper-parameters and metrics with MLflow or Weights & Biases
  • Managing Model Lineage: "Which data produced this specific model version?"
• Automating the Training Pipeline
  • Designing Directed Acyclic Graphs (DAGs) for automated training
  • Integrating Continuous Training (CT): Triggering retrains based on schedule or data changes
• Workshop
  • Setup an MLflow server to track experiments and version-control a model artifact

Day 3: Advanced CI/CD Integration & Governance

• CI/CD Pipelines
  • Validating Models in the Pipeline: Precision/Recall gates before deployment
  • Performance Testing for AI: Testing inference latency and resource consumption (CPU/GPU)
• Deployment & Monitoring in Production
  • Canary & Shadow Deployments: Testing new models on live traffic without impacting users
  • Drift Detection: Monitoring for Data Drift and Concept Drift to trigger automated rollbacks
• Reliability, Scalability, and Governance
  • Automated Rollback Strategies: Implementing "circuit breakers" for degrading models
  • Responsible AI: Integrating bias detection and explainability (XAI) into the pipeline
• Workshop
  • Building triggers an automated model evaluation and blocks a deployment if accuracy falls below a threshold