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Deep Learning AI Model Deployment

This module equips learners with end-to-end expertise in deploying deep learning models at scale—from model optimization (ONNX, quantization, distillation) to secure, production-grade APIs integrated with CI/CD, monitoring, and stress testing. It covers real-world deployment across edge and cloud platforms, ensuring learners can deliver high-performance, scalable, and collaborative AI services.

Day 1-15: Introduction & Environment Setup​

Topics Covered​

  • Deployment Challenges:
    • Managing large model sizes (200+ MB), latency constraints for real‐time inference, and hardware-specific optimizations (GPU vs. CPU tradeoffs).
    • Handling framework incompatibilities (e.g., PyTorch vs. TensorFlow) and ensuring reproducibility.
  • Best Practices:
    • Containerization and environment reproducibility using Docker.
    • Secure code versioning with Git and automated testing via CI/CD.

Hands‐on Tasks​

  • Set up a Python (3.8+) development environment using conda/virtualenv.
  • Install and configure Docker (using an NVIDIA‐CUDA base image if targeting GPU deployment).
  • Create a basic Git repository with detailed README and commit a sample “Hello, Inference!” application.
  • Configure an initial CI/CD pipeline using GitHub Actions that runs unit tests and builds a Docker image.

Deliverables​

  • A summary report documenting specific deployment challenges (e.g., model drift, latency, conversion pitfalls) and best practices.
  • A public GitHub repository containing:
    • A well-commented Docker file (with CUDA support when applicable).
    • CI/CD configuration files (e.g., GitHub Actions YAML) that trigger on code changes.

Day 16-30: Model Export & Optimization Formats​

Topics Covered​

  • Export Formats:
    • Detailed study of ONNX, TorchScript (dynamic vs. static graphs), and TensorFlow SavedModel formats.
    • Specific pitfalls: unsupported operators during conversion, dynamic shape handling, and precision differences.
  • Optimization Strategies:
    • Techniques for post-training quantization (e.g., 8‐bit quantization), model pruning, and knowledge distillation.
    • Evaluating performance trade‐offs (accuracy vs. speed/memory usage).

Hands‐on Tasks​

  • Convert a sample PyTorch model (e.g., ResNet50) to ONNX and troubleshoot common conversion errors.
  • Experiment with post‐training quantization using PyTorch Quantization Toolkit.
  • Compare inference performance (latency, throughput) before and after conversion.

Deliverables​

  • A detailed research document (with code snippets and screenshots) describing the conversion process, including common errors and their solutions.
  • A public blog post and a GitHub repository demonstrating:
    • A complete PyTorch → ONNX conversion pipeline.
    • Sample code for quantization and performance benchmarks.

Day 31–45: API Integration & Inference Engines​

Topics Covered​

  • API Development:
    • Building RESTful APIs using FastAPI versus Flask; best practices in routing, error handling, and documentation.
  • Inference Acceleration:
    • Integrating NVIDIA TensorRT for GPU acceleration and Intel OpenVINO for CPU optimization.
    • Overcoming challenges such as multi-threading for concurrent requests and managing GPU memory.

Hands‐on Tasks​

  • Develop a FastAPI endpoint that accepts inputs (e.g., images or text) and returns model predictions.
  • Integrate TensorRT to optimize the inference engine and measure speed improvements.
  • Set up a local test to compare baseline inference performance versus accelerated inference.

Deliverables​

  • A demo API application (with source code hosted on GitHub) that:
    • Exposes endpoints for inference.
    • Includes benchmarking scripts comparing TensorRT/ONNX runtime performance.
  • Comprehensive documentation and a public blog tutorial outlining the setup, code, and performance results.

Day 46–60: Advanced Model Optimization​

Topics Covered​

  • Optimization Techniques:
    • Implementing quantization (dynamic and static) and model pruning (structured/unstructured).
    • Understanding and applying knowledge distillation to transfer performance from a large “teacher” model to a smaller “student” model.
  • Challenges:
    • Balancing reduced precision with accuracy loss.
    • Identifying optimal pruning thresholds.

Hands‐on Tasks​

  • Apply quantization to a benchmark model (e.g., BERT or ResNet50) and compare latency and accuracy.
  • Experiment with pruning strategies using available PyTorch libraries.
  • Implement a simple knowledge distillation experiment and compare performance metrics.

Deliverables​

  • Code demonstrations (with before/after metrics) showing:
    • Model performance (inference time, memory usage) before and after optimization.
  • A benchmarking report (with charts/graphs) detailing performance gains, resource usage improvements, and potential trade‐offs.
  • Updated GitHub repository with the optimization experiments and detailed README.

Day 61–75: Multi‐Model & Ensemble Deployment Strategies​

Topics Covered​

  • Ensemble Techniques:
    • Methods such as bagging, boosting, and stacking for combining multiple model predictions.
  • Deployment Architecture:
    • Designing a scalable system to host multiple models with A/B testing and fallback mechanisms.
  • Challenges:
    • Load balancing requests between models while keeping latency minimal.
    • Implementing real‐time ensemble aggregation without significant overhead.

Hands‐on Tasks​

  • Design an architecture using Draw.io that shows multiple model endpoints and an ensemble aggregator.
  • Develop a sample code repository where an API dispatches requests to several models and aggregates the responses.

Deliverables​

  • A detailed whitepaper on multi‐model deployment strategies including ensemble methods.
  • An architecture diagram (Draw.io file) and a sample code repository that demonstrates ensemble API endpoints.
  • A blog post summarizing the design choices, challenges, and implementation details.

Day 76–90: CI/CD Pipeline for Continuous Deployment​

Topics Covered​

  • Automated Deployment:
    • Building pipelines for continuous integration (CI) and continuous deployment (CD) that automatically run tests, rebuild Docker images, and deploy updates.
  • Versioning & Rollbacks:
    • Strategies for version control, model versioning, and automated rollback in case of failed deployments.

Hands‐on Tasks​

  • Configure GitHub Actions (or Jenkins) to trigger on every commit:
    • Run unit/integration tests
    • Build Docker images.
    • Deploy the updated image to a Kubernetes cluster (or similar orchestration platform).

Deliverables​

  • A complete CI/CD pipeline (with workflow YAML or Jenkinsfile) integrated into a public GitHub repository.
  • Detailed documentation outlining the deployment workflow, version control, and rollback strategies.
  • Test logs and integration results captured in automated reports.

Day 91–105: Monitoring & Logging​

Topics Covered​

  • Real‐Time Monitoring:
    • Instrumenting code with Prometheus client libraries to expose metrics (inference latency, error rates, GPU utilization).
  • Visualization & Alerts:
    • Building Grafana dashboards to visualize metrics and setting up alerts for performance anomalies.

Hands‐on Tasks​

  • Integrate Prometheus monitoring in the deployed API.
  • Configure Grafana dashboards to monitor key metrics.
  • Write alerting rules to notify when performance thresholds are breached.

Deliverables​

  • Code integration that exposes Prometheus metrics endpoints.
  • A detailed report with screenshots of Grafana dashboards and configuration files.
  • Step‐by‐step public documentation for setting up monitoring and logging.

Day 106-120: Edge & Cloud Deployment Scenarios​

Topics Covered​

  • Deployment Platforms:
    • Hands‐on comparisons between deploying models on cloud platforms (AWS SageMaker, Google AI Platform, Azure ML) versus edge devices (e.g., NVIDIA Jetson Nano).
  • Platform‐Specific Challenges:
    • Latency, cost, scalability, and hardware limitations.

Hands‐on Tasks​

  • Deploy a sample model on AWS SageMaker and record the configuration, performance metrics, and cost analysis.
  • Deploy a trimmed‐down version on an edge device and compare differences in inference speed and resource usage.

Deliverables​

  • A comprehensive research report comparing cloud and edge deployment scenarios with experimental results.
  • A live demo (or recorded walkthrough) demonstrating deployment on at least one cloud service and one edge device.
  • Architecture diagrams and detailed configuration guides hosted in a public repository.

Day 121-135: Security in Model Deployment​

Topics Covered​

  • API & Data Security:
    • Securing RESTful APIs with OAuth2.0 and JWT.
    • Encrypting model weights and securing data in transit with TLS.
  • Deployment Security:
    • Implementing network policies in Kubernetes and ensuring compliance with security best practices.

Hands‐on Tasks​

  • Implement OAuth/JWT authentication in the deployed API.
  • Configure TLS for secure communication between services.
  • Demonstrate encryption of model files at rest.

Deliverables​

  • A security best practices document with detailed setup instructions.
  • A code demo showcasing secured endpoints and encrypted communications.
  • Example configuration files for TLS and authentication, published on GitHub and accompanied by a detailed blog post.

Day 136-150: Performance Benchmarking & Stress Testing​

Topics Covered​

  • Load Testing:
    • Using tools such as Apache JMeter or Locust to simulate high traffic.
    • Measuring response time, throughput, and error rates under load.
  • Stress Testing:
    • Identifying system limits and performance bottlenecks.
    • Evaluating performance under resource saturation.

Hands‐on Tasks​

  • Develop test scripts for JMeter/Locust to simulate concurrent requests.
  • Execute load tests and collect performance metrics.
  • Analyze data to tune resource allocation and scaling parameters.

Deliverables​

  • A detailed benchmarking report with graphs and tables comparing performance under different loads.
  • Sample test scripts integrated into the CI/CD pipeline.
  • Public documentation outlining testing methodologies and performance tuning suggestions.

Day 151-165: Production‐Grade Deployment​

Topics Covered​

  • Scalability & Resilience:
    • Integrating load balancers (e.g., Nginx, HAProxy), autoscaling groups, and fault‐tolerance patterns (circuit breakers, retries).
  • Real‐World Simulation:
    • Simulating live traffic and monitoring system response to common issues (model drift, service outages).

Hands‐on Tasks​

  • Develop a production‐grade deployment plan including load balancing and auto‐scaling configurations.
  • Simulate live traffic and capture analytics via monitoring tools.
  • Troubleshoot common issues and document remediation steps.

Deliverables​

  • A comprehensive production deployment plan document with detailed architecture diagrams.
  • A live demo or recorded walkthrough of a production‐grade deployment, including analytics reports.
  • A post‐deployment troubleshooting guide and performance analysis report.

Day 166-180: Public Contribution & Collaboration​

Topics Covered​

  • Open‐Source Practices:
    • Setting up a public contribution pipeline with detailed issue tracking, automated pull request reviews (using bots like Dependabot), and contributor guidelines.
  • Community Engagement:
    • Establishing moderation systems and maintaining code quality in an open‐source environment.

Hands‐on Tasks​

  • Implement contribution guidelines, issue templates, and pull request templates in the repository.
  • Configure automated bots for CI/CD integration and issue tagging.
  • Organize a community “code sprint” to onboard external contributors.

Deliverables​

  • A fully implemented public contribution system integrated into the CI/CD workflow.
  • A public GitHub repository with detailed contribution guidelines, issue templates, and moderation processes.
  • Final comprehensive project documentation and a summary blog post on collaboration best practices.

Tech Stack​

  • Languages & Frameworks:
    • Python, TensorFlow, PyTorch
  • Deployment & Containerization:
    • Docker, Kubernetes, ONNX, NVIDIA TensorRT, Intel OpenVINO
  • APIs & Web Frameworks:
    • FastAPI, Flask
  • CI/CD & Versioning:
    • GitHub Actions, Jenkins
  • Monitoring & Logging:
    • Prometheus, Grafana
  • Cloud Platforms:
    • AWS, Google Cloud, Azure
  • Documentation:
    • Draw.io, Markdown, LaTeX