Skip to main content

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: Deployment Fundamentals & Environment Setup​

Topics Covered​

  • Deployment Challenges:
    • Managing large model sizes (e.g., 200+ MB).
    • Latency bottlenecks in real-time inference.
    • Hardware-specific optimizations (CPU vs. GPU).
    • Framework compatibility (TensorFlow vs. PyTorch).
    • Ensuring reproducibility and avoiding model drift.
  • Best Practices:
    • Docker for containerization and reproducibility.
    • Git for version control; GitHub Actions for CI/CD.
    • Writing modular, testable code for robust deployment.
  • Model Export & Optimization:
    • ONNX, TorchScript, and TensorFlow SavedModel formats.
    • Common conversion pitfalls: unsupported ops, shape mismatches.
    • Basics of post-training quantization and pruning.

Hands‐on Tasks​

  • Set up Python 3.8+ environment using Conda or virtualenv.
  • Install Docker and create a CUDA-based container (if using GPU).
  • Build a GitHub repo with a sample “Hello Inference!” model.
  • Convert a PyTorch model (e.g., ResNet18) to ONNX.
  • Configure a basic CI/CD pipeline using GitHub Actions for testing and Docker image builds.
  • Perform simple quantization using PyTorch Toolkit and measure baseline vs quantized inference speed.

Deliverables​

  • A public GitHub repo with:
    • Dockerfile (with optional CUDA support).
    • CI/CD config (GitHub Actions).
    • ONNX export script and quantization example.
  • Summary report describing:
    • Key deployment challenges and solutions.
    • Conversion and quantization workflow (with screenshots and benchmarks).

Day 16-30: Scalable API Deployment & Performance Optimization​

Topics Covered​

  • API Development:
    • RESTful APIs using FastAPI (vs Flask).
    • Routing, validation, and error handling.
    • Deploying optimized inference pipelines.
  • Inference Acceleration:
    • Integration with NVIDIA TensorRT (GPU) and Intel OpenVINO (CPU).
    • Efficient memory management and multi-threaded inference.
  • Model Optimization Techniques:
    • Advanced quantization (static/dynamic).
    • Pruning (structured/unstructured).
    • Basic knowledge distillation to compress large models.
  • Multi-Model & Ensemble Strategies:
    • Designing lightweight ensemble systems (e.g., averaging, fallback).
    • Managing multiple endpoints in a low-latency API.

Hands‐on Tasks​

  • Build a FastAPI-based inference API (e.g., image classifier).
  • Integrate ONNX Runtime or TensorRT for inference acceleration.
  • Conduct performance benchmarking (latency/throughput).
  • Apply pruning or distillation to a ResNet or DistilBERT model.
  • Implement a lightweight ensemble system (e.g., 2 models with simple aggregation logic).
  • Write unit tests and update the CI/CD pipeline to auto-deploy on push.

Deliverables​

  • Public GitHub repo with:
    • FastAPI-based inference API.
    • Benchmarking scripts comparing standard and accelerated inference.
    • Optimization experiments (quantization, pruning/distillation).
    • Lightweight ensemble dispatcher script.
  • Blog/tutorial post covering:
    • Full deployment pipeline and benchmarks.
    • Key lessons and trade-offs in real-world optimization.

Day 31 onwards: Research Papers (Till Day 180)​

After the first 30 days of the program, once all the basics are covered, we'll start providing you Research Papers to implement on your own. There is a proper structure that needs to be followed for this review:

Research Papers​

You'll be receiving Research Papers from the Management Team to implement. Once you're done with the first 30 days of the program, you may connect with someone from the Management to request for your papers. You are required to review 1 paper every 20 days so essentially, you'll be reviewing 7 research papers throughout the program, considering a 10 day buffer, making this a 180-Day Module.

Submission Requirements:​

  • README
    • Proper documentation with summary and steps for running the code
  • Proper functioning Code
    • With comments
  • Research References
  • Video Demo
    • Video should be at least 5 minutes in length.
    • Video should be in Landscape (16:9 Ratio) and should compulsarily record your device screen with the code running.
    • You should explain all the aspects of the code implementation and what you've learnt out of the research paper.
    • Audio should be clear with minimal to no background noise.
    • If the demo requires having your video or some kind of sample input given, it should be properly demonstrated first and should be of good quality.
    • There should be no cuts in the video.
    • Video should compulsarily be in one of the following formats:
      • .mkv
      • .mov
      • .mp4
      • .webm
      • .avi

Outcomes (What exactly is Circuit Breakers?)​

All of the reviews submitted will go on our Github Repo and YouTube Channel with credits to the Whole Team and will remain their always for people to reference. You can also add the direct links to the Github and YouTube to your resume for reference in future.
This is what we'll be doing with your submissions:

  • The code implementation and documentation will be published to our Github Repo.
  • The video submitted will be published on our YouTube Channel.

Tech Stack​

  • Languages & Frameworks:
    • PyTorch, TensorFlow, ONNX
  • APIs & Web Frameworks:
    • FastAPI, Flask
  • Optimization:
    • TensorRT, OpenVINO, PyTorch Quantization
  • Deployment:
    • Docker, GitHub Actions
  • Documentation:
    • Draw.io, Markdown, LaTeX