AI Product Development & MLOPs Full Syllabus

Duration: 10–12 Weeks | Level: Intermediate to Advanced
Ideal For: AI Engineers, Product Managers, ML Enthusiasts, Developers, Startup Founders
Goal: Learn how to build real AI products from scratch and deploy them with proper MLOps workflows that scale.

Module 1: Foundations of AI Product Thinking

• What is an AI product? How it differs from traditional software
• Real-world AI product examples: ChatGPT, TikTok, Google Lens, etc.
• Key roles: AI Product Manager vs Data Scientist vs MLOps Engineer
• Importance of user feedback and iteration in AI-based products

Activity: Analyze a popular AI product and break down its key features

Module 2: Identifying AI Use-Cases & Problem Statements

• Business-first vs model-first approach
• Choosing the right AI use case for a product
• Defining problem statements the right way (e.g., “Improve conversion rate using AI”)
• Common use cases: recommendation systems, chatbots, fraud detection, image tagging

Task: Draft a one-pager for your own AI product idea

Module 3: Data Collection, Preparation & Labeling

• Understanding data needs based on use-case
• Collecting and cleaning data (structured & unstructured)
• Data labeling and annotation strategies
• Tools: Labelbox, CVAT, Roboflow, Amazon SageMaker Ground Truth
• Ethics and privacy in data sourcing

Hands-on: Annotate a dataset using CVAT or Roboflow

Module 4: Model Selection, Training & Evaluation

• Choosing the right ML model (traditional ML vs Deep Learning)
• Using pre-trained models (e.g., HuggingFace, OpenAI, TensorFlow Hub)
• Fine-tuning vs training from scratch
• Evaluation metrics for classification, regression, NLP, vision, etc.
• Iterating based on performance

Project: Train a basic image classifier or sentiment analysis model

Module 5: AI Frameworks & Toolkits Overview

• PyTorch vs TensorFlow vs Scikit-learn
• ONNX, HuggingFace Transformers, LangChain
• AutoML tools: Google Vertex AI, H2O.ai, Amazon SageMaker
• Integration with apps using FastAPI or Flask

Task: Build and serve a small AI model using FastAPI

Module 6: MLOps Fundamentals

• What is MLOps and why it’s critical
• Key MLOps lifecycle stages: experimentation → training → testing → deployment → monitoring
• Version control for models and data
• Tools overview: MLflow, DVC, Weights & Biases, Kubeflow

Diagram Task: Draw a full MLOps pipeline for your project

Module 7: CI/CD for ML (DevOps Meets AI)

• How CI/CD works for ML: from notebook to production
• GitHub Actions / GitLab CI for ML pipelines
• Automating training, testing, deployment
• Containers (Docker) for reproducibility
• Model registry and tracking (MLflow)

Hands-on: Deploy a model using GitHub + Docker + MLflow

Module 8: Model Deployment (Real World Focus)

• REST APIs vs batch inference vs real-time inference
• Serving models at scale using:
  • Flask/FastAPI
  • TensorFlow Serving
  • TorchServe
  • NVIDIA Triton
• Cloud deployment: AWS, GCP, Azure basics
• Using Streamlit/Gradio for prototypes

Project: Deploy your AI model as a working web app

Module 9: Monitoring, Feedback Loops & Model Drift

• What happens after deployment?
• Monitoring model performance in production
• Data drift, concept drift, user drift
• Feedback loop: improving models using real-world data
• Tools: EvidentlyAI, Prometheus, Grafana, BentoML

Mini Case Study: What caused Zillow’s AI pricing model to fail?

Module 10: Responsible AI, Ethics & Scalability

• Bias in AI models and how to reduce it
• Fairness, Explainability (XAI), Accountability
• Data privacy laws: GDPR, HIPAA
• Scaling AI products across users and geographies
• Productization challenges and cost optimization

Discussion: How should we design AI for fairness + scalability?

Module 11: Building AI Products with LLMs (Optional Advanced Module)

• Introduction to LLMs (Large Language Models) like GPT, Claude, Gemini
• Prompt engineering and chain-of-thought
• Tools: LangChain, LlamaIndex, Pinecone
• Use-cases: AI chatbots, document search, summarization
• LLMOps introduction

Hands-on: Build a GPT-powered Q&A bot with your own data

Final Capstone Projects (Pick One or Build Your Own)

1. AI Resume Screening Tool + Feedback Loop
2. Sales Forecasting Web App with Model Retraining
3. MLOps Pipeline using GitHub, Docker, MLflow & FastAPI
4. AI Document Search Tool using LangChain + OpenAI

After This Course, You’ll Be Able To:

• Build & deploy AI products end-to-end, not just toy models
• Set up proper version control and CI/CD for ML projects
• Understand and apply real-world MLOps workflows
• Handle data pipelines, drift, and retraining cycles
• Combine AI + product design thinking for user-friendly solutions

Tools You’ll Use Throughout:

Area	Tools
Data & Modeling	Pandas, Scikit-learn, PyTorch, TensorFlow
MLOps	MLflow, DVC, Weights & Biases, GitHub Actions
Deployment	FastAPI, Streamlit, Docker, HuggingFace Spaces
Cloud & Scaling	AWS S3, EC2, Lambda, GCP Vertex AI
UI & Prototypes	Gradio, Streamlit, Next.js (optional)