MLOps & GenAI Program

Overview

This is a two-month training program (weekend sessions) designed for students with no prior AI/ML experience. You’ll learn the fundamentals of shipping ML systems (MLOps) and building modern Generative AI apps (prompting, RAG, LangChain, agentic systems).

Course plan

• Schedule: Saturday + Sunday, 7:00–9:00 AM EST (2 hours each)
• Time commitment: 4 hours/week (2–3 months)
• Assessment: Open-book test at the end of each course
• Certificate: Certificate of completion from Monk Technologies upon passing

Pricing & payment

• Price: $500
• Payment link: paypal.me/monktechnologies
• After payment, share a screenshot of the transaction.

Prerequisite (optional): Python fundamentals

Python fundamentals are required for the MLOps Fundamentals, GenAI & Prompt Engineering courses. This is an optional class for learners already comfortable with Python.

Course 1: MLOps Fundamentals

Target audience: Students with no prior experience in AI/ML

Level: Introductory to Intermediate

Delivery: Hands-on sessions facilitated by the instructor

Module 1: Introduction to MLOps

• Understanding the need for MLOps
• Differences between MLOps, DevOps, and DataOps
• Overview of the MLOps lifecycle
• Roles and responsibilities of an MLOps engineer

Module 2: Data Management and Preprocessing

• Data collection and validation techniques
• Data transformation and feature engineering
• Tools: Pandas, NumPy, and Scikit-learn
• Best practices for data versioning

Module 3: Model Development and Experimentation

• Overview of model training and evaluation
• Experiment tracking with MLflow
• Hyperparameter tuning basics
• Introduction to model registries

Module 4: Model Packaging and Deployment (2 hrs)

• Containerization with Docker
• Serving models using BentoML
• Deployment strategies: REST APIs and batch processing
• Introduction to cloud deployment options

Module 5: CI/CD for Machine Learning (2 hrs)

• Setting up CI/CD pipelines for ML projects
• Integration with GitHub Actions and Jenkins
• Automating testing and deployment processes
• Monitoring pipeline performance

Module 6: Monitoring and Maintenance

• Monitoring model performance in production
• Detecting and handling data drift
• Logging and alerting mechanisms
• Tools: Prometheus, Grafana, and ELK stack

Module 7: Capstone Project and Review (3.5 hrs)

• End-to-end MLOps project implementation
• Review of key concepts and best practices
• Q&A session and feedback
• Discussion on real-world MLOps scenarios

---

Course 2: Generative AI & Prompt Engineering

Target audience: Students with no prior experience in AI/ML

Level: Introductory to Intermediate

Delivery: Hands-on sessions facilitated by the instructor

Module 1: Introduction to Generative AI

• Understanding Generative AI and its applications
• Overview of Large Language Models (LLMs)
• Ethical considerations in Generative AI
• Introduction to popular Generative AI tools

Module 2: Prompt Engineering Basics

• Crafting effective prompts for LLMs
• Techniques: Zero-shot, Few-shot, and Chain-of-Thought prompting
• Common pitfalls and how to avoid them
• Hands-on exercises with prompt design

Module 3: Retrieval-Augmented Generation (RAG)

• Understanding the RAG architecture
• Integrating external knowledge sources
• Implementing RAG with vector databases
• Use cases and best practices

Module 4: Introduction to LangChain

• Overview of LangChain framework
• Building applications with LangChain
• Integrating LLMs with external tools and APIs
• Hands-on project using LangChain

Module 5: Agentic AI and Autonomous Agents

• Concept of autonomous AI agents
• Designing and deploying AI agents
• Tools and frameworks for Agentic AI
• Ethical considerations and safety measures

Module 6: Advanced Prompt Engineering Techniques

• Exploring advanced prompting strategies
• Evaluating and refining prompt outputs
• Case studies of successful prompt engineering
• Hands-on exercises with complex prompts

Module 7: Capstone Project and Review

• Developing a Generative AI application from scratch
• Incorporating RAG, LangChain, and Agentic AI concepts
• Peer review and feedback sessions
• Final Q&A and course wrap-up

---

Enroll / ask questions

Chat with us on WhatsApp: Enroll via WhatsApp