MLOps & LLMOPS Online Training Course

About DeepNeuron

DeepNeuron stands as a prominent e-learning platform, offering live instructor-led sessions. Our platform boasts a user-friendly and cost-effective learning solution, providing accessible and interactive online training. Serving both professionals and students, we have amassed millions of learners globally. Our diverse student base spans across countries such as the US, India, the UK, Canada, Singapore, Australia, the Middle East, and others. Covering a wide array of categories including MLOps, LLMOps, Gen AI, DevOps, Cloud Computing, Data Science, Artificial Intelligence, Power BI, Cybersecurity, Business Intelligence, Automation Testing, Project Management, Programming, and Web Development, we've fostered a thriving community exceeding 20k+ learners worldwide.

🚀 MLOps & LLMOps Course Roadmap Overview

Phase 1: Machine Learning Foundation
Phase 2: ML Project Deployment Lifecycle
Phase 3: MLOps Toolchain & Automation
Phase 4: Capstone Projects (Retail & Banking)
Phase 5: LLMOps for Real-World Use Cases

Phase 1: Machine Learning Foundation

Basic Python, Pandas, Scikit-learn
Supervised/Unsupervised Learning
Model Evaluation (accuracy, F1, etc.)

Phase 2: Machine Learning Project (Retail Sales Forecasting)

Data Cleaning and Feature Engineering
Training and Evaluating Regression Models
Saving Model as Pickle or Joblib
Testing Inference Script

Phase 3: MLOps Tools & Concepts

Version Control with Git & GitHub
Docker for containerization
Kubernetes for orchestration
MLflow for experiment tracking
Airflow for scheduling pipelines
Kubeflow for model training pipelines
AWS Lambda, SageMaker, CodePipeline
Terraform & CloudFormation for IaC

Phase 4: Advanced MLOps Capstone Projects

🛍️ Project 1: Retail Product Demand Forecasting (Kubernetes + MLflow + Airflow)

Automate data preprocessing using Airflow DAG
Model training pipeline tracked with MLflow
Deploy model using Docker and expose via Kubernetes Ingress
Version everything with GitHub Actions

🏦 Project 2: Banking Loan Default Prediction (SageMaker + Lambda + Step Functions)

Use SageMaker to train and host the model
Trigger inference through AWS Lambda
Use AWS Step Functions to orchestrate re-training when data drift is detected

🧾 Project 3: Insurance Fraud Detection (Kubeflow + Terraform + MLflow)

Use Kubeflow Pipelines for training and evaluation
Track experiments using MLflow integrated with S3
Deploy infrastructure using Terraform

🛒 Project 4: Personalized Recommendation Engine (Airflow + Docker + Kubernetes)

Build pipeline to retrain recommender model weekly
Automated ETL using Airflow + Docker
CI/CD with GitHub → ECR → EKS deployment

Phase 5: LLMOps Course

🔍 Fundamentals

What are LLMs?
Tokenization, Attention Mechanism
Prompt Engineering Basics

⚙️ LLMOps Tools

LangChain, Haystack for orchestration
LLM tracking with MLflow & Weights & Biases
Serving via FastAPI, Docker, and Kubernetes
RAG (Retrieval Augmented Generation)

📦 Deployment Stack

LLM Model Hosting (e.g. Falcon, Mistral, GPT-J)
Embedding Models with FAISS/ChromaDB
LangChain Agents for complex workflows
CI/CD using GitHub + Docker + Lambda + ECS

Phase 6: Advanced LLMOps Projects

📚 Project 1: RAG Chatbot for Internal Documentation (LangChain + Chroma + Streamlit)

Ingest docs → Chunk & embed → Query via RAG
Use LangChain for routing + ChromaDB for vector storage
UI built using Streamlit

📄 Project 2: Legal Document Summarizer (FastAPI + HuggingFace Transformers + Docker)

Upload legal PDFs → summarize sections using LLM
Run inference using quantized local models
Serve app with FastAPI + Docker

💬 Project 3: Customer Support Q&A Bot (RAG + Lambda + S3 + FAISS)

Query customer issues against FAQ embeddings
Use Lambda to deploy serverless backend
Store queries and logs in S3 for feedback loop

📈 Project 4: Financial Report Analyzer Bot (LangChain + Airflow + Weights & Biases)

Automate ingestion of new quarterly reports using Airflow
Use LangChain + LLM to analyze KPIs
Track model and prompt performance via Weights & Biases