FULLSTACK MLOPS: DATA ENGINEERING, MLOPS & GENAI

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The Full-Stack MLOps: Data Engineering, MLOps & GenAI syllabus covers the end-to-end pipeline of modern AI systems — from data ingestion and processing to model deployment, monitoring, and GenAI integration. It explains how real-world ML workflows are automated and scaled, making it essential for building production-ready AI solutions. This training is ideal for data engineers, ML engineers, developers, and professionals aiming to work in AI, automation, and cloud-based machine learning environments.

Duration of Training : 70 Hours
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Detailed Syllabus • Hands-on Labs • Assignments • Projects

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Overview

The Syllabus

Curriculum Designed by Experts

MODULE 1 — Introduction to MLOps (3 Hours)

Topics
What is MLOps? Evolution from DevOps
ML lifecycle overview: Data → Training → Deployment → Monitoring
MLOps maturity levels (0–3)
Problems solved by MLOps in real enterprises
Concepts: Experiment tracking, reproducibility, CI/CD for ML, automation
Tools landscape: Airflow, DVC, MLflow, Docker, K8s, LangChain
Hands-on Labs
Setup Python environment (Conda/Poetry)
Setup GitHub repo + branching strategy
Create a baseline ML training script
Assignments
Create ML project folder structure using best practices
Setup GitHub project with README, workflow diagram

MODULE 2 — Machine Learning Concepts Refresher (3 Hours)

Topics
ML workflow recap
Data preprocessing, splitting, cross-validation
Metrics: accuracy, F1, AUC, MAE, RMSE
Bias-variance & model generalization
Introduction to model serialization (pickle/ONNX)
Hands-on Labs
Train baseline model (LogReg/RandomForest)
Save model artefacts
Assignments
Build multiple models, evaluate and log metrics

MODULE 3 — Data Engineering with Apache Airflow (8 Hours)

Topics
Airflow architecture: Scheduler, Webserver, Workers
DAGs, operators, sensors, XCom
Task flow API
Airflow scheduling & orchestration
Integrating with databases, APIs, cloud storage
CI/CD for Airflow DAGs (GitHub Actions)
Hands-on Labs
Install Airflow (Docker Compose)
Build DAG for:
Data ingestion (API → local/DB)

MODULE 4 — Data Version Control (DVC) (5 Hours)

Topics
Why dataset versioning matters
DVC remote backends: S3, GCS, Azure
Pipelines, metrics, params.yaml
Model reproducibility
DVC + GitOps workflow
Hands-on Labs
Initialize DVC
Track dataset + preprocessing outputs
DVC pipeline:
data → features → model
Push to S3
Compare multiple experiments
Assignments
Create complete DVC pipeline with metrics + params
Reproduce experiments via CLI

MODULE 5 — MLflow (Local) (6 Hours)

Topics
MLflow components:
Tracking
Models
Registry
Projects
Logging metrics, parameters, artifacts
MLflow UI
Model promotion lifecycle
Packaging MLflow models for deployment
Hands-on Labs
Setup MLflow locally
Log:
Parameters
Metrics
Confusion matrix
Model artefacts
Register a model version
Transition “Staging → Production”
Assignments
Build experiment pipeline with MLflow logging
Store multiple runs + compare

MODULE 6 — MLflow with AWS (7 Hours)

Topics
Architecture: MLflow + S3 + EC2
Using MLflow tracking URI
Storing artifacts in S3
Dockerizing MLflow server
AWS IAM roles for MLflow
Deploying MLflow model on SageMaker
Hands-on Labs
Configure MLflow remote tracking
Run Elastic Compute MLflow server
Deploy Registered Model on SageMaker endpoint
Test production endpoint with API calls
Assignments
Deploy MLflow server using Docker on EC2
Register + deploy MLflow model to SageMaker

MODULE 7 — CI/CD for ML Pipelines (8 Hours)

Topics
CI vs CD vs CT (Continuous Training)
GitHub Actions / GitLab CI pipelines for ML:
Lint → Test → Train → Validate → Deploy
Model testing automation
ML pipeline triggers
CD for Airflow & MLflow
Hands-on Labs
Build GitHub Actions pipeline:
Run unit tests
Train model
Log run to MLflow
Push Docker image
Automated deployment to:
Kubernetes
AWS ECR → ECS/SageMaker
Assignments
Create CI/CD with automated retraining
Push Docker image containing ML model
Create model registry → prod deployment workflow

MODULE 8 — Monitoring & Observability (5 Hours)

Topics
Data drift vs model drift
Monitoring for ML services
EvidentlyAI dashboards
Prometheus, Grafana
Real-time alerting
Monitoring LLM & RAG pipelines
Hands-on Labs
Create monitoring dashboard
Setup drift dashboards
Add Prometheus exporter to API service
Assignments
Build monitoring pipeline for classification/regression model Create Grafana dashboard with drift alerts

MODULE 9 — Containerization with Docker (4 Hours)

Topics
Dockerfile best practices for ML
Image optimization
Multi-stage builds
Docker Compose
GPU-enabled containers (NVIDIA)
Hands-on Labs
Containerize ML model
Build inference API using FastAPI
Run inference inside a container
Push to ECR/GCR/ACR
Assignments
Build optimized Docker image (<300MB)
Containerize ML pipeline step

MODULE 10 — Kubernetes for ML Deployment (7 Hours)

Topics
Pods, Deployments, Services, ConfigMaps
Autoscaling (HPA)
Secrets management
Using GPUs in K8s
KFServing / Seldon Core for ML serving
Canary deployment for ML models
Hands-on Labs
Deploy ML API on Minikube
Add autoscaling based on CPU/latency
Integrate K8s with MLflow model
Test production rollout
Assignments
Deploy ML model to Kubernetes with CI/CD
Implement canary rollout to compare new vs old model

MODULE 11 — Introduction to LLMs & GenAI (4 Hours)

Topics
Evolution of transformer → LLM
Architecture of GPT, Llama, Mistral
Embeddings: BERT, SentenceTransformers
Prompt engineering
Inference optimization (quantization, caching)
Hands-on Labs
Text embeddings generation
Prompt-based text generation with open models
Assignments
Compare performance of multiple embedding models

MODULE 12 — RAG Systems & Vector Databases (8 Hours)

Topics
RAG Architecture
Documents → Chunks → Embeddings → Index → Retrieval
Vector DBs:
Pinecone
FAISS
Weaviate
LangChain vs LlamaIndex
RAG evaluation metrics
Hands-on Labs
Build RAG pipeline using LangChain
Store embeddings in Pinecone
Query top-K chunks
Build question-answering chatbot
Add metadata filtering
Assignments
Build custom RAG search engine
Create multi-document QA chatbot
Evaluate RAG with RAGAS

MODULE 13 — LLM Deployment (LLMOps) (6 Hours)

Topics
LLM inference optimization:
quantization
LoRA/QLoRA
batching
Guardrails & moderation
LLM cost optimization strategies
Observability for LLM workloads
Async workers, load balancing
OpenAI-compatible server deployment
Hands-on Labs
Deploy LLM with FastAPI
Add guardrails (GuardrailsAI/Pydantic)
Log prompts & responses for monitoring
Deploy LLM on:
AWS EC2
GCP Vertex AI
Azure OpenAI (optional)
Assignments
Deploy production-grade LLM API
Add evaluation + monitoring pipeline

CAPSTONE PROJECT (6 Hours Guided + Odline Work)

Build a Full Production-Grade MLOps Pipeline with LLM RAG Integration

Pipeline Structure
Data Engineering
Airflow DAG for ETL
DBT transforms
S3 storage
Training Pipeline
DVC versioning
MLflow tracking
Automated CI/CD training
Model Deployment
Docker + Kubernetes
Autoscaling
Monitoring
Drift detection
Grafana dashboard
GenAI/RAG Integration
Pinecone vector DB
RAG answering layer
LLM deployment (FastAPI)
Deliverables
End-to-end GitHub repo
Architecture diagram
CI/CD pipelines
Airflow DAGs
LLM RAG system
Deployment YAMLs
Final demonstration video

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Full-stack Mlops: Data Engineering, Mlops & GenAI Course Certification With Training In Bangalore

Q: MODULE 1 — Introduction to MLOps (3 Hours)

Topics What is MLOps? Evolution from DevOps ML lifecycle overview: Data → Training → Deployment → Monitoring MLOps maturity levels (0–3) Problems solved by MLOps in real enterprises Concepts: Experiment tracking, reproducibility, CI/CD for ML, automation Tools landscape: Airflow, DVC, MLflow, Docker, K8s, LangChain Hands-on Labs Setup Python environment (Conda/Poetry) Setup GitHub repo + branching strategy Create a baseline ML training script Assignments Create ML project folder structure using best practices Setup GitHub project with README, workflow diagram

Q: MODULE 2 — Machine Learning Concepts Refresher (3 Hours)

Topics ML workflow recap Data preprocessing, splitting, cross-validation Metrics: accuracy, F1, AUC, MAE, RMSE Bias-variance & model generalization Introduction to model serialization (pickle/ONNX) Hands-on Labs Train baseline model (LogReg/RandomForest) Save model artefacts Assignments Build multiple models, evaluate and log metrics

Q: MODULE 3 — Data Engineering with Apache Airflow (8 Hours)

Topics Airflow architecture: Scheduler, Webserver, Workers DAGs, operators, sensors, XCom Task flow API Airflow scheduling & orchestration Integrating with databases, APIs, cloud storage CI/CD for Airflow DAGs (GitHub Actions) Hands-on Labs Install Airflow (Docker Compose) Build DAG for: Data ingestion (API → local/DB)

Q: MODULE 4 — Data Version Control (DVC) (5 Hours)

Topics Why dataset versioning matters DVC remote backends: S3, GCS, Azure Pipelines, metrics, params.yaml Model reproducibility DVC + GitOps workflow Hands-on Labs Initialize DVC Track dataset + preprocessing outputs DVC pipeline: data → features → model Push to S3 Compare multiple experiments Assignments Create complete DVC pipeline with metrics + params Reproduce experiments via CLI

Q: MODULE 5 — MLflow (Local) (6 Hours)

Topics MLflow components: Tracking Models Registry Projects Logging metrics, parameters, artifacts MLflow UI Model promotion lifecycle Packaging MLflow models for deployment Hands-on Labs Setup MLflow locally Log: Parameters Metrics Confusion matrix Model artefacts Register a model version Transition “Staging → Production” Assignments Build experiment pipeline with MLflow logging Store multiple runs + compare

Q: MODULE 6 — MLflow with AWS (7 Hours)

Topics Architecture: MLflow + S3 + EC2 Using MLflow tracking URI Storing artifacts in S3 Dockerizing MLflow server AWS IAM roles for MLflow Deploying MLflow model on SageMaker Hands-on Labs Configure MLflow remote tracking Run Elastic Compute MLflow server Deploy Registered Model on SageMaker endpoint Test production endpoint with API calls Assignments Deploy MLflow server using Docker on EC2 Register + deploy MLflow model to SageMaker

Q: MODULE 7 — CI/CD for ML Pipelines (8 Hours)

Topics CI vs CD vs CT (Continuous Training) GitHub Actions / GitLab CI pipelines for ML: Lint → Test → Train → Validate → Deploy Model testing automation ML pipeline triggers CD for Airflow & MLflow Hands-on Labs Build GitHub Actions pipeline: Run unit tests Train model Log run to MLflow Push Docker image Automated deployment to: Kubernetes AWS ECR → ECS/SageMaker Assignments Create CI/CD with automated retraining Push Docker image containing ML model Create model registry → prod deployment workflow

Q: MODULE 8 — Monitoring & Observability (5 Hours)

Topics Data drift vs model drift Monitoring for ML services EvidentlyAI dashboards Prometheus, Grafana Real-time alerting Monitoring LLM & RAG pipelines Hands-on Labs Create monitoring dashboard Setup drift dashboards Add Prometheus exporter to API service Assignments Build monitoring pipeline for classification/regression model Create Grafana dashboard with drift alerts

Q: MODULE 9 — Containerization with Docker (4 Hours)

Topics Dockerfile best practices for ML Image optimization Multi-stage builds Docker Compose GPU-enabled containers (NVIDIA) Hands-on Labs Containerize ML model Build inference API using FastAPI Run inference inside a container Push to ECR/GCR/ACR Assignments Build optimized Docker image (<300MB) Containerize ML pipeline step

Q: MODULE 10 — Kubernetes for ML Deployment (7 Hours)

Topics Pods, Deployments, Services, ConfigMaps Autoscaling (HPA) Secrets management Using GPUs in K8s KFServing / Seldon Core for ML serving Canary deployment for ML models Hands-on Labs Deploy ML API on Minikube Add autoscaling based on CPU/latency Integrate K8s with MLflow model Test production rollout Assignments Deploy ML model to Kubernetes with CI/CD Implement canary rollout to compare new vs old model

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