FULL STACK COURSES

★★★★★

(2,095 ratings) 50,000+ Student

FULLSTACK MLOPS: DATA ENGINEERING, MLOPS & GENAI

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.

Radical Technologies

50,000+ English 70 hours Weekdays / Weekends Classroom / Online / Corporate

Online / Classroom

IT Training Programme

Duration 70 hours

Batch Type Weekdays / Weekends

Mode of Training Classroom / Online / Corporate

Locations Pune, Bangalore, Kochi

Language English

Certification Globally Recognized

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100% placement assistance

What you'll learn

Understand core concepts and architecture from the ground up

Get hands-on with the tools used by working professionals

Build real-world projects you can add to your portfolio

Learn industry best practices and coding standards

Practice with real datasets and real-world scenarios

Prepare for certification and technical interviews

Work on collaborative, team-based exercises

Apply performance tuning and optimization techniques

Understand how the technology fits into a larger ecosystem

Complete assignments reviewed by mentors

Programme Overview

14 sections covering the complete curriculum — a single, progressive learning arc.

70 hours

Training Duration

Core Modules

187

Total Lessons

4.4★

Average Rating

50K+

Students Trained

Foundations & Core Concepts

Get hands-on with the fundamentals and architecture — the building blocks for everything that follows.

Fundamentals Architecture Setup

Hands-On Practical Training

Work through real exercises and assignments designed to mirror what you will do on the job.

Practicals Assignments Labs

Real-World Projects

Apply what you have learned to end-to-end projects that go straight into your portfolio.

Projects Portfolio Case Studies

Advanced Techniques

Go beyond the basics with advanced concepts, integrations and production-grade practices.

Advanced Integration Best Practices

Ecosystem Integration

Understand how this technology connects with the broader tools and platforms used in the industry.

Ecosystem Tools Platforms

Performance & Interview Prep

Master optimization techniques and prepare for the technical interview questions employers actually ask.

Optimization Interview Prep Certification

Who is this programme for?

Whether you're already writing code, working with data, or supporting applications today — this programme is built to take you into a FULL STACK COURSES role.

Software Developers

Engineers who want to add this skill set to their toolkit

Analysts & Consultants

Professionals moving into a more technical, hands-on role

IT Professionals

System admins and support engineers upskilling into a new domain

Fresh Graduates

CS/IT graduates aiming for a job-ready technical role

Course Curriculum

14 sections • 187 lessons • 70 hours

Jump to Section

Total Duration

70 hours

100% Practical · Unlimited Doubt Sessions

01 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

02 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

03 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)

04 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

05 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

06 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

07 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

08 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

09 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

10 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

11 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

12 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

13 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

14 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

Tools & Technologies

Every tool listed here is installed, configured and used in a hands-on lab session.

Core Tools

Hands-On Labs

Practical Environment

Industry-Standard Tools

Real-World Setup

Guided Exercises

Skill Building

Sample Datasets

Practice Material

Practice & Projects

Mini Projects

Applied Practice

Assignments

Mentor Reviewed

Doubt Sessions

Live Support

Career Readiness

Resume Building

Career Support

Mock Interviews

Interview Prep

Certification Prep

Global Recognition

Deployment & Delivery

Production Practices

Real-World Ready

Best Practices

Industry Standards

187+

Hands-On Lessons

Core Modules

70 hours

Training Duration

100%

Practical Training

You don't just learn FULLSTACK MLOPS: DATA ENGINEERING, MLOPS & GENAI. You ship it.

Three major projects, each mirroring how production teams actually work — from guided foundations to a portfolio-ready capstone.

PROJECT // 01

Guided Foundation Project

→Requirement Analysis

→Guided Implementation

→Mentor Review

→Iteration

Foundation Beginner

Apply the fundamentals in a structured, mentor-reviewed project

Take the core concepts from the first half of the curriculum and apply them to a realistic scenario, with guidance and feedback from your mentor at every step.

Structured project brief

Step-by-step implementation

Mentor feedback and review

Documented outcome

Stack Core Concepts Best Practices

PROJECT // 02

Applied Practice Project

→Scenario Design

→Independent Build

→Testing & Validation

→Peer Review

Applied Intermediate

Build a more independent project mirroring real production scenarios

Work through a project that combines multiple concepts from the curriculum, closer to how work is actually structured on the job — less hand-holding, more ownership.

End-to-end implementation

Testing and validation

Documentation

Peer/mentor review

Stack Applied Skills Testing

PROJECT // 03

Capstone Project

→Planning

→End-to-End Build

→Review & Refinement

→Presentation

Capstone Advanced

Take a project from requirements to a polished, portfolio-ready deliverable

Your final project — plan, build, test and present a complete solution using everything covered in the curriculum, reviewed by mentors before you graduate.

Complete working solution

Presentation-ready documentation

Mentor sign-off

Portfolio-ready deliverable

Stack Full Curriculum Portfolio

All 3 projects go directly into your portfolio & resume — reviewed by mentors before you graduate.

See Sample Project Reports

Upcoming Batches

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Why Radical Technologies

Live Online Training

Highly practical oriented training
Installation support on your system
24/7 Email and Phone support
100% Placement Assistance
Global Certification Preparation
Trainer-Student Interactive Portal
Assignments and Projects by Mentors

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Live Classroom Training

Weekend / Weekdays / Morning / Evening batches
80:20 Practical and Theory ratio
Real-life Case Studies
Easy make-up for missed sessions
PSI | Kryterion | Redhat Test Centers
Lifetime Video Classroom Access (coming soon)
Resume Prep and Mock Interviews

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Self-Paced Training

Learn 300+ courses at your own time
50,000+ Satisfied Learners
Course Completion Certificate
Practical Labs available
Mentor Support available
Doubt Clearing Session available
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Global Certification

Radical Technologies is the leading IT certification institute in Pune, offering globally recognized certifications across various domains. With expert trainers and comprehensive materials, we ensure students gain in-depth knowledge and hands-on experience to excel in their careers. Our certification programs are tailored to meet industry standards — from cloud technologies to data science — empowering individuals to stay ahead in the ever-evolving tech landscape.

Career Services

At Radical Technologies, we are committed to your success beyond the classroom. Our 100% Job Assistance program ensures that you are not only equipped with industry-relevant skills but also guided through the job placement process. With personalised resume building, interview preparation, and access to our extensive network of hiring partners, we help you take the next step confidently into your IT career.