Production, Not Notebooks
Learn to take models from experiments to reliable, monitored production systems.
Next BatchUpcoming WeekendMaster MLOps: Build & DeployProduction-Ready AI Systems
Go beyond training models. Learn to design, deploy, monitor, and scale ML systems with CI/CD, Docker, Kubernetes, LLMOps, and GenAI.
Enroll Now - ₹39,999
- 6 Months Duration
6+ Projects Portfolio- 17+ Tools Industry
- 100% Live Sessions
About the ProgramWhat is This MLOps Course?
The Full MLOps Stack
Docker, Kubernetes, MLflow, DVC, GitHub Actions, Terraform, and cloud ML platforms.
LLMOps & GenAI
Deploy RAG pipelines, fine-tuned models, and agents with proper evaluation and monitoring.
Real Deployments
Build end-to-end pipelines and a production-scale capstone you can show employers.
The ProblemWhy most AI courses don't work
The market is flooded with certificates that don't lead to jobs. We built Mindgorithm to fix what's broken.
90% of Models Never Ship
Most ML models fail to reach production. MLOps skills bridge that gap.
Up to ₹40 LPA Salary
ML Engineers command the highest premiums in the AI job market.
Automate the ML Lifecycle
CI/CD, versioning, and monitoring are how top AI teams actually ship.
Every Industry Is Hiring
Banking, healthcare, retail, legal: every sector needs people who build with AI.
The gap isn't knowledge, it's applied skill. Those who can actually build MLOps systems will command the biggest career advantage of this decade.
Ready to
future-proof your career?
Join 500+ professionals who've chosen practical AI training.
Who Should JoinIs This MLOps Course Right for You?
This program is designed for anyone ready to build real AI skills regardless of their technical background.
Data
Scientists
Take your models out of notebooks and into reliable production systems.
DevOps
Engineers
Apply your infrastructure skills to the fast-growing ML platform space.
Software
Developers
Learn to build, deploy, and operate ML-powered services end to end.
Career
Switchers
Move into one of the best-paid engineering roles in AI.
Eligibility: Bachelor's degree with min. 50% • Coding experience NOT required
CurriculumMLOps Course Curriculum
10 Modules, 6+ Projects
Master MLOps with hands-on training in CI/CD for ML, Docker, Kubernetes, LLMOps & GenAI deployment. Job-ready MLOps certification with real-world projects.
Python & ML Foundations for Production
Build the foundation — Python and essential ML concepts before diving into deployment
- Python for Data Science — NumPy, Pandas, Matplotlib
- Version control with Git — branching, pull requests, and collaboration workflows
- Core ML concepts — supervised/unsupervised learning, evaluation metrics
- Writing clean, modular, production-ready code — not notebook spaghetti
- Setting up reproducible development environments
ML Systems Design & Architecture
Learn to think in systems, not models — design end-to-end ML architectures
- End-to-end ML architectures: data ingestion, feature stores, training pipelines
- Serving infrastructure and feedback loops for continuous improvement
- How Netflix, Uber, and Spotify design their ML platforms
- Feature stores — online vs offline, consistency, and freshness guarantees
- System design patterns for scalable ML — batch vs real-time serving
Data & Model Versioning
Track every dataset, every model, every experiment — reproducibly
- DVC for data versioning — track large files without bloating your repo
- Model registries — promote models from staging to production safely
- Artifact management so you never lose track of what worked and why
- Lineage tracking — trace any prediction back to its training data and code
- Integrating DVC with GitHub Actions for automated versioning
Experiment Tracking & Reproducibility
Run hundreds of experiments without losing your mind
- MLflow for experiment tracking — logging runs, metrics, parameters, and artifacts
- Weights & Biases for advanced visualization and team collaboration
- Comparing runs and making experiment-driven decisions at scale
- Automating hyperparameter sweeps — grid search, random search, Bayesian optimization
- Building experiment dashboards for stakeholder reporting
CI/CD Pipelines for Machine Learning
Automate the path from code commit to deployed model
- GitHub Actions workflows for ML — test, validate, retrain, deploy automatically
- Data validation with Great Expectations — catch bad data before training
- Model validation gates — performance thresholds before any promotion
- CML (Continuous Machine Learning) for reporting metrics in pull requests
- Jenkins pipelines for enterprise ML deployment workflows
Docker & Kubernetes for ML
Containerize your ML models and orchestrate them at scale
- Writing production Dockerfiles for ML — handling dependencies and GPU support
- Kubernetes fundamentals — pods, deployments, services, and ingress
- Managing GPU workloads on Kubernetes for training and inference
- Helm charts for reproducible ML deployments
- KServe for standardized model serving on Kubernetes with autoscaling
Cloud Deployment — AWS, GCP & Azure
Deploy ML models to the three major clouds with confidence
- AWS SageMaker — managed training, endpoints, and pipelines
- GCP Vertex AI — AutoML, custom training, and model registry
- Azure ML — experiment tracking, pipelines, and managed endpoints
- Serverless inference with Lambda and Cloud Functions for cost optimization
- Multi-cloud strategies, Terraform IaC, and ML infrastructure cost management
Model Monitoring, Drift & Observability
Shipping is only half the job — keep your models healthy in production
- Data drift detection — statistical tests, PSI, and KL divergence
- Model degradation monitoring — tracking prediction quality over time
- Building Grafana dashboards for real-time model observability
- Automated alerting with Prometheus and PagerDuty integration
- Retraining triggers — when and how to automatically retrain your models
GenAI & LLMOps — RAG, Fine-Tuning & Agents
The frontier of MLOps — this module alone puts you ahead of 90% of courses
- Building and deploying RAG pipelines — vector databases, retrieval, and reranking
- Fine-tuning open-source LLMs with LoRA and QLoRA on custom datasets
- AI agent orchestration — multi-step reasoning, tool use, and memory systems
- Prompt versioning and management — LangSmith, PromptLayer, and custom solutions
- Production guardrails — content safety, hallucination detection, and rate limiting
Production-Scale AI Systems — Capstone
Bring it all together — design, build, and deploy an end-to-end production ML system
- Full system design from data ingestion to user-facing predictions
- CI/CD pipeline with automated testing, validation, and deployment
- Production monitoring, alerting, and auto-scaling configuration
- Portfolio presentation — live demo, technical write-up, and GitHub README
- Mock interview prep — walking through your system design with mentors
Tools You'll Master
Docker
Kubernetes
MLflow
DVC
GitHub Actions
Terraform
AWS SageMaker
GCP Vertex AI
Azure ML
Prometheus
Grafana
LangChain
FastAPIPortfolio ProjectsYou Will Build 6+ Real AI Applications
Not toy examples — applications you can deploy and show employers.
End-to-End ML Pipeline with CI/CD
Build a fully automated ML pipeline: data ingestion → training → validation → deployment → monitoring. Trigger retraining on data drift. Ship to cloud with GitHub Actions.
- MLflow
- GitHub Actions
- Docker
- AWS
LLM-Powered App — Deployed & Monitored
Deploy a RAG-based Q&A application with vector search, prompt management, and cost monitoring. Add guardrails and evaluation metrics for production safety.
- LangChain
- Pinecone
- FastAPI
- vLLM
Real-Time Fraud Detection System
Build a streaming fraud detection pipeline that processes transactions in real time, serves predictions via API, and alerts on anomalies — with full observability.
- Kafka
- Kubernetes
- Prometheus
- Grafana
Recommendation Engine in Production
Deploy a recommendation system with feature stores, A/B testing infrastructure, and model versioning. Handle cold-start, real-time updates, and scaling to millions of users.
- Feast
- Redis
- A/B Testing
- GCP
Model Monitoring & Alerting Dashboard
Create a production monitoring system that tracks prediction quality, detects data drift, and triggers automated retraining — with a Grafana dashboard for observability.
- Evidently
- Grafana
- Prometheus
- Slack Alerts
AI Agent Orchestration System
Build and deploy an autonomous AI agent with tool-use, memory, and multi-step reasoning. Includes prompt versioning, evaluation harness, and production guardrails.
- LangGraph
- Vector DB
- Docker
- Guardrails
Build these
projects and more
Graduate with a portfolio that proves your skills — not just a certificate.
Career OutcomesWhere This MLOps Course Takes You
MLOps skills are in explosive demand. Here are the roles you'll be qualified for.
- ₹15–35 LPA
MLOps Engineer
US: $130K–$190K
- ₹18–40 LPA
ML Engineer
US: $140K–$210K
- ₹16–38 LPA
AI Engineer
US: $135K–$200K
- ₹14–30 LPA
Data Platform Engineer
US: $120K–$175K
Salary ranges are estimates and vary by experience and company.
₹39,999
- Live Saturday & Sunday classes
- Batch starts: Upcoming Weekend
- Duration: 6 months • 100% Live
- Lifetime curriculum updates included
Small Batch Size
Only 50 seats per batch. Personalized instructor support.
Direct Instructor Access
WhatsApp group for quick doubt resolution, answered in hours.
Lifetime Updates
Get all future curriculum updates at no extra cost.
Certificate of Completion
Showcase your skills on LinkedIn and your resume.
Alumni Network Access
Join a growing community of AI practitioners across India.
Frequently Asked QuestionsQuestions? We've got answers.
MLOps (Machine Learning Operations) is the practice of deploying, monitoring, and maintaining machine learning models in production environments. It combines ML, DevOps, and data engineering to automate the entire ML lifecycle. With 90% of ML models failing to reach production, MLOps skills bridge the critical gap between building models and running them reliably at scale — making it one of the most sought-after skill sets in AI today.
MLOps builds on existing programming and ML knowledge. If you are comfortable with Python and understand basic ML concepts, you can become job-ready in MLOps within 5–6 months of structured, hands-on training. The key is practical experience with tools like Docker, Kubernetes, CI/CD pipelines, and cloud platforms — which is exactly what this course focuses on from Day 1.
Yes, basic Python programming experience is recommended. You should be comfortable writing scripts and working with libraries like Pandas and NumPy. Prior ML experience is helpful but not mandatory — Module 1 covers the ML foundations needed before moving into deployment topics. If you can write a Python function and use the command line, you are ready.
The course is designed to be completed in 5–6 months at a pace of 10–15 hours per week. Every module includes hands-on labs and projects, so you will be building and deploying throughout — not just watching videos. By the end, you will have a production-grade portfolio ready for job applications.
This course prepares you for roles like MLOps Engineer, ML Engineer, AI Engineer, Data Platform Engineer, and ML Infrastructure Engineer. These are among the highest-paying roles in tech — ranging from ₹15–40 LPA in India and $120K–$200K+ in the US. Companies across industries are hiring for these skills, from tech giants to fintech startups to healthcare companies.
Three key differences: First, our curriculum is more production-focused — you deploy in every module, not just the capstone. Second, we cover LLMOps and GenAI deployment in depth, which most academic programs are still adding. Third, pricing — our course costs a fraction of the ₹3–4 Lakh that IISc and BITS programs charge, with no compromise on depth or mentorship quality. We focus on skills that get you hired, not brand prestige.
Yes. The course includes dedicated placement assistance: resume reviews, mock interviews, portfolio optimization, and introductions to hiring partners. More importantly, the portfolio of 6+ deployed projects you build during the course is your strongest asset — it demonstrates real skills far more effectively than any certificate alone.
You will work with the modern MLOps stack: Docker, Kubernetes, MLflow, DVC, GitHub Actions, Terraform, AWS SageMaker, GCP Vertex AI, Evidently AI, Prometheus, Grafana, LangChain, LlamaIndex, vLLM, FastAPI, and more. Every tool is taught through hands-on projects — you will not just learn about them, you will build production systems with them.
Ready to
Master MLOps?
Join the next batch. Learn by building. Walk away with skills that matter.