Real time industry trainers
90%+students receive placement assistance
MLOps & LLMOps Course in Marathahalli, Bangalore
Ship, Scale, and Monitor Production AI Systems with Hands-On MLOps.
Move machine learning and LLM models out of notebooks into production. Master DVC, MLflow, FastAPI, Docker, Kubernetes, CI/CD/CT pipelines, Terraform, AWS deployment, and drift monitoring through live labs.
Real time industry trainers- Certificate on completion
- Classroom + online available
- Flexible batch timings
10hands-on MLOps & LLMOps modules
15+years training IT professionals
5,000+students trained
Industry Recognition
Where MLOps & Production AI Skills Take You
Organizations across fintech, e-commerce, healthcare, SaaS startups, and Global Capability Centers (GCCs) no longer want ML models stuck in Jupyter notebooks. They hire MLOps engineers who can version datasets, package inference APIs, automate CI/CD/CT pipelines, scale containers on Kubernetes, and monitor live models and LLMs reliably.
Hired across every AI-adopting sector
BFSI, e-commerce, SaaS product firms, healthcare, and enterprise GCCs all require MLOps engineers to operationalize ML and Generative AI models reliably.
Bridges Data Science and Cloud DevOps
Combines model lifecycle management (DVC, MLflow, FastAPI) with cloud-native infrastructure (Docker, Kubernetes, Terraform, CI/CD), making you valuable to both AI and platform teams.
Covers modern LLMOps & RAG observability
Goes beyond classic Scikit-learn pipelines to include deploying, evaluating, and monitoring LLM/RAG services for latency, token cost, and hallucinations.
Salary data, sourced
Entry-level MLOps and ML deployment engineers in India typically start around ₹5 to 9 LPA, advancing to ₹12 to 18 LPA (1–2 yrs) and ₹18 to 28 LPA with 2–4 years of production experience (Glassdoor/Naukri).
Tools & technologiesWhat you'll work with
Every tool in this MLOps curriculum is mapped directly to production ML engineering and AI platform job descriptions in Bangalore and across India.
Course Curriculum
10-Module MLOps & LLMOps Syllabus — Module by Module
This MLOps training in Bangalore takes you from experiment tracking and API packaging to Kubernetes orchestration, cloud IaC, drift observability, and LLMOps across 10 hands-on modules.
MLOps Foundations & Reproducible ML Lifecycles
- Why Jupyter notebooks fail in production: reproducibility, dependency drift, and hidden technical debt
- End-to-end MLOps architecture: data pipelines, training pipelines, model registry, serving, and monitoring
- Structuring production Python ML repositories with clean environments and modular code
- Versioning large datasets and trained model artifacts with Git and DVC (Data Version Control)
Experiment Tracking & Model Registry with MLflow
- Setting up an MLflow tracking server with local and cloud artifact stores
- Logging hyperparameters, evaluation metrics, artifacts, and environment signatures automatically
- Comparing training runs and diagnosing overfitting or metric regressions
- Managing model versions and lifecycle stages (Staging, Production, Archived) in the MLflow Model Registry
Packaging & Serving ML Models as High-Performance APIs
- Serializing Scikit-learn, TensorFlow, and PyTorch models safely (ONNX, TorchScript, pickle/joblib trade-offs)
- Building asynchronous REST inference APIs using FastAPI and Pydantic schema validation
- Batch inference vs real-time online inference vs streaming patterns
- Testing latency, throughput, and API contracts with Postman and Swagger/OpenAPI
Containerizing Machine Learning Workloads with Docker
- Docker architecture for ML: images, layers, volumes, and dependency caching
- Writing lean multi-stage Dockerfiles for Python, Scikit-learn, and PyTorch inference services
- CPU vs GPU container base images and managing environment secrets securely
- Pushing versioned ML images to Docker Hub and AWS Elastic Container Registry (ECR)
Scaling & Orchestrating ML Services on Kubernetes
- Kubernetes architecture for AI workloads: Pods, Deployments, Services, ConfigMaps, and Secrets
- Deploying containerized FastAPI ML models onto a Kubernetes cluster
- Resource requests/limits, readiness/liveness probes, and Horizontal Pod Autoscaling (HPA) for traffic spikes
- Rolling updates, canary rollouts, and shadow deployment patterns for safer model releases
CI/CD & Continuous Training (CT) Pipelines for ML
- Designing CI/CD/CT workflows with GitHub Actions and Jenkins
- Automated code linting, unit tests, data schema validation, and model quality gates
- Triggering automated retraining pipelines when new labeled data arrives or performance drops
- Automated container build, registry push, and cluster deployment on merge
Workflow Orchestration & Feature Engineering Pipelines
- Orchestrating multi-step ETL and ML training DAGs using Apache Airflow
- Scheduling retries, backfills, and failure alerts for batch prediction workflows
- Understanding training-serving skew and feature store concepts for consistent online/offline features
Cloud ML Deployment (AWS) & Infrastructure as Code (Terraform)
- Deploying production ML microservices on AWS (EC2, S3, IAM, ECR, ECS/EKS, and Amazon SageMaker overview)
- Provisioning reproducible cloud storage, networking, and compute clusters using Terraform IaC
- Cost optimization strategies for GPU/CPU training and inference instances
Model Monitoring, Drift Detection & LLMOps
- Monitoring production API latency, error rates, and hardware utilization with Prometheus and Grafana
- Detecting data drift, covariate shift, and concept drift on live production traffic
- LLMOps fundamentals: deploying RAG/LLM endpoints, tracking prompt versions, token cost, latency, and hallucination guardrails
End-to-End Production MLOps Capstone & Interview Prep
- Capstone Project: take a real ML/LLM use case from raw data versioning (DVC) and MLflow tracking to a Dockerized FastAPI service deployed via CI/CD onto AWS/Kubernetes with live drift monitoring
- Architecture code review, GitHub portfolio documentation, and mock MLOps system-design interviews
Is this for you?
Who Should Join This MLOps Course
Data Scientists & ML EngineersProfessionals who build models in notebooks and want to deploy, scale, and monitor them in production.
DevOps, Cloud & SRE EngineersInfrastructure engineers looking to transition into high-demand AI Platform, MLOps, and LLMOps roles.
Software & Backend DevelopersPython or Java developers aiming to build and ship scalable ML/LLM microservices and AI pipelines.
Data EngineersPipeline engineers expanding from Spark/SQL/Airflow into end-to-end ML training and serving infrastructure.
CS/IT & AI GraduatesFreshers with Python fundamentals who want production deployment skills that stand out from notebook-only resumes.
Technical Consultants & FreelancersEngineers delivering production RAG, LLM, and predictive ML deployments for enterprise clients.Flexible Batch Options
Choose a Batch That Fits Your Schedule
Our MLOps training in Bangalore is available in weekday, weekend, and live online formats—all capped at 20 students with live cloud and cluster labs.
Ideal for freshers and full-time learners.
- 1 to 2 hrs/day · Mon to Fri
- Morning and evening slots
- 6 to 8 weeks total
- Max 20 students
Best suited for working developers, DS & DevOps pros.
- 2 to 3 hrs/session · Sat to Sun
- No clash with office hours
- 8 to 10 weekends total
- Max 20 students
Live online MLOps training across IndiaSame trainer, same 10-module curriculum, same batch cap (max 20). Zero pre-recorded videos: live Docker, Kubernetes, MLflow, and cloud deployment debugging in every session.
Learn MLOps from Working Production AI & Platform Engineers
MNP's MLOps trainers bring 10 to 15+ years of combined hands-on experience across machine learning deployment, Kubernetes infrastructure, cloud automation, and LLMOps.
- Principal MLOps Architect12+ years designing cloud-native ML platforms, model registries, and automated retraining pipelines
- Kubernetes & Cloud Infrastructure LeadExpert in Docker, EKS/Kubernetes autoscaling, Terraform IaC, and CI/CD automation on AWS
- Applied AI & LLMOps SpecialistSpecializes in FastAPI model serving, RAG/LLM observability, drift detection, and latency optimization
Turn Your MLOps Skills Into High-Growth AI & Platform Roles
MNP reports a 90%+ placement assistance rate (structured career support, not a job guarantee). Support includes:
- Resume building with MLOps, AI Platform, and Cloud ML Engineer templates
- GitHub portfolio review showcasing deployed Docker, Kubernetes, MLflow, and CI/CD pipelines
- Mock technical and ML system-design interviews with real industry scenarios
- LinkedIn profile optimization for MLOps and LLMOps recruiter searches
- Direct referrals to hiring partners and weekly job alerts for Bangalore and pan-India roles
- Offer letter and salary negotiation guidance
Student success stories
What Our Cloud, DevOps & AI Students Say
Real students who trained at MNP Technologies and transitioned into cloud, DevOps, data, and enterprise IT roles.
Anjali Sahu
Alchemy Techsol India
Thanks to the hands-on SAP FICO training and incredible placement support at MNP Technologies. This comprehensive program gave me the exact skills and confidence I needed to successfully land my dream career.
Bharath J
Sumadhura
The SAP MM training at MNP Technologies helped me gain practical knowledge, build confidence and secure a great opportunity. I’m grateful for the trainers and placement support.
Ashlesha Reddy
CSS Corp Pvt Ltd
Enrolling in MNP Technologies for AWS & DevOps was the best decision for my career, thanks to the incredibly supportive trainers who went above and beyond to solve every technical problem.
Durga Bhavani
Sumadhura
MNP’s live training and expert mentors gave me the practical SAP FICO skills needed to stand out. Thanks to their dedicated placement support and mock interviews, I easily secured my breakthrough corporate role!
G Manisha Naidu
BSOL System (P.V.T)
The AWS & DevOps training at MNP Technologies gave me the hands-on experience and confidence I needed to grow in my career. Thank you for the great support.
Mohan Rama Chandrareddy
Sumadhura
The live project training in SAP FICO at MNP Technologies gave me a massive corporate advantage and hands-on confidence. I am incredibly thankful to MNP and their brilliant placement team for helping me.
Nadigatla Sai Praveen
Sumadhura
The live training and case studies at MNP Technologies made complex SAP MM modules incredibly easy to master. Thanks to their consistent mock interviews and dedicated placement support, I built the confidence needed to clear my corporate selection process smoothly!
Syed Junaid Ahammad
Sumadhura
MNP’s live training and hands-on projects gave me a deep, practical understanding of SAP MM. Their dedicated placement support and mock interview sessions were excellent, helping me confidently clear my rounds and secure my job!
Monika
Course: AWS & DevOps
Enrolling in the AWS & DevOps course at MNP Technologies was a game-changer for me. The hands-on training with real-time scenarios gave me the exact technical skills and confidence I needed to transition into the cloud industry.
Philips
Course: AWS & DevOps
Thanks to the hands-on AWS & DevOps training and mentorship I received, I gained the confidence and real-world skills needed to successfully transition into a cloud and DevOps engineering role.
Sweta
Deloitte · AWS & DevOps
The practical exposure I got during my AWS & DevOps training at MNP Technologies was crucial in helping me step confidently into my role at Deloitte. The trainers focus on real-world enterprise scenarios that make all the difference.
Yogireddy
Course: AWS & DevOps
MNP Technologies doesn't just teach AWS & DevOps; they make you career-ready. Grateful to the entire team for the excellent mentorship and continuous support throughout my job hunt!
Madhusudan
Course: SAP FICO
MNP Technologies doesn't just teach SAP FICO; they make you career-ready. Grateful to the entire team for the excellent mentorship and continuous support throughout my job hunt!
Musthak
Course: DevOps
Markappa
Course: SAP MM
Frequently Asked Questions
What is MLOps and how is it different from DevOps?
DevOps automates building, testing, and deploying traditional software code. MLOps extends DevOps principles to machine learning and Generative AI—adding data versioning (DVC), experiment tracking (MLflow), Continuous Training (CT), GPU/CPU model serving, and live data/concept drift monitoring.
What prerequisites do I need to join this MLOps course?
Basic familiarity with Python programming and Linux command-line basics is recommended. Whether you come from a Data Science/ML background (needing Docker, Kubernetes, CI/CD, and AWS) or a DevOps/Developer background (needing MLflow, model serving, and drift monitoring), Module 1 bridges the gap step by step.
Does this course cover LLMOps and Generative AI deployment?
Yes. Module 9 covers LLMOps fundamentals including serving LLM and RAG endpoints, prompt/version tracking, token and latency monitoring, and guardrails against model hallucinations.
What tools will I use hands-on during the labs?
You will practice live with Python, Git, DVC, MLflow, FastAPI, Docker, Kubernetes, GitHub Actions, Jenkins, Apache Airflow, Terraform, AWS cloud services, Prometheus, and Grafana.
What job roles can I apply for after completing MLOps training?
Graduates target MLOps Engineer, AI Deployment Engineer, Machine Learning Platform Engineer, LLMOps Engineer, and Cloud/DevOps Engineer for AI roles across startups, IT consultancies, and GCCs.
Is MLOps training available online outside Bangalore?
Yes. Classroom batches run at our Marathahalli, Bangalore center, and live interactive online batches are available across India with the exact same trainer, live lab debugging, and 20-student batch cap.
How can I check MLOps course fees and attend a free demo?
Course fees depend on your chosen weekday, weekend, or live-online batch format, with EMI options available. Book a free demo via our contact page or call +91 72599 12345.
Ready to Build Production-Grade MLOps & LLMOps Pipelines?
Weekday, Weekend, and Live Online batches available—classroom in Marathahalli, Bangalore and live online across India. Book a free demo or call +91 72599 12345.
