--- title: "MLOps Training in Bangalore: Course, Labs, Jobs | MNP" url: https://mnptechnologies.com/mlops-training-bangalore/ description: "Join MNP MLOps course in Marathahalli, Bangalore and online. Master MLflow, FastAPI, Docker, Kubernetes, CI/CD, AWS and LLMOps with live labs and placement." ---# 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.[Book a Free Demo](https://mnptechnologies.com/contact-us/)[View Full Syllabus](https://mnptechnologies.com/mlops-training-bangalore/#syllabus)- Real time industry trainers - Certificate on completion - Classroom + online available - Flexible batch timings**90%+** students receive placement assistance**10** hands-on MLOps & LLMOps modules**15+** years training IT professionals**5,000+** students trainedIndustry Recognition## Where MLOps & Production AI Skills Take YouOrganizations 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 sectorBFSI, 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 DevOpsCombines 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 observabilityGoes beyond classic Scikit-learn pipelines to include deploying, evaluating, and monitoring LLM/RAG services for latency, token cost, and hallucinations.### Salary data, sourcedEntry-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 & technologies## What you'll work withEvery tool in this MLOps curriculum is mapped directly to production ML engineering and AI platform job descriptions in Bangalore and across India.Python, Scikit-learn & PyTorch Model ArtifactsGit & DVC (Data & Model Versioning)MLflow (Experiment Tracking & Model Registry)FastAPI, Pydantic & REST Inference MicroservicesDocker & Multi-Stage ML Container ImagesKubernetes (Deployments, Services & Autoscaling)GitHub Actions & Jenkins CI/CD/CT PipelinesApache Airflow Workflow OrchestrationAWS Cloud (EC2, S3, ECR, EKS & SageMaker Overview) + Terraform IaCPrometheus, Grafana, Drift Detection & LLMOps ObservabilityCourse Curriculum## 10-Module MLOps & LLMOps Syllabus — Module by ModuleThis 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.**Module 1**### 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)**Module 2**### 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**Module 3**### 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**Module 4**### 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)**Module 5**### 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**Module 6**### 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**Module 7**### 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**Module 8**### 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**Module 9**### 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**Module 10**### 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 interviewsIs this for you?## Who Should Join This MLOps Course**Data Scientists & ML Engineers** Professionals who build models in notebooks and want to deploy, scale, and monitor them in production.**DevOps, Cloud & SRE Engineers** Infrastructure engineers looking to transition into high-demand AI Platform, MLOps, and LLMOps roles.**Software & Backend Developers** Python or Java developers aiming to build and ship scalable ML/LLM microservices and AI pipelines.**Data Engineers** Pipeline engineers expanding from Spark/SQL/Airflow into end-to-end ML training and serving infrastructure.**CS/IT & AI Graduates** Freshers with Python fundamentals who want production deployment skills that stand out from notebook-only resumes.**Technical Consultants & Freelancers** Engineers delivering production RAG, LLM, and predictive ML deployments for enterprise clients.Flexible Batch Options## Choose a Batch That Fits Your ScheduleOur MLOps training in Bangalore is available in weekday, weekend, and live online formats—all capped at 20 students with live cloud and cluster labs.**Weekday Batch**### 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**Weekend Batch**### 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 India** Same 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.Your mentors## Learn MLOps from Working Production AI & Platform EngineersMNP'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 Architect** 12+ years designing cloud-native ML platforms, model registries, and automated retraining pipelines - **Kubernetes & Cloud Infrastructure Lead** Expert in Docker, EKS/Kubernetes autoscaling, Terraform IaC, and CI/CD automation on AWS - **Applied AI & LLMOps Specialist** Specializes in FastAPI model serving, RAG/LLM observability, drift detection, and latency optimizationPlacement outcomes## Turn Your MLOps Skills Into High-Growth AI & Platform RolesMNP 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 guidanceStudent success stories## What Our Cloud, DevOps & AI Students SayReal students who trained at MNP Technologies and transitioned into cloud, DevOps, data, and enterprise IT roles.### Anjali SahuAlchemy Techsol IndiaThanks 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 JSumadhuraThe **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 ReddyCSS Corp Pvt LtdEnrolling 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 BhavaniSumadhuraMNP’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 NaiduBSOL 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 ChandrareddySumadhuraThe 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 PraveenSumadhuraThe 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 AhammadSumadhuraMNP’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!### MonikaCourse: AWS & DevOpsEnrolling 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.### PhilipsCourse: AWS & DevOpsThanks 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.### SwetaDeloitte · AWS & DevOpsThe 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.### YogireddyCourse: AWS & DevOpsMNP 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!### MadhusudanCourse: SAP FICOMNP 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!### MusthakCourse: DevOps### MarkappaCourse: SAP MM[View All Reviews](https://mnptechnologies.com/testimonials/)## 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.[Book a Free Demo](https://mnptechnologies.com/contact-us/)