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MLOPS COURSE IN BANGALORE

Advance your ML career with Bangalore’s most industry-focused MLOps Course. Learn to build reproducible ML pipelines, automate CI/CD workflows, and deploy models at scale using AWS, Azure, and GCP. Designed for freshers and working professionals, the program includes certification and placement support with top AI and data teams in Bengaluru.

4.9★
Average Rating
1000+
Students Placed
150+
Hiring Partners
100%
Placement Support

Perks of Joining Our MLOps Course in Bangalore

250+

Professionals in Bangalore
upskilled in MLOps

Evening & Weekend

Flexible live online batches
designed for working professionals

8+ Projects

Hands-on builds with MLflow, Kubeflow, Airflow, CI/CD & monitoring

Placement Assistance

Mock interviews, resume building
and recruiter referrals in Bangalore

Learning Format
Live Online Bootcamp
Course Duration
4.5 months (approx. 18 weeks)
Next Cohort starts
02 Mar, 2026

Highlights of Our MLOps Course in Bangalore

Hands-on MLOps Projects

Implement tracking with MLflow, orchestrate pipelines with Airflow/Kubeflow, and deploy with Docker & Kubernetes.

Cloud-Native Deployments

Practice on AWS SageMaker, Azure ML, and GCP Vertex AI with IaC patterns for real infra.

Bangalore Hiring Network

Alignment with roles at Infosys, Wipro, TCS, Flipkart, Swiggy, Razorpay, Meesho, and AI platform teams.

24/7 Learning Support

Get timely help from mentors and a dedicated support team throughout your builds.

Main Course Page

Check Out the Full MLOps Course Curriculum

This Bangalore page gives you a local view. Explore the complete MLOps curriculum, tools, projects, and certifications on our main course page.

No signup required — explore at your own pace

What you'll find on the main page

Full Curriculum

Deep-dive modules covering every topic end to end

Hands-on Projects

Build portfolio-ready apps with real-world datasets

Certification

Industry-recognized certificate on completion

Career Support

Placement prep, mock interviews and resume reviews

Fee and EMI Plans

Transparent pricing with flexible payment options

4.9 Rated

Top-rated by 340+ working professionals across India

Industry-Recognized CertificationLive Mentor-Led SessionsPlacement Assistance4.9 ★ Average Rating

Why Learn MLOps in Bengaluru’s Product & Platform Scene?

Bengaluru teams need engineers who can move models from notebooks to real users. This MLOps course bridges data science and DevOps so you can ship reliable, monitored ML at scale.

From unicorns to global captives, Bengaluru companies expect automated pipelines, robust model serving, observability, and cost control. Learn the exact toolchain—MLflow, Airflow/Kubeflow, Docker, Kubernetes, AWS SageMaker, Azure ML, and GCP Vertex AI—guided by engineers who run these stacks in production.

What Makes Our MLOps Course in Bangalore Different

Compare our production-focused MLOps training with typical ML courses in Bengaluru

Feature
Learning Format
School of Core AI

Live online with mentor code reviews & project clinics

Other Institutes

Recorded videos with limited interaction

Feature
Curriculum Depth
School of Core AI

Covers pipelines, serving, cloud, observability & governance

Other Institutes

Focus on modeling only—no production coverage

Feature
Projects & Case Studies
School of Core AI

8+ real projects including CI/CD, K8s, and canary rollouts

Other Institutes

Mini-projects not aligned to platform realities

Feature
Cloud Coverage
School of Core AI

AWS SageMaker, Azure ML, GCP Vertex AI hands-on

Other Institutes

Single-cloud demos only

Feature
Placement Support
School of Core AI

Bengaluru-focused referrals & mock panels

Other Institutes

Generic job portal guidance

Feature
Certification
School of Core AI

Industry-recognized MLOps certificate

Other Institutes

Basic completion certificate

Placement-Focused MLOps Training (Live Online)

01

Expert-Led Sessions

Learn directly from mentors who design and operate ML platforms at Bengaluru’s leading product, fintech, and SaaS firms.

02

Built for Working Pros & Career Switchers

Evening and weekend-friendly schedules designed for data scientists, ML engineers, DevOps/SREs, and software developers transitioning into MLOps.

03

Real-World Assignments

Hands-on work with versioned datasets, Airflow DAGs, CI/CD pipelines, containerized model serving, and blue/green rollout strategies.

04

1:1 Mentor Hours & Doubt Clearing

Get unstuck quickly with scheduled one-on-one mentor sessions, detailed code reviews, and active Slack/forum support.

05

AI-Focused Mock Interviews

Prepare with 10+ mock rounds that simulate real hiring panels—covering platform design, failure scenarios, and cost vs SLA trade-offs.

06

Career & Placement Support

Receive personalized resume feedback, portfolio reviews, and recruiter referrals targeted at Bengaluru’s MLOps and AI hiring market.

MLOps Course in Bangalore

Build, Ship, and Scale ML in Production — The Bengaluru Way

Master the full MLOps lifecycle with tools like MLflow, Airflow, Kubeflow, Docker, Kubernetes, AWS SageMaker, Azure ML, and GCP Vertex AI. Designed for Bengaluru’s product, fintech, and AI platform teams that demand production-ready ML pipelines.

What You’ll Learn in the MLOps Course

01

Pipelines & Experimentation

Master experiment tracking with MLflow, reproducibility, data/version control, and pipeline orchestration using Airflow and Kubeflow.

02

Serving & Scaling

Package models with Docker, serve with TorchServe or Ray Serve, and scale using Kubernetes/Helm with canary and blue-green rollout strategies.

03

Cloud MLOps

Deploy end-to-end ML systems on AWS SageMaker, Azure ML, and GCP Vertex AI. Implement infra-as-code, manage secrets, roles, and policies securely.

04

Observability & Governance

Set up monitoring with Prometheus/Grafana, detect data & concept drift, ensure fairness & lineage tracking, and apply cost-control mechanisms.

MLOps Learning Roadmap: From Zero to Production

1

Foundation

  • Core concepts
  • Tools & setup
  • Hands-on intro
2

Build

  • Advanced techniques
  • Guided projects
  • Industry tools
3

Specialise

  • Elective tracks
  • Capstone project
  • Peer reviews
4

Launch

  • Portfolio prep
  • Mock interviews
  • Placement drive

Skills You Will Command

MLflow & Experiment TrackingAirflow & Kubeflow PipelinesDocker, Kubernetes & HelmAWS SageMaker, Azure ML, Vertex AITorchServe, Ray Serve, FastAPICI/CD with Jenkins & GitHub ActionsMonitoring: Prometheus & GrafanaData & Model Drift, LineageSecurity, Secrets, IAM & Cost Control

Industry-Recognized MLOps Certification (Bengaluru)

Complete the program and earn an industry-recognized certificate that validates your ability to ship reliable ML to production—trusted by hiring teams across Bengaluru.

Certificate of Completion

Issued by School of Core AI upon successful completion of the programme

Top Companies Hiring for MLOps in Bangalore

Flipkart – Data platform & ML ops
Swiggy – Real-time ML pipelines
Razorpay – Fintech AI infra teams
Meesho – Recommender & ML ops
Infosys – Global MLOps practices
Wipro AI Labs – Cloud AI ops
TCS – Enterprise ML pipelines
Accenture Bengaluru – AI ops hiring
CRED – Data & model ops
BYJU’S – AI-driven learning systems
Microsoft India – Azure ML ops
Google Bengaluru – Cloud AI teams
Amazon Bengaluru – AWS SageMaker ops
PhonePe – Payment AI infra
IBM Research – Enterprise AI platforms

Career Opportunities in MLOps – Bangalore

MLOps roles are among the fastest-growing in Bengaluru as teams productize ML at scale.

Roles
  • MLOps Engineer 28–32%
  • Machine Learning Engineer 22–26%
  • Data Scientist 18–22%
  • AI Platform Engineer 12–16%
  • Cloud ML Engineer 8–12%
MLOps Engineer

Design CI/CD pipelines, containerize models, automate deployments, and monitor SLAs. Share: 28–32%, Median CTC: ₹18–30 LPA.

Machine Learning Engineer

Train/optimize models and integrate them with production services and feature stores. Share: 22–26%, Median CTC: ₹15–25 LPA.

AI Platform Engineer

Scale platform primitives—feature stores, registries, infra APIs—for thousands of ML users. Share: 12–16%, Median CTC: ₹20–32 LPA.

In-Demand MLOps Skills in Bangalore

What Bengaluru hiring managers expect from modern MLOps engineers.

1
MLflow tracking, model registry & artifacts
2
Airflow/Kubeflow orchestration & retries
3
Docker images, K8s deployments, Helm charts
4
AWS SageMaker, Azure ML, Vertex AI patterns
5
TorchServe/Ray Serve, REST & gRPC serving
6
Drift detection, data contracts & lineage
7
Prometheus/Grafana monitoring & alerting
8
Secrets, IAM, policies & supply-chain security
9
FinOps for ML — optimize cloud cost & latency

Explore Related Tracks

Upskill across adjacent domains to grow into platform leadership roles.

Our Vibrant Student Community

Bengaluru learners collaborate on capstones, share interview tips, and review pipelines together—accelerating growth into platform teams.

Benefits of Our MLOps Community

Code Reviews & Pairing
Code Reviews & Pairing
Access to Templates & IaC
Access to Templates & IaC
Hiring Referrals & Mock Panels
Hiring Referrals & Mock Panels
Mentorship from Platform Engineers
Mentorship from Platform Engineers
Career Growth Playbooks
Career Growth Playbooks
Alumni work in Bengaluru’s product, fintech, and platform teams across unicorns and global captives.

Our Alumni Network

MLOps Course Fees

Single transparent fee covering complete training, real-world projects, certification and placement support. EMI and part-payment options are available with our counsellor.

Total Course Fee
60,000

Final fee, EMI plans and any ongoing offers will be confirmed by your counsellor based on your batch, mode and payment preference.

Your Career Path after MLOps

AI Platform Engineer

Scale infra & tooling

LLMOps Specialist

Productionize LLMs

Engineering Manager (AI)

Lead ML Platform teams

Testimonials of Our Successful Learners

Came in as a data analyst. Left with a production-ready ML pipeline and a new job title. The Airflow + MLflow stack prepared me for real Bengaluru interviews.
HN
Harshitha N.
MLOps Engineer
Exactly the bridge I needed from ML modeling to deployment. The Kubernetes, Helm, and monitoring parts were gold.
AR
Akash R.
ML Engineer → Platform
Hands-on exposure to SageMaker and Azure ML helped me align with my company’s stack. Landed an internal move in 4 months.
SK
Suhani K.
Cloud ML Engineer
Loved the mock interviews—deep questions on failure modes, rollbacks, and cost control. Felt just like Bengaluru product teams.
RP
Rohit P.
AI Platform Engineer
The capstone used MLflow, Airflow, and TorchServe end-to-end. That portfolio piece got me shortlisted at two fintechs.
VG
Vikas G.
MLOps Associate
I’m from a DevOps background. This course helped me translate my skills to ML platforms with confidence.
TS
Tanvi S.
DevOps → MLOps

Frequently Asked Questions

The program runs for ~4.5 months with live online classes, projects, and mock interviews scheduled for working professionals.