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MLOps & LLMOps · New program

MLOps Engineering: Notebook to ProductionMLOps course in Chennai · classroom & live online

Take machine learning from notebook to production with pipelines, serving and monitoring.

12 WeeksIntermediateProgramClassroom + Online⚡ Fast-track available
Request syllabus →Interview prep →
9modules
46lessons
9hands-on labs
98hours

What you'll learn in this MLOps course

Write clean, working Python programs
Collaborate on code with Git like a professional team
Package and run any app in containers
Build and evaluate machine learning models
Take models from notebook to production
Deploy and run applications on Kubernetes
Build reliable CI/CD pipelines
Run LLM applications reliably in production

Who this MLOps course is for

🛠️

IT professionals

Move up from support or operations roles.

💻

Developers & engineers

Add the next skill your team needs.

🎓

Graduates with basics

Turn foundations into job-ready skills.

✓ Basic IT or programming knowledge✓ Comfortable with computers and the command line✓ A laptop for hands-on labs

New to MLOps? Our advisors can suggest the best starting point for your background.

What you'll build

MLOps course syllabus

9 modules · 46 lessons · 9 major labs · 98 hours

01

Python fundamentals

6 lessons · 1 lab
12 hrs+
  1. Syntax, variables and data types
  2. Control flow and functions
  3. Lists, dicts and comprehensions
  4. Files, modules and packages
  5. Errors and exceptions
  6. Virtual environments and pip
HANDS-ON LAB

Build a command-line tool that cleans and summarises CSV data

02

Git & GitHub

5 lessons · 1 lab
6 hrs+
  1. Repositories, commits and history
  2. Branching and merging
  3. Pull requests and code review
  4. Resolving merge conflicts
  5. GitHub workflows for teams
HANDS-ON LAB

Run a team pull-request workflow end to end

03

Docker & containers

5 lessons · 1 lab
10 hrs+
  1. Images and Dockerfiles
  2. Volumes and networks
  3. Docker Compose
  4. Registries
  5. Image hardening and Podman
HANDS-ON LAB

Containerise a three-service application

04

Machine learning

5 lessons · 1 lab
14 hrs+
  1. Supervised and unsupervised learning
  2. Regression and classification
  3. Model evaluation
  4. Feature engineering
  5. scikit-learn pipelines
HANDS-ON LAB

Customer churn prediction model

05

MLOps

5 lessons · 1 lab
14 hrs+
  1. Experiment tracking with MLflow
  2. Training pipelines
  3. Model registry
  4. Model serving with FastAPI and KServe
  5. Monitoring and drift
HANDS-ON LAB

Train-to-serve pipeline with automatic retraining

06

Kubernetes core

6 lessons · 1 lab
16 hrs+
  1. Cluster architecture
  2. Pods, Deployments and Services
  3. ConfigMaps and Secrets
  4. Storage and StatefulSets
  5. Ingress and networking
  6. Helm charts
HANDS-ON LAB

Deploy a production-style app with Helm

07

CI/CD pipelines

5 lessons · 1 lab
12 hrs+
  1. GitHub Actions
  2. Jenkins pipelines
  3. Artifacts and versioning
  4. Environments and approvals
  5. Rollbacks and release strategies
HANDS-ON LAB

Pipeline that tests, builds and deploys on every push

08

LLMOps

5 lessons · 1 lab
8 hrs+
  1. Prompt versioning
  2. Evaluation suites
  3. Tracing and observability
  4. Guardrails
  5. Cost control
HANDS-ON LAB

Evaluation harness that scores every release

09

Career launch

4 lessons · 1 lab
6 hrs+
  1. Resume and LinkedIn that get shortlisted
  2. Building your project portfolio
  3. Interview preparation and mock rounds
  4. Job search strategy
HANDS-ON LAB

Mock technical + HR interview with feedback

Your 12 weeks journey

WEEK 1Python fundamentals
WEEK 2Git & GitHub
WEEK 3Docker & containers
WEEKS 4–5Machine learning
WEEKS 6–7MLOps
WEEKS 8–9Kubernetes core
WEEK 10CI/CD pipelines
WEEK 11LLMOps
WEEK 12Career launch

MLOps tools you'll use

PythonVS CodeGitGitHubDockerPodmanComposescikit-learnpandasMLflowFastAPIKServeKuberneteskubectlHelmGitHub Actions

MLOps projects you'll build

PROJECT 01

Deploy a production-style app with Helm

Hands-on project from the Kubernetes core module.

PROJECT 02

Pipeline that tests, builds and deploys on every push

Hands-on project from the CI/CD pipelines module.

PROJECT 03

Evaluation harness that scores every release

Hands-on project from the LLMOps module.

MLOps course fees & batches in Chennai

Course fee

₹34,999 incl. GST

Save up to 15%: from ₹29,740. Ask an advisor about the offer for your batch.

Pay in full or in two instalments. Online learners outside India: see the fee in your currency on our worldwide page.

Get the fee & next batch →Ask on WhatsApp

What the fee includes

  • 98 hours of live, instructor-led classes
  • Hands-on labs and 3 portfolio projects
  • Session recordings for revision
  • 1:1 mentoring
  • Interview preparation: questions and mock tests
  • Placement assistance: resume reviews, mock interviews and referrals

Batches & mode

  • Weekday batches
  • Weekend batches for working professionals
  • Fast-track batches
  • Classroom at our Arumbakkam centre, Chennai
  • Live online from anywhere

MLOps certification & exam cost

🎓
Certification this course prepares you for
Google Cloud Professional Machine Learning Engineer

The exam is booked directly with the certification provider, and its cost is set by the provider for your country. Our advisors share the current exam cost and help you plan the right exam date, with mock tests beforehand.

SOTT Academy certificate of completion (specimen)
Specimen for illustration only. Not a valid certificate. © SOTT Academy
Awarded on completion

SOTT certificate of completion

Every learner who completes the MLOps course is awarded the SOTT Academy Certificate of Completion, with your name, the course, the date and a unique certificate number.

  • Awarded when you complete the course
  • Unique certificate number
  • Add it to your resume and LinkedIn profile

MLOps jobs & career path

With placement assistance: resume and LinkedIn reviews, mock interviews, 1:1 mentoring and referrals to openings where available.

Step-by-step guide: MLOps Engineer career path

STEP 1ML EngineerTrain and ship models
STEP 2MLOps EngineerPipelines and serving
STEP 3Senior MLOps / LLMOpsReliability and evals
STEP 4ML Platform LeadPlatforms for teams
STEP 5AI Platform ArchitectStrategy and scale
Free with this course

Interview prep: 3 levels, mock tests and model answers

Practise the questions real interviewers ask, from fresher to lead.

Fresher · 0–3 yearsMid-level · 3–8 yearsExperienced · 8–15 years
Start practising →

MLOps course FAQ

What is MLOps?+
MLOps is the practice of taking machine-learning models from a notebook to production and keeping them working: pipelines, deployment, monitoring and retraining.
Who is this MLOps course for?+
Data scientists who want to deploy their models, DevOps and cloud engineers moving into AI, and developers who want to work on machine-learning platforms.
Do I need machine-learning experience?+
Basic Python helps. The course covers Python, Git, Docker and machine-learning fundamentals before MLOps, so you can join without production ML experience.
How long is the course?+
12 weeks, about 98 hours across 9 modules, with a lab in every module.
Which tools will I use?+
Python, Git and GitHub, Docker, Kubernetes, CI/CD pipelines, experiment tracking and model-serving tools, plus LLMOps tools for evaluating language models.
What projects will I build?+
A production-style app deployed with Helm, a pipeline that tests, builds and deploys on every push, and an evaluation harness that scores every release.
Does it cover LLMOps?+
Yes. A dedicated module covers running large language model applications in production: evaluation, monitoring and cost.
Which certification does it align to?+
It builds the skills for cloud ML engineering certifications such as Google Cloud Professional Machine Learning Engineer and AWS Machine Learning Engineer – Associate; we advise which one fits your role.
Is the course online or in the classroom?+
Both. Join us at our Arumbakkam centre in Chennai or live online from anywhere. Session recordings are shared with you for revision, so you can catch up if you miss a class.
Are there weekend and fast-track batches?+
Yes. We offer weekday, weekend and fast-track batches, so you can choose the schedule that suits you best.
What jobs can I apply for?+
MLOps Engineer, ML Platform Engineer, Machine Learning Engineer and AI Infrastructure roles.
Do you help with placements?+
Yes. Our placement assistance includes resume and LinkedIn reviews, mock interviews, 1:1 mentoring and referrals to openings where available, so you go into interviews well prepared.
Can I attend a free demo class first?+
Yes. Book a free consultation and our advisor will arrange a demo session for you.
What are the fee and payment options?+
Our advisors will share the current fee, upcoming batch dates and payment options for the batch that suits you.
I have another question. Who can I ask?+
Our advisors are happy to help. Call or WhatsApp us, or send a message from the Contact page, and we will get back to you within one working day.
Keep exploring

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