AI & Data

MLOps

Turn model experiments into reproducible pipelines, served predictions and monitored machine learning systems.

Intermediate → AdvancedClassroom / Live · availability pending

A clear learning outcome.

Turn model experiments into reproducible pipelines, served predictions and monitored machine learning systems.

Who should join

Students with relevant programming foundations who want a structured next step. Discuss prerequisites with an advisor.

What you’ll learn

Establish a reproducible ML development workflow.
Connect model decisions to reproducible evidence.
Automate the path from dataset to deployable model.
Deliver predictions through a practical service.
Identify changes that affect deployed ML systems.

Technologies & concepts

PythonMLflowDockerModel serving

Inside the syllabus.

Suggested syllabus for academic review. Module allocations are illustrative; confirm the current batch curriculum and total duration before enrolling.

MODULE 01
ML engineering foundationsSuggested allocation: 3–4 sessions

ML lifecycle
Data validation
Python environments
Version control
Reproducibility

Assignment / build: Package an existing model experiment.

Learning outcome: Establish a reproducible ML development workflow.

MODULE 02
Experiment trackingSuggested allocation: 3–4 sessions

Metrics
Parameters
MLflow
Artifacts
Data and model versioning

Assignment / build: Track experiments and compare candidate models.

Learning outcome: Connect model decisions to reproducible evidence.

MODULE 03
Training pipelinesSuggested allocation: 3–4 sessions

Feature pipelines
Orchestration
Evaluation gates
Model registry
Automation

Assignment / build: Build a pipeline with a model evaluation gate.

Learning outcome: Automate the path from dataset to deployable model.

MODULE 04
Serving & deliverySuggested allocation: 3–4 sessions

Batch vs online inference
Model APIs
Containerization
CI/CD
Deployment

Assignment / build: Expose a containerized model API.

Learning outcome: Deliver predictions through a practical service.

MODULE 05
Monitoring capstoneSuggested allocation: 3–4 sessions

Data drift
Prediction quality
Latency
Retraining triggers
Rollbacks

Assignment / build: Create a monitoring plan and end-to-end pipeline.

Learning outcome: Identify changes that affect deployed ML systems.

Build something worth discussing.

Tracked ML pipeline

A suggested project direction. Your mentor will confirm scope and review criteria.

Monitored prediction API

A suggested project direction. Your mentor will confirm scope and review criteria.

UP TO100%SCHOLARSHIP

CAST · YOUR OPPORTUNITY TO LEARN

Your ambition. A scholarship to match.

Apply for CAST — the Coding Blocks Aptitude and Scholarship Test — and earn a scholarship of up to 100% on course fees.

Available for classroom and Live interactive courses, including Sampoorna. Awards are subject to the test’s eligibility and award terms.

Apply for CAST

Your schedule. Your batch.

No upcoming batches

No batches are currently listed for this course. Enquire about the next intake.

Get the current schedule ↗

Learn. Practice. Build.

The suggested methodology connects concept sessions, exercises, reviewed assignments and a practical project. Confirm doubt support, recording availability and mock interview inclusions for your selected batch.

Career relevance

This pathway builds skills for project work and relevant technical discussions. Course completion alone does not guarantee an interview or employment outcome.

A little clarity

Good questions.
Clear answers.

Classroom or live: which learning mode is right for me?

Classroom learning suits students who prefer an in-person routine and peer interaction. Live learning suits those who need to attend remotely. Ask the advisor about center availability, schedules and support for your selected batch.

How long are the programs?

Duration depends on the course, batch and schedule. This source package leaves duration and dates open until the current batch information is confirmed.

Are there projects and doubt sessions?

Project ideas appear on each course page. Project scope, doubt-session format and availability must be confirmed against the selected batch curriculum and support policy.

Continue your journey

Related pathways.

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Data Science

Learn to ask better questions of data, build reproducible analyses and evaluate machine learning models.

Classroom / Live · enquireBeginner → Intermediate

PythonPandasSQLscikit-learn

Duration & mentor: confirmed with your batch.

Syllabus & projects insideView course ↗

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can start today.

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