What do I need to know before starting?

The entry expectations are listed on this page. If you are close but not quite there, tell us at application; we run short pre-course preparation material and would rather get you ready than turn you away.

Does this course include the internship?

Yes, for learners who complete the core modules and meet the mid-point assessment bar. The internship is on live client work with a supervisor and code review, and you receive a written reference describing what you actually did.

Is there a certificate?

Yes, on completion of the capstone. More usefully, you finish with deployed projects, a public repository history, and a supervisor’s reference. Those are what interviews actually turn on.

What roles does Data Engineering & AI Ops prepare me for?

Typical destinations are Data Engineer, Analytics Engineer, MLOps Engineer, AI Platform Engineer and Data Platform Engineer. The curriculum is built backwards from what those interviews and first ninety days actually require.

What do I need to know before starting?

The entry expectations are listed on this page. If you are close but not quite there, tell us at application; we run short pre-course preparation material and would rather get you ready than turn you away.

Does this course include the internship?

Yes, for learners who complete the core modules and meet the mid-point assessment bar. The internship is on live client work with a supervisor and code review, and you receive a written reference describing what you actually did.

Is there a certificate?

Yes, on completion of the capstone. More usefully, you finish with deployed projects, a public repository history, and a supervisor’s reference. Those are what interviews actually turn on.

What roles does Data Engineering & AI Ops prepare me for?

Typical destinations are Data Engineer, Analytics Engineer, MLOps Engineer, AI Platform Engineer and Data Platform Engineer. The curriculum is built backwards from what those interviews and first ninety days actually require.

Academy track14 weeks · Intermediate

Data Engineering & AI Ops

Name: Data Engineering & AI Ops
Price: 58000 INR

The plumbing that decides whether AI works at all

Apply for this trackSee the full syllabus

₹58,000or 4 monthly instalments

Duration: 14 weeks
Level: Intermediate
Effort: 12–15 hrs/week
Format: Live online + project labs

Apply now

Next cohort intake is open · instalments available

Why this track

Every failing AI project we have audited failed at the data layer, not the model layer. This track covers the unglamorous half that decides everything: reliable pipelines, well-modelled warehouses, orchestration that recovers from failure, and the operational discipline to run models in production.

14weeks of live, project-based work

8modules, each ending in something deployed

4portfolio projects, reviewed like client work

5+roles this track prepares you for

Outcomes

What you will be able to do

Build ingestion pipelines that are idempotent and recover from failure
Model a warehouse that analysts and applications can both use
Orchestrate scheduled and event-driven workflows with proper alerting
Operate vector stores and embedding pipelines at production scale
Serve and monitor models, and detect drift before users report it
Attribute and control infrastructure and inference cost

Before you start

Entry expectations

If you are close but not quite there, tell us at application. We would rather get you ready than turn you away.

01Python and SQL fundamentals
02Basic command line and Git
03Some exposure to databases

Tools you will use

PythonSQLPostgreSQLDuckDBAirflowDagsterdbtKafkapgvectorDockerKubernetesPrometheusGrafana

Syllabus

The route, module by module

8 modules over 14 weeks. Every one ends in something deployed to a real URL and reviewed line by line.

- Batch and streaming, file formats, partitioning, idempotency, and why most pipelines break on the second run
- Advanced SQL, dimensional modelling, slowly changing dimensions, and building marts people trust
- Airflow or Dagster, dependencies, retries, backfills, data contracts and quality tests
- Chunking at scale, embedding pipelines, index maintenance, incremental updates and permission filtering
- Packaging, batching, GPU and CPU trade-offs, autoscaling, and latency budgets under real traffic
- Data quality monitoring, model performance monitoring, drift detection, and alerts that mean something
- Storage lifecycle, compute right-sizing, caching, token accounting and per-tenant cost attribution
- An end-to-end pipeline feeding a served model, with quality tests, dashboards and a cost report

Portfolio

What you will have built

01An idempotent ingestion pipeline with data quality tests
02A dimensional warehouse with documented contracts
03An embedding pipeline with incremental index updates
04Capstone: a monitored, cost-attributed production data and model platform

Where it leads

Roles this prepares you for

Data EngineerAnalytics EngineerMLOps EngineerAI Platform EngineerData Platform Engineer

Questions

Before you apply

The programme runs 14 weeks and expects 12–15 hrs/week including project work. Live sessions are scheduled outside standard office hours and recorded, so working professionals can keep a full-time job while studying.