AI & Data · Advanced Specialist
MLOps
Version the evidence. Automate the path. Monitor the behaviour.
Turn a notebook model into a governed, observable production service.
Advanced layer includedIndustry Edge tools and scenario-based labs beyond the recognized core. Curriculum v1.1 • Updated Jul 2026
Recognized curriculum, prerequisites, assessments, and public fees disclosed before counselling.
Industry Edge Lab includedAdvanced tools, emerging patterns, and scenario-based labs beyond the recognized core.Registration and admissions follow-up continue securely in BharatCampus ONE.
Included beyond the published curriculum
The recognized core is your foundation—not the finish line.
Every MLOps cohort also enters the CohortAI Industry Edge Lab: an evolving layer of advanced tool categories, emerging architecture and production patterns, and scenario-based labs. A guided counselling preview connects the most relevant capabilities to your target role, followed by a release-governed lab sequence after enrollment.
Release-governed advanced tools Emerging architecture patterns Scenario-based labs
Fit before enrollment
Who should join
Data scientists, ML developers, DevOps learners and Python professionals moving into production AI systems.
Role pathways
Your starting point
Python, Git and machine-learning fundamentals. Docker familiarity is helpful but not mandatory.
This is not an exclusion filter. It helps us confirm whether you can start directly or would benefit from a short bridge before the cohort.Recognized core
Skills and technology stack
These employability foundations are part of the public learning promise and remain visible before registration.
Python Git Experiment tracking Data/model versioning Testing Docker CI/CD/CT Model APIs Orchestration Monitoring Cloud/Kubernetes foundations
Build-first curriculum journey
8 missions. One defensible portfolio.
Mentor-led hybrid learning with 1.5-hour live instruction, all-day supervised practice access, weekly build evidence and a version-locked capstone.
M011 week · 7.5 live hours
Software engineering and Git for ML
Notebook Escape: another learner must run the experiment without asking the author.
- Project structure and environment management
- Git branching, reviews and release tags
- Configuration and secrets
- Logging and error handling
Guided labRefactor a notebook into a repeatable command-line training package with configuration and logs.
Portfolio evidencePackaged ML repository, release tag and reproducibility guide.
AssessmentCold-machine reproducibility practical.
M022 weeks · 15 live hours
Experiment tracking and data/model versioning
Experiment Time Machine: reproduce the exact winning model from its evidence.
- Experiments, runs, parameters and metrics
- Artefact and model registry concepts
- Dataset snapshots and lineage
- Reproducible comparisons
Guided labTrack competing experiments against versioned data and promote one model through a controlled registry workflow.
Portfolio evidenceExperiment dashboard, lineage map and promotion decision record.
AssessmentReproduction and lineage audit.
M031 week · 7.5 live hours
Packaging, testing and reproducibility
Green Model Build: block a technically valid model that violates behaviour expectations.
- Reusable preprocessing and inference packages
- Unit and integration tests
- Data/schema validation
- Model-behaviour and regression tests
Guided labCreate a test suite that catches schema, preprocessing, performance and prediction-regression failures.
Portfolio evidenceTested package, container and model-quality gate.
AssessmentBroken-model build repair challenge.
M042 weeks · 15 live hours
CI/CD/CT for machine learning
No Blind Promotion: prove why this model is allowed into production.
- ML pipeline stages and triggers
- Continuous integration for code/data
- Continuous delivery of model services
- Continuous training and evaluation
Guided labBuild an automated workflow that trains, evaluates and promotes only when quality gates pass.
Portfolio evidencePipeline-as-code, promotion policy and rollback evidence.
AssessmentPipeline failure and policy review.
M052 weeks · 15 live hours
Model serving, APIs and containers
Inference Traffic Lab: meet a latency target without returning inconsistent predictions.
- Online versus batch inference
- Prediction API design
- Input validation and feature parity
- Containerisation and resource control
Guided labServe a model through a validated API, load-test it and execute a canary rollout.
Portfolio evidenceInference service, load report and deployment strategy.
AssessmentLatency and rollback simulation.
M061 week · 7.5 live hours
Orchestration and cloud platforms
Workflow Recovery Drill: resume safely after a mid-pipeline failure.
- Workflow DAGs and scheduling
- Data/training/validation components
- Container and artefact handoff
- Kubernetes and managed ML platform concepts
Guided labOrchestrate a reusable train-to-register pipeline with retry and artefact lineage.
Portfolio evidenceWorkflow definition, component contracts and cloud architecture.
AssessmentPipeline architecture and failure-recovery practical.
M072 weeks · 15 live hours
Monitoring, drift and observability
Drift War Room: decide whether to monitor, retrain, roll back or investigate data.
- Service and model health
- Data and concept drift
- Prediction quality and delayed labels
- Slice and fairness monitoring
Guided labSimulate drift, identify affected segments and define a response without unnecessary retraining.
Portfolio evidenceMonitoring dashboard, drift report and response playbook.
AssessmentSignal-to-action review.
M081 week · 7.5 live hours
MLOps capstone
Model Release Day: prove reproducibility, quality, rollout safety and operational ownership.
- Repository and platform architecture
- Versioned data and experiments
- Tested training and promotion
- Serving and rollout
Guided labDeliver a complete governed ML platform path from data change to monitored prediction service.
Portfolio evidencePlatform repository, lineage, registry, service, dashboards, runbooks and postmortem.
AssessmentAI Platform Review Board and MLOps interview simulation.
The week feels different here
A repeatable rhythm from concept to evidence
Weekly rhythm
- 1Concept sprint and visual roadmap
- 2Guided implementation lab
- 3Independent build challenge
- 4Debug, review and production-pattern clinic
- 5Skill check, demo and learning reflection
Learner experience
CohortAI Skill Passport with milestone badges
Build-first weekly checkpoints rather than lecture-only completion
Peer demo and code/design review rituals
Mentor office hours and all-day practice-lab access
Capstone Demo Day with a business narrative, technical walkthrough and interview-style defence
Responsible AI-assisted learning policy: explain, verify and own every submitted artifact
Interview-ready proof
What you will be able to show—not merely claim
These are tangible outputs you can demonstrate, explain, and defend through a technical walkthrough, business narrative, or interview conversation.
Reproducible experiment repository
Versioned data/model pipeline
Tested containerised inference service
Automated train-evaluate-promote workflow
Monitored production ML capstone
Transparent evaluation
How your progress is assessed
Completion standard
Minimum 70% overall, mandatory capstone pass, at least 80% lab submissions and versioned portfolio evidence.
CohortAI Industry Edge Lab
Your published curriculum is the foundation. The Industry Edge is what keeps it moving forward.
The AI Platform Lab extends the program with unified ML and GenAI tracking, feature management, cloud-native serving, open telemetry and modern inference patterns.
Your counselling preview connects relevant capability categories to your target role. The complete release-governed lab sequence becomes part of the enrolled learning plan.Version-controlled learning promise
MLOPS@1.1.0
Released 24 July 2026 under catalog CAT-2026.09.1. This page presents the approved curriculum, learner outcomes, duration, and public fee for informed comparison.
Straight answers
Frequently asked questions
Who is the MLOps program designed for?
Data scientists, ML developers, DevOps learners and Python professionals moving into production AI systems.
What prerequisites do I need?
Python, Git and machine-learning fundamentals. Docker familiarity is helpful but not mandatory.
What is included in the public curriculum?
The public curriculum includes the recognized stack, all 8 module themes, guided labs, portfolio artifacts, assessment model, duration, and public fees. The Industry Edge Preview explains how advanced capability categories extend this foundation for your target role.
Where do counselling and enrollment happen?
CohortAI supports public discovery and program comparison. Registration, consent, counselling follow-up, application updates, and enrollment are managed securely in BharatCampus ONE.