AI5 PROFESSIONAL CERTIFICATE · BY TGC INDIA · UPDATED AUGUST 2026
MLOps & Model Deployment
Package, deploy, monitor and update machine-learning models.
Online + OfflineWeekdays + WeekendsRegular + Fast Track
22+years in training
20,000+learners across TGC
5classroom locations
Live onlinejoin from anywhere
NEXT START OPTIONS
Choose a live batch.
COURSE OVERVIEW
What this course
is built to do.
Package, deploy, monitor and update machine-learning models using reproducible projects, APIs, containers and operating pipelines.
Each module combines a live trainer demonstration, guided lab, assignment and review before the next stage.
CONNECTED CAREER DIRECTIONS
MLOps EngineerModel Deployment EngineerMachine Learning Platform AssociateAI Operations Engineer
IS THIS COURSE RIGHT FOR YOU?
Choose it for the right reason.
You can train machine-learning models
You want deployment and monitoring skills
You need production-style portfolio work
YOUR LEARNING ARC
From guided foundation to finished work.
Reproduce
Version code, data, configuration and experiments.
Serve
Package a model behind a validated API.
Release
Use containers, tests and delivery gates.
Operate
Monitor, retrain and roll back safely.
INDUSTRY TASKS
Practise the work, not only the tool.
- Create a tracked ML training pipeline
- Deploy a containerised inference API
- Build drift monitoring and retraining flow
PRO
LED
LED
YOUR TRAINING TEAM
Learn from experienced working professionals.
Live demonstrations, guided practice and direct project feedback are part of the course. Trainers update examples and tool coverage as professional practice changes.
WHAT YOU WILL BE ABLE TO DO
Course outcomes
01
Create reproducible ML projects
A versioned training pipeline with experiment records.
02
Serve models reliably
A containerised API with schema and automated tests.
03
Monitor model and service health
A dashboard with drift, error and latency alerts.
04
Manage model updates
A deployed capstone with registry, retraining and rollback procedures.
TAKE THE NEXT STEP
Need fees, syllabus or the right batch?
An AI5 Academy advisor can help you compare mode, schedule and starting level.
DETAILED COURSE FLOW
5 learning modules
Package, deploy, monitor and update machine-learning models through reproducible pipelines and production-style operating practices.
Reproducible ML projects
Turn experimental notebooks into versioned projects that can be rebuilt.
CORE TOPICS
- Project structure and environments
- Data and model versioning
- Experiment tracking with MLflow
- Configuration, seeds and reproducibility
GUIDED PRACTICE
Convert a notebook model into a repeatable training project with tracked runs.
MODULE DELIVERABLE
Reproducible ML repository
Model packaging and API serving
Expose trained models through a tested service contract.
CORE TOPICS
- Model serialisation and loading
- FastAPI prediction endpoints
- Input schema and validation
- Batch and online inference patterns
GUIDED PRACTICE
Package a trained model and serve predictions through a validated API.
MODULE DELIVERABLE
Model inference service
Containers, CI and delivery
Create consistent build and release processes across development and production.
CORE TOPICS
- Docker images and dependencies
- Unit, data and model tests
- CI pipelines and release gates
- Registry and environment promotion
GUIDED PRACTICE
Containerise the service and run automated tests before creating a release build.
MODULE DELIVERABLE
Containerised ML delivery pipeline
Monitoring and model health
Detect data changes, performance loss and operating failures after launch.
CORE TOPICS
- Service logs, latency and errors
- Data quality and drift
- Prediction and model performance
- Alerts, dashboards and incident response
GUIDED PRACTICE
Instrument a deployed model and simulate drift or service failure.
MODULE DELIVERABLE
Model monitoring dashboard
Retraining and production capstone
Design safe updates with rollback, approval and documentation.
CORE TOPICS
- Retraining triggers and pipelines
- Model registry and comparison
- Canary, rollback and approval
- Cloud deployment and operating runbook
GUIDED PRACTICE
Deploy a complete ML service and demonstrate monitoring, retraining and rollback.
MODULE DELIVERABLE
MLOps production capstone
HOW THE TRAINING WORKS
Learn it. Apply it. Get it reviewed. Improve it.
Every important skill moves through explanation, demonstration, guided use and independent application. Trainer feedback is used to revise the work before it becomes part of the final portfolio.
Concept briefing
The trainer explains the principle, use case, limitations and the quality standard expected.
Live demonstration
A complete task is demonstrated while the trainer explains decisions, checks and common mistakes.
Guided lab
Learners repeat the method with support, ask questions and correct problems during the session.
Applied assignment
The same method is used on a different brief so the learner must make independent decisions.
Review and revision
Work is checked against a rubric, revised after feedback and prepared for project presentation.
PROGRESS IS CHECKED THROUGHClass exercisesModule deliverablesProject reviewsFinal capstone presentation
PORTFOLIO WORK
Projects you can show
Guided practice brief
Plan, produce, test and present a finished piece with trainer feedback.
Individual application
Plan, produce, test and present a finished piece with trainer feedback.
Workflow build
Plan, produce, test and present a finished piece with trainer feedback.
Industry-style assignment
Plan, produce, test and present a finished piece with trainer feedback.
Quality review
Plan, produce, test and present a finished piece with trainer feedback.
Final capstone
Plan, produce, test and present a finished piece with trainer feedback.
TAKE THE NEXT STEP
Need fees, syllabus or the right batch?
An AI5 Academy advisor can help you compare mode, schedule and starting level.
TOOLS COVERED
DockerMLflowFastAPICloud
Tool coverage may be updated when the industry changes. Core methods remain part of the course.COMMON QUESTIONS
Before you apply
How much machine learning is required?
You should already be able to train and evaluate a supervised model in Python.
＋Are Docker and MLflow included?
Yes. Both are used in the practical delivery flow.
＋Which cloud is used?
Deployment patterns are taught so they can be applied to common cloud services; the class platform depends on available access.
＋Do I need coding experience?
No, unless the course level says otherwise. Your advisor will check the right starting level.
＋Are classes live or recorded?
Classes are trainer-led in the classroom or live online. Recordings may support revision but do not replace class.
＋Will I receive a certificate?
Yes. Course completion requires attendance, assignments and the final project.
＋Can working professionals join?
Yes. Weekday, weekend and selected fast-track schedules are available.
＋NEXT BATCH
Choose your course.
Choose your schedule.
Online or offline. Weekdays or weekends. Regular or fast track. Speak with an AI5 Academy advisor about the right starting level.