MLOps Foundations
Taking models from notebook to production: versioning, pipelines, deployment and monitoring.
- ✓ Instructor-led live sessions, online or in Bengaluru
- ✓ Hands-on labs in every module
- ✓ Capstone project with trainer review
- ✓ Curriculum customisable for corporate teams
- ✓ Certificate of completion
Next batch: Mon, 04 Jan 2027 · 32 hours · 4 days
Machine Learning & MLOpsMLOps Foundations
Duration: 32 hours · 4 days
Next batch: Mon, 04 Jan 2027
Format: Online live · Bengaluru classroom
Modules: 8 · Topics: 38
Level: Intermediate
Fee: ₹14,999
Trusted by leading experts
Upcoming batches
Open batches for individuals; corporate batches on your dates.
| Programme | Starts | Mode | Schedule | Duration | |
|---|---|---|---|---|---|
| MLOps Foundations | Mon, 04 Jan 2027 | Online live | Weekday | 4 days | Enquire |
| MLOps Foundations | Sat, 27 Feb 2027 | Online live | Weekend (Sat) | 5 Saturdays | Enquire |
Soaring demand, and where it takes you
Many organisations struggle to move models into production, making MLOps skills valuable.
Become an expert
Roles this programme prepares you for:
Become a MLOps Engineer
Builds pipelines that train, deploy and monitor models.
- Track experiments and models with MLflow
- Build reproducible training pipelines
- Use a model registry for versioning
About the course
Taking machine learning from notebook to production: experiment tracking, reproducible pipelines, model registries, CI/CD for ML, deployment as APIs, and monitoring for drift.
Taking models from notebook to production: versioning, pipelines, deployment and monitoring.
Why should I attend?
Data scientists
ML engineers
DevOps and platform engineers
Data engineers supporting ML
Prerequisites
Python; basic ML (scikit-learn); Git and Docker basics
Level
Intermediate
Delivery modes, duration and fee
Online live
Instructor-led virtual classroom; weekday (full-day) or weekend (Saturday) batches
Bengaluru classroom
In-person batches at our Bengaluru training venue
On-site at your office
Private corporate batch anywhere in India, scheduled around your teams
Blended
Live sessions plus guided self-paced labs and mentor check-ins
Duration: 32 hours · 4 days
Course fee (open batch): ₹14,999
Corporate / team batches: per-participant or flat batch pricing on request, based on group size, format and customisation.
What will you learn
- Track experiments and models with MLflow
- Build reproducible training pipelines
- Use a model registry for versioning
- Automate testing and deployment with CI/CD
- Serve models as APIs in containers
- Monitor model performance and drift
Skills you will gain
Lifecycle
Automation
Operations
Tools and platforms covered
MLflow
Python
scikit-learn
Docker
FastAPI
GitHub Actions
KubernetesCourse curriculum
M01 MLOps overview
- ML lifecycle
- Common failures
- Maturity levels
- Roles in MLOps
- Tooling landscape
🧪 Hands-on: Assess an ML workflow
M02 Experiment tracking
- MLflow tracking
- Parameters and metrics
- Artefacts
- Autologging
- Comparing runs
🧪 Hands-on: Track experiments
M03 Pipelines and registry
- Reproducible pipelines
- Model registry
- Versioning
- Stage transitions
- Data versioning (DVC)
🧪 Hands-on: Register a model
M04 CI/CD for ML
- Testing data and models
- Automated training
- Deployment gates
- Model validation tests
- GitHub Actions for ML
🧪 Hands-on: Build a CI pipeline
M05 Serving
- FastAPI services
- Containers
- Scaling
- Batch vs online inference
- Kubernetes basics
🧪 Hands-on: Deploy a model API
M06 Monitoring
- Performance metrics
- Data drift
- Retraining triggers
- Concept drift
- Alerting
🧪 Hands-on: Add drift monitoring
M07 Feature management
- Feature stores
- Training-serving skew
- Point-in-time correctness
- Reuse
🧪 Hands-on: Build a feature pipeline that avoids skew
M08 Capstone: MLOps pipeline
- Track
- Register
- Deploy
- Monitor
🧪 Hands-on: Deliver a CI/CD-driven model with monitoring
Hands-on projects
Production ML pipeline
A tracked, tested and deployed model with monitoring and retraining triggers.
Why train with us
Built around your stack
Labs and examples adapted to the tools, cloud and data your team actually uses.
Practice-first
Every module ends in a lab, and the course ends with a reviewed capstone.
Practitioner-led
Delivered by Karthick Selvam and vetted specialist trainers from our network.
Visible progress
Attendance, lab completion and assessment results shared with your L&D team.
Certificate of completion
Participants who complete the labs and capstone receive a Learning Dots certificate of completion for MLOps Foundations. [confirm certificate wording / sample image]
Learner reviews
“[Verified learner review for this course]”
“[Verified learner review for this course]”
Meet your trainer
Karthick Selvam
Founder · Data Engineering Corporate Trainer, Bengaluru
[Trainer bio, experience and areas of expertise]
Frequently asked questions
Do participants need ML experience?
Basic familiarity with training models in Python is expected.
Can the curriculum be customised for our team?
Yes. Modules, depth, duration and labs can be tailored to your stack and goals.
What delivery formats are available?
Online live instructor-led sessions, Bengaluru classroom, or on-site at your office.
How do we enquire or book?
Use the enquiry form on this page or the Enquire button next to any batch.
What does it cost?
The open-batch fee is ₹14,999 per participant plus GST. Corporate batches are priced on group size, format and customisation.
How long is the course?
32 hours · 4 days. Corporate batches can be split into half-day sessions.
A closer look
About this course
Taking machine learning from notebook to production: experiment tracking, reproducible pipelines, model registries, CI/CD for ML, deployment as APIs, and monitoring for drift.
What is MLOps Foundations?
Taking models from notebook to production: versioning, pipelines, deployment and monitoring.
Who this course is for
Data scientists, ML engineers, DevOps and platform engineers, Data engineers supporting ML.
What you will be able to do
Track experiments and models with MLflow. Build reproducible training pipelines. Use a model registry for versioning. Automate testing and deployment with CI/CD. Serve models as APIs in containers. Monitor model performance and drift.
Career paths
Roles this course supports include MLOps Engineer, ML Engineer, ML Platform Engineer. Many organisations struggle to move models into production, making MLOps skills valuable.
Related courses
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Core machine learning concepts, algorithms and evaluation, explained with practical examples.
Machine Learning & MLOpsMachine Learning with Python
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