Live MLOps Workshop•Sunday, 13th September 2026•11:00 AM EST•2 hours•₹499 / $19

Live MLOps Workshop

Deploy ML to Production in 2 Hours

This workshop is for people who can train a model but want to become credible at shipping it. We go from notebook to service: FastAPI endpoint, Docker packaging, deployment thinking, health checks, CI/CD, and the minimal production discipline serious ML work needs.

If you have been searching deploy ML model production, MLOps tutorial, or docker ML model workflows, this session is designed to answer that exact need with a practical, end-to-end deployment path.

deploy ml model productionmlops tutorialml deploymentdocker ml modelfastapi model serving

Notebook to API

Turn a model into a service with clear inputs and outputs.

Docker + cloud path

Understand packaging, reproducibility, and deployment readiness.

MLOps without fluff

Learn health checks, versioning, CI/CD, and observability basics.

11:00 AM EST · Live + recording

2 hours

Deployment-focused session

₹499 / $19

Geo-aware pricing

Recording included

Replay the full flow later

Search intent

Why this page is intentionally built to rank for high-intent workshop searches

Search visibility is strongest when a page resolves the real question behind a keyword. This section turns the page into a stronger resource for people actively searching for this exact topic.

If you have been searching deploy ML model production, MLOps tutorial, or docker ML model workflows, this session is designed to answer that exact need with a practical, end-to-end deployment path.

A lot of people can say “I built a model.” Far fewer can say “I shipped it safely, monitored it, and understood the operational tradeoffs.” That difference matters in interviews and on real teams.

If you are tired of notebooks that never become products, this workshop gives you a sharper route to production fluency.

Keyword focus for this workshop page

deploy ml model production
mlops tutorial
ml deployment
docker ml model
fastapi model serving

What you will build

What you will build in this ML deployment workshop

This is not a vague webinar page. You should know exactly what you are getting before you register. The bullets below are intentionally specific so the workshop attracts serious learners with real engineering intent.

FastAPI prediction service

Wrap a model behind a clean REST interface.

Dockerized runtime

Package the service for repeatable environments.

Cloud deployment blueprint

Understand the steps that take a container to a live endpoint.

CI/CD release pattern

See how testing and builds fit into safer deployment.

Health and monitoring basics

Know what to track once the service is live.

Versioning discipline

Avoid chaos when the model evolves.

Code preview: the kind of deployment surface we will construct

1from fastapi import FastAPI
2app = FastAPI()
3 
4@app.get("/health")
5def health():
6    return {"status": "ok"}
7 
8@app.post("/predict")
9def predict(payload: PredictionRequest):
10    features = preprocess(payload)
11    score = model.predict(features)
12    return {"prediction": score}

This preview is representative, not decorative. The workshop is built around code, architecture reasoning, and the exact implementation details that make these workflows usable in real projects.

Join the workshop and build this live$19

Outcome and social proof

Why deployment fluency matters so much

Outcome

A stronger production story

Explain deployment with much more credibility.

Outcome

Reusable service scaffold

Keep a pattern for future FastAPI + Docker projects.

Outcome

Better MLOps vocabulary

Discuss CI/CD, health checks, and versioning with confidence.

Outcome

Portfolio depth

Move your projects beyond notebooks.

Practical application

How you can use Deploy ML to Production in 2 Hours after the workshop ends

The goal is not just to attend a live session. The goal is to leave with a mental model and implementation pattern you can reuse in interviews, portfolio work, internal projects, or customer-facing products.

Implementation takeaways

FastAPI prediction service

Wrap a model behind a clean REST interface.

Dockerized runtime

Package the service for repeatable environments.

Cloud deployment blueprint

Understand the steps that take a container to a live endpoint.

CI/CD release pattern

See how testing and builds fit into safer deployment.

Career and project leverage

A stronger production story

Explain deployment with much more credibility.

Reusable service scaffold

Keep a pattern for future FastAPI + Docker projects.

Better MLOps vocabulary

Discuss CI/CD, health checks, and versioning with confidence.

Portfolio depth

Move your projects beyond notebooks.

Who this is for

Who should join this ML deployment workshop?

The strongest SEO pages also help visitors self-qualify quickly. These personas make it obvious whether the workshop matches your current stage and goals.

ML engineers

You want to strengthen the serving and operations side of your skillset.

Data scientists

You want to move from experimentation to shipping.

Backend engineers

You want to understand how ML deployment differs from standard API work.

Interview candidates

You want a more credible production ML story.

Agenda

Detailed agenda: how the production deployment session runs

This is a detailed live workshop, not a teaser. The timeline below shows exactly how the two-hour session is structured so you can assess whether the depth matches what you need.