Where you are: the model performs, in a notebook. The gap: tracking, packaging, serving, CI/CD, cloud deployment and monitoring — MLflow, FastAPI, Docker, GitHub Actions, AWS CodeBuild, CodeDeploy, CodePipeline, SageMaker. Where this takes you: an ML system your team can run, retrain and trust. The path: the MLOps lifecycle first, then AWS-native implementation, both self-paced.
Implementation first — not a lecture series, not a tour of tool logos.
A model is not a product, and a notebook is not a system. The other half is repeatability, deployment and operations — and it is usually learned on a live project, under pressure. Here is where it typically bites.
It all lives in one notebook — no modular pipeline, no config, nothing reusable next quarter.
Runs and metrics are scattered across spreadsheets, chat threads, and lost tabs.
It runs locally. There is no clean route to a reproducible, deployable artifact.
Wrapping a model in FastAPI and standing up a prediction endpoint is still new ground.
Retraining, testing, and redeploying models are all manual, ad-hoc steps.
AWS console clicks work once, but nothing is versioned or repeatable.
CodeBuild, CodeDeploy, CodePipeline, S3, SageMaker — still feel like disconnected tools.
Once a model is deployed, there's no visibility into how it behaves in production.
Tracking, registry, drift and pipelines are familiar words, not yet decisions you make.
A model is not a product. A notebook is not a production system. Close this layer and you stop handing work over for someone else to operationalise — you ship it yourself, and you can explain every choice in it.
Lifecycle thinking on its own stays abstract. AWS clicks on their own stay shallow. You need both, in that order, which is why they are packaged as one path.
The MLOps Bootcamp gives you 40+ hours of production ML lifecycle depth — the mental model behind experiment tracking, packaging, serving, CI/CD, deployment, and monitoring. The Enterprise MLOps with AWS course drops you into a real AWS project and walks you through S.C.A.L.E-style implementation using S3, CodeBuild, CodeDeploy, CodePipeline, SageMaker, Data Wrangler, AutoML, and MLflow. Together, they cover both MLOps thinking and hands-on production workflows.
By the end you can stand up the pipeline, deploy the model, watch it in production, and answer the reliability, cost and rollback questions a review will put to you — because you ship and operate ML systems, not just train them.
A production ML course and an AWS-native implementation course, in sequence — the mental model first, then the same ideas running on real cloud infrastructure.
40+ hours of live bootcamp recordings covering the broader MLOps implementation journey — lifecycle, Git, project structuring, Docker, FastAPI, CI/CD, MLflow, deployment, monitoring, and production ML system thinking.
AWS-specific implementation course covering CodeBuild, CodeDeploy, CodePipeline, S3, FastAPI deployment, SageMaker Studio, Data Wrangler, AutoML, MLflow, preprocessing, and training at scale — end-to-end.
Both courses ship together at one bundle price. Self-paced access to everything included on enrolment.
Two lists. Five minutes of honesty here saves you a wasted month.
Own an ML system end to end — structure it, track it, package it, serve it, automate it, deploy it on AWS, and keep watch on it once real traffic arrives.
MLOps lifecycle and production ML architecture
Git, GitHub, and project structuring for ML systems
ML model packaging and deployment workflows
FastAPI & Streamlit ML application serving
Docker basics for ML deployment
CI/CD for ML using GitHub Actions and AWS services
MLflow for experiment tracking, model registry, and model serving
AWS CodeBuild, CodeDeploy, CodePipeline for ML deployment
S3-based model deployment workflows
SageMaker Studio and key SageMaker components
Data Wrangler and AutoML basics
Preprocessing and training at scale using SageMaker jobs
Monitoring and debugging ML systems
How production ML systems are structured, automated, and operated
Production readiness for real ML deployments
Both courses laid out end to end, nothing hidden. Expand any module to see exactly what it covers.
Each step assumes the one before it. By step ten you are reasoning about drift and observability, not about how to get the pipeline to run.
SDLC, architecture, and where CI/CD, tracking, deployment, monitoring fit.
Modular pipelines, config, reusable components.
Tracking server, metrics, model registry, versioning.
Reproducible artifacts, container-based deployment thinking.
Prediction APIs, Streamlit, model-serving patterns.
GitHub Actions, automation workflows, deployment pipelines.
CodeBuild, CodeDeploy, CodePipeline, S3 — end to end.
SageMaker Studio, Data Wrangler, AutoML fundamentals.
Python preprocessing scripts, remote processing, SageMaker jobs.
Drift, observability, operational readiness — the discipline behind ML that stays alive in production.
GenAI apps, RAG pipelines and agent platforms run on the same disciplines you build here — even when the model itself arrives over an API.
MLOps is the part that survives every wave of AI. Model APIs, embeddings, vector stores and agent frameworks turn over every year. Lifecycle discipline, reproducibility, deployment, tracking and monitoring do not. This is the layer you keep.
It carries directly into LLMOps, RAG systems, Agentic AI and production AI platforms — the operational patterns are the same ones, wearing newer names.
Six deliberate choices, each aimed at what you can build on Monday rather than what reads well in a syllabus.
Aimed squarely at the production layer around the model — where projects actually stall.
Every module is anchored in hands-on demos and real project structure.
Tools are taught in context of a real ML lifecycle, not as isolated features.
Bootcamp gives the mental model; AWS course gives the implementation muscle.
You leave with the vocabulary and habits of engineers who ship ML systems.
A strong base before LLMOps, Agentic AI, RAG, and production AI platforms.
Working implementations and source code across both courses, so you lift a proven pattern into your own project instead of rebuilding it from a video.
One bundle price. Both courses. Self-paced access — no cohort date to wait for, you begin the day you decide.
40+ hours of MLOps bootcamp recordings + Enterprise MLOps with AWS — production-focused, implementation-first.
Self-paced · production-focused · no hype, no guarantee — a serious bundle for engineers who want to ship ML.
The questions experienced engineers ask before committing time to this.
It is a bundle combining the MLOps Bootcamp recordings and the Enterprise MLOps with AWS course. Two complete learning assets, sold together at a single bundle price.
The MLOps Bootcamp includes 40+ hours of live session recordings, and the AWS-specific implementation course is also included on top of that.
It assumes you already have Python and ML basics. The focus is the move from model training to production MLOps workflows, so a first-time programmer will be playing catch-up throughout.
Yes. The bundle includes AWS-specific MLOps implementation covering S3, CodeBuild, CodeDeploy, CodePipeline, SageMaker, Data Wrangler, AutoML, preprocessing, and training at scale.
Yes. MLflow is covered for experiment tracking, model management, model registry, model serving, and integration with ML workflows — in both the bootcamp and the AWS course.
Yes. Docker and deployment workflows are part of the broader MLOps bootcamp content and continue into the AWS implementation project.
Yes. It covers GitHub Actions and AWS CI/CD workflows using CodeBuild, CodeDeploy, and CodePipeline — both as concepts and as hands-on implementation.
No. This is a production MLOps bundle. It builds strong foundations that are useful before moving deeper into LLMOps, Agentic AI, RAG, and production AI systems — but those need additional focused learning.
Yes — this is a self-paced bundle built from recorded MLOps Bootcamp sessions and the AWS-specific implementation course content.
This gives you a strong implementation foundation. Real production systems may require additional project-specific architecture, security, governance, monitoring, and organisation-specific deployment practices on top of what's taught here.
You already build models that work. Add the tracking, packaging, serving, automation and monitoring that turn one into a system your organisation can depend on — MLflow, FastAPI, Docker, CI/CD and the AWS MLOps stack.
Take a Model to ProductionComplete bundle · 40+ hours of bootcamp · AWS implementation course · self-paced.
You already bring real engineering experience. These live programs add the production layer on top of it — without asking you to start over.
This course is part of our self-paced foundations library, recorded from earlier live bootcamps. For the current live cohort experience, the programs below are where to go next.
Eight live weeks. One production-style Agentic AI system you build end to end — orchestration, governed tools & MCP, production RAG, async execution, evaluation, security, deployment — and every decision something you can defend. Nothing else required first: Python and LangChain foundation bonuses included free.
Secure Your Seat →Once you can ship the system, the harder question is which system to build. Discovery, scoping, an architecture you can defend, evaluation, delivery and adoption — twelve weeks of live case labs. Reserved for Diamond Members; not sold separately.
Explore Diamond →