A complete self-paced MLOps bundle combining 40+ hours of bootcamp recordings with an AWS-specific implementation course across MLflow, FastAPI, Docker, CI/CD, AWS CodeBuild, CodeDeploy, CodePipeline, SageMaker, monitoring, and production ML pipelines.
A serious, implementation-focused MLOps bundle — not a lecture series, not a tool tour.
A model is not a product. A notebook is not a production system. MLOps is the layer that helps ML systems move toward repeatable, deployable, and maintainable workflows.
Everything lives in one notebook. There's no modular pipeline, no config, no reusable components.
Runs and metrics are scattered across spreadsheets, chat threads, and lost tabs.
Working locally, but no clear path to reproducible, deployable model artifacts.
No experience wrapping models in FastAPI or shipping prediction endpoints.
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.
Terms like tracking, registry, drift, and pipelines are familiar but not truly understood.
A model is not a product. A notebook is not a production system. This bundle helps you move from model training toward repeatable, deployable, maintainable ML workflows.
The bundle exists to help learners understand the complete MLOps implementation journey — broad lifecycle thinking plus deep AWS-specific implementation, in 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 should have the vocabulary, patterns, and implementation habits that separate someone who trains models from someone who ships and operates ML systems in production.
A production ML course and an AWS-native implementation course, packaged together so learners can move from lifecycle thinking to hands-on cloud deployment.
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. Read both honestly — a five-minute check now saves weeks later.
By the end of this bundle, you should be comfortable across the MLOps toolchain — from lifecycle thinking to AWS-native production deployment.
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
Every module of both courses, laid out end-to-end. Click any module to expand its full topic list.
Ten steps from the lifecycle mental model to running production ML on AWS.
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 systems, LLM apps, RAG pipelines, and Agentic AI platforms all rely on the same MLOps disciplines you'll build here — even when the model comes from an API.
MLOps is the foundation that survives every wave of AI. Model APIs, embeddings, RAG stores, and agent workflows may change — but lifecycle discipline, reproducibility, deployment, tracking, and monitoring stay constant. This bundle strengthens exactly that layer.
This foundation also supports learners who later move into LLMOps, RAG systems, Agentic AI, and production AI platforms — because the operational patterns are the same.
Six deliberate choices that separate this bundle from the average MLOps course.
Focused on the production layer around models — where most learners 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.
Structured content across both courses, hands-on implementation, and the resources to keep coming back to.
One bundle price. Both courses. Self-paced access. Practical production-focused MLOps implementation path.
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.
Direct answers to what comes up most often before enrolling.
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 is best suited for learners who already have some Python and ML basics. The bundle focuses on moving from model training toward production MLOps workflows, so absolute beginners will feel behind.
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.
Build the foundations to track, package, deploy, automate, and reason about ML systems in production-style workflows — with MLflow, FastAPI, Docker, CI/CD, and the full AWS MLOps stack.
Enroll NowComplete bundle · 40+ hours of bootcamp · AWS implementation course · self-paced.