MLOps Mastery 100 Labs for Production AI

What you'll learn

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Architect production-grade ML inference systems using Docker, Kubernetes, KServe, Istio, and modern MLOps patterns.
Package and optimize machine learning and generative AI models using ONNX, TorchScript, INT8 quantization, TensorRT, Triton, and vLLM.
Deploy predictive and LLM workloads through Kubernetes-native serving infrastructure with secure, repeatable release processes.
Master GPU-aware inference optimization, dynamic batching, KV caching, PagedAttention, multi-LoRA serving, and high-concurrency performance testing.
Build automated MLOps and Continuous Training pipelines using Kubeflow Pipelines, MLflow, Argo, and reproducible artifact lineage.
Implement production observability with Prometheus, Grafana, OpenTelemetry, Evidently AI, inference metrics, audit logs, and automated incident hooks.
Secure and govern AI platforms using RBAC, OPA Gatekeeper, network policies, JWT/OAuth2, secret management, supply-chain security, and immutable audit trails.
Engineer resilient and cost-aware AI infrastructure with autoscaling, canary releases, blue-green deployments, failover, FinOps gates, and chaos engineering.
Extend ML systems beyond the data center through edge inference, federated learning, WebAssembly, serverless inference, hybrid-cloud failover, Kafka, and multi-
Architect and deliver the Lab 100 sovereign AI platform-a reproducible, governed, observable, scalable enterprise ML platform recreated entirely from infrastruc
Requirements

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Required Knowledge
1. Basic Python programming
2. Basic Linux command-line usage
3. Basic Git knowledge is helpful but not mandatory
4. Basic understanding of REST APIs is recommended
5. Basic understanding of machine learning concepts such as models, inference, training, and datasets
Recommended Hardware
1. 16+ GB RAM
2. 4+ CPU cores
3. 100+ GB available SSD storage
4. NVIDIA GPU with sufficient VRAM for the selected models
Description

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This course contains the use of artificial intelligence.
I only charge a fee solely for the time invested in building this comprehensive curriculum.
Stop Vibe Coding. Start Engineering AI Systems.
You can build a machine learning model in a notebook in an afternoon.
You can make an LLM answer questions in a few lines of code.
You can deploy an API with a handful of commands.
But that is not the same thing as building aproduction-grade AI system.
The real engineering challenge begins when your model needs reproducible data, version-controlled artifacts, automated testing, secure deployment, scalable inference, monitoring, drift detection, rollback mechanisms, compliance evidence, and continuous retraining.
That is the gap this course is designed to close.
This is not a collection of disconnected MLOps tool demonstrations.
It is a100-lab engineering journey that progressively takes you from a clean local development environment to an enterprise-grade LLM/RAG platform.
From Notebook to Production Infrastructure
The course starts where most AI projects actually begin: with Python, Git, notebooks, scripts, and local environments.
But we don't stay there.
You will progressively introduce the engineering layers required to transform experimental machine learning into reliable operational systems.
You'll containerize workloads with Docker.
You'll build reproducible data pipelines with DVC.
You'll create consistent feature workflows with Feast.
You'll validate data with Great Expectations.
You'll track experiments and models with MLflow.
You'll automate testing and deployment through GitHub Actions.
Then the infrastructure gets serious.
You'll move into Kubernetes and Kubeflow to orchestrate machine learning workflows at scale.
You'll package and serve models with BentoML.
You'll engineer high-throughput inference using adaptive batching and gRPC.
You'll introduce autoscaling, canary deployments, blue-green releases, automated rollback logic, and load testing.
The objective is not simply to know what these tools are.
The objective is to understand how the pieces fit together.
What's Inside the 100 Labs?
Build the Engineering Foundation
Labs 1-10 establish the development discipline required for everything that follows.
You'll create isolated Python environments, version notebooks, modularize ML code, test data scripts, containerize workloads, orchestrate local services, expose inference APIs, and assemble your first automated end-to-end ML pipeline.
This is your transition from experimentation to engineering.
Control Your Data
Labs 11-20 introduce reproducible data operations.
You'll use DVC to version datasets and pipelines, separate raw and processed data, track metrics, and create reproducible ETL workflows.
You'll also work with Feast for feature management and Great Expectations for automated data validation.
Now your model isn't just versioned.
Its data lineage becomes part of the system.
Track Every Experiment and Model
Labs 21-30 introduce MLflow and Optuna.
You'll track experiments, parameters, metrics, artifacts, hyperparameter sweeps, model packages, production candidates, lifecycle transitions, and lineage.
You'll also create automated model quality gates so that promotion isn't based on someone's intuition or a manual checklist.
Automate the ML Lifecycle
Labs 31-40 bring CI/CD into the picture.
GitHub Actions, automated testing, Continuous Machine Learning, model evaluation reports, image publishing, semantic versioning, webhooks, staging environments, and smoke tests become part of the workflow.
The goal is simple
Stop treating deployment as an event. Make it a repeatable system.
Scale with Kubernetes and Kubeflow
Labs 41-50 introduce Kubernetes orchestration and Kubeflow Pipelines.
You'll create containerized pipeline components, move artifacts between steps, execute parallel workloads, schedule recurring pipelines, manage secrets, troubleshoot failed pods, and preserve pipeline metadata.
This is where your MLOps architecture starts behaving like infrastructure rather than a collection of scripts.
Engineer High-Performance Inference
Labs 51-60 focus on model serving.
You'll work with BentoML, adaptive batching, gRPC, Kubernetes deployments, horizontal autoscaling, Prometheus metrics, canary deployments, blue-green releases, automated rollbacks, and Locust-based stress testing.
You won't just deploy a model.
You'll learn how tooperate inference under production pressure.
Observe and Repair Production AI
Labs 61-70 address one of the hardest problems in real-world ML: systems change after deployment.
You'll establish statistical baselines, capture inference telemetry, detect data and concept drift, generate quality reports, visualize health metrics with Grafana, configure alerts, trigger retraining, and preserve operational logs for auditing.
The model is no longer treated as a static artifact.
It becomes a continuously monitored production system.
Secure and Govern the Platform
Labs 71-80 introduce enterprise security and governance.
You'll implement Kubernetes RBAC, scan container images, manage secrets with Vault, encrypt sensitive data, mask PII, enforce OPA policies, create immutable audit trails, classify AI risks, document models and datasets, and perform security testing.
Because production AI isn't only about accuracy.
It is also aboutwho can access it, what it does, how it is changed, and whether you can prove what happened.
Enter the LLMOps Era
Labs 81-90 move into modern LLM infrastructure.
You'll build vector search with Qdrant, prepare documents for RAG, generate embeddings, perform approximate nearest-neighbor search, deploy local LLM runtimes, construct RAG endpoints, version prompts, evaluate responses with Ragas, monitor token consumption and latency, and introduce guardrails.
This section connects traditional MLOps engineering with the rapidly evolving world ofLLMOps and production RAG systems.
Engineer for Sovereignty and Resilience
Labs 91-99 go beyond standard cloud deployment.
You'll explore hybrid and edge architectures, decentralized/federated learning, secure model synchronization, quantization, ONNX optimization, disaster recovery, multi-region failover, chaos engineering, and Infrastructure as Code with OpenTofu.
The question changes from
"Can I deploy this?"
to
"Can this system survive?"
Lab 100: The PhD-Level Engineering Capstone
Everything culminates inLab 100: Productionizing an Enterprise-Grade LLM RAG System.
This is not another toy chatbot.
You will architect an integrated production system where private documents move through a version-controlled DVC pipeline, embeddings are indexed into Qdrant, inference is handled through a vLLM backend and BentoML service, workflows are orchestrated with Kubeflow, experiment and model metadata are tracked through MLflow, and production behavior is monitored with Evidently AI and Prometheus.
The architecture also incorporates automated CI/CD validation, security scanning, drift monitoring, retraining logic, canary deployment, and compliance-oriented auditing.
The final objective is to demonstrate that the system can move beyond a successful demo and operate as arepeatable production platform.
That is the real transformation this course is targeting.
You begin with a Python environment.
You finish thinking like anAI systems architect.
Why Enroll Now?
The industry is moving beyond the era where simply knowing how to train a model is enough.
Modern AI teams need engineers who understand the complete lifecycle
Data → Code → Model → Container → Pipeline → Deployment → Inference → Observability → Security → Governance → Continuous Improvement
And increasingly
RAG → LLM Serving → Evaluation → Guardrails → Sovereign Infrastructure
You don't need to master 100 technologies before beginning.
You need a structured path that introduces each layer at the right time and shows you how it connects to the layers that came before it.
That's what these 100 labs are designed to provide.
Don't just learn how AI models are created. Learn how production AI systems are engineered, operated, secured, observed, and continuously improved.
Start Lab 1 and build toward Lab 100.
Who this course is for

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1. The Aspiring AI / MLOps Engineer
You know Python and machine learning, but your experience stops at notebooks and prototypes.
2. The DevOps / Platform Engineer Moving Into AI
You already understand Linux, containers, Kubernetes, CI/CD, infrastructure, or cloud platforms.
3. The Senior Engineer / Architect Seeking AI Sovereignty
You are responsible for architecture, reliability, security, compliance, cost, or technical strategy.
Homepage

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