MLOps Engineer
For engineers who want to take ML and LLM models from notebooks to production. You will build, deploy, monitor, and automate reliable ML systems on cloud infrastructure.
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01 Programming and Engineering Foundations Build the coding, scripting, and engineering habits every production ML system depends on. 02 Machine Learning Fundamentals Understand the models you will ship so you can train, evaluate, and debug them with confidence. 03 Data Engineering and Management Build reliable, versioned, and validated data pipelines that feed training and inference. 04 Containers and Kubernetes Package ML workloads into portable containers and run them at scale on Kubernetes. 05 Cloud Platforms and Infrastructure Provision secure, cost-efficient cloud infrastructure and accelerators for ML workloads. 06 ML Pipelines and Experiment Management Automate training with reproducible pipelines, tracked experiments, and a governed model registry. 07 Model Optimization, Serving, and Deployment Optimize models and serve them through fast, scalable APIs using safe release strategies. 08 Monitoring, Observability, and Governance Detect drift, trace failures, retrain automatically, and keep models secure, fair, and compliant. 09 LLMOps and Generative AI Systems Operate LLM applications in production, from fine-tuning and serving to RAG, agents, and evaluation. 10 Capstone Projects and Career Prove your skills with end-to-end projects, a strong portfolio, and interview preparation. MLOps Engineer For engineers who want to take ML and LLM models from notebooks to production. You will build, deploy, monitor, and automate reliable ML systems on cloud infrastructure. 10 STAGES · 50 TOPICS · 191 SUBTOPICS 1.1 Python for Production Core Python and OOP Classes · Decorators · Generators · Context Managers Type Hints and Validation mypy · Pydantic Asynchronous Programming asyncio · Concurrency · Multiprocessing Packaging and Environments uv · Poetry · pip · pyproject.toml Profiling and Performance cProfile · py-spy · Memory Profiling 1.2 Linux and Shell Scripting Linux Command Line Basics File System and Permissions Process and Service Management systemd · cron · Signals Bash Scripting Variables · Loops · Pipes 1.3 SQL and Databases Advanced Queries CTEs · Window Functions · Joins Schema Design and Normalization Query Optimization Indexes · Query Plans 1.4 Version Control with Git Branching Strategies GitFlow · Trunk-Based Merge Conflicts and Rebasing Git Hooks and pre-commit 1.5 Testing and Code Quality Unit and Integration Testing pytest · Fixtures Mocking and Property-Based Testing unittest.mock · Hypothesis Linting and Formatting Ruff · Black Design Patterns for ML Code 1.6 Systems Languages (Optional) C++ for High-Performance Inference Go or Rust for Infrastructure Tooling 2.1 Classical Machine Learning Regression and Classification Clustering and Dimensionality Reduction K-Means · DBSCAN · PCA Tree-Based Models Random Forest · XGBoost · LightGBM Feature Engineering scikit-learn Pipelines · Encoders · Scalers 2.2 Deep Learning Basics Neural Network Fundamentals PyTorch Essentials Tensors · Autograd · DataLoaders TensorFlow and Keras Basics Transformer Architecture Basics Pre-training vs Fine-tuning 2.3 Evaluation Metrics Classification Metrics Precision · Recall · F1 · ROC-AUC Regression Metrics MSE · MAE · R-Squared Validation Strategies Cross-Validation · Holdout · Time-Based Splits Generative Metrics Perplexity · BLEU · ROUGE 3.1 Data Pipelines and Orchestration Apache Airflow Prefect Dagster Mage 3.2 Data Versioning Data Version Control (DVC) lakeFS Table Formats Delta Lake · Apache Iceberg 3.3 Data Quality and Validation Great Expectations Pandera Deequ 3.4 Data Labeling and Annotation Label Studio Argilla Programmatic Labeling Snorkel · Weak Supervision LLM-Assisted Labeling 3.5 Big Data and Streaming Apache Spark PySpark · Spark SQL Apache Kafka Stream Processing Apache Flink · Spark Streaming Lakehouses and Warehouses Databricks · Snowflake · BigQuery 3.6 Feature Stores Feast Hopsworks Managed Feature Stores SageMaker · Vertex AI · Databricks 4.1 Docker Docker Fundamentals Images · Containers · Volumes · Networking Docker Compose Multi-Stage Builds and Layer Caching Minimal and Distroless Images GPU Containers (NVIDIA Container Toolkit) 4.2 Kubernetes Kubernetes Architecture Control Plane · Nodes · etcd Core Objects Pods · Deployments · Services · Ingress Config and Storage ConfigMaps · Secrets · Volumes Autoscaling HPA · KEDA · Karpenter GPU Scheduling (NVIDIA GPU Operator) 4.3 Kubernetes Packaging and GitOps Helm Charts Kustomize GitOps Argo CD · Flux 5.1 Cloud Fundamentals Identity and Access Management (IAM) Networking Basics VPCs · Subnets · Load Balancers Object Storage S3 · GCS · Azure Blob Compute Services EC2 · Compute Engine · Azure VMs Managed Kubernetes EKS · GKE · AKS Serverless Compute Lambda · Cloud Run · Azure Functions 5.2 Managed ML Platforms Amazon SageMaker Google Vertex AI Azure Machine Learning Databricks 5.3 Infrastructure as Code Terraform and OpenTofu Pulumi Ansible 5.4 Hardware and Accelerators GPUs and TPUs CUDA and GPU Monitoring nvidia-smi · NVML · DCGM GPU Clouds RunPod · Modal · Lambda · CoreWeave 5.5 Cost Optimization (FinOps) Pricing Models On-Demand · Spot · Reserved Autoscaling and Right-Sizing Cloud Cost Monitoring Budgets · Tagging · Kubecost 6.1 Experiment Tracking MLflow Tracking Weights & Biases Comet Hyperparameter Tuning Optuna · Ray Tune 6.2 Model Registry and Versioning MLflow Model Registry Cloud Registries SageMaker · Vertex AI Model Packaging and Lineage Signatures · Artifacts · Metadata 6.3 ML Pipeline Platforms Kubeflow Pipelines ZenML Metaflow Cloud Pipelines SageMaker Pipelines · Vertex AI Pipelines 6.4 CI/CD for Machine Learning GitHub Actions GitLab CI/CD Jenkins Continuous Machine Learning (CML) Model Validation Gates 6.5 Distributed Training Data Parallelism DDP · FSDP Model Parallelism Pipeline · Tensor Distributed Frameworks Ray Train · DeepSpeed Communication Backends NCCL · MPI 7.1 Model Optimization Format Conversion ONNX · torch.export Quantization PTQ · QAT · INT8 · FP8 Pruning and Knowledge Distillation Compilers and Runtimes TensorRT · OpenVINO · ONNX Runtime 7.2 API Development FastAPI Flask and Django gRPC vs REST GraphQL 7.3 Model Serving Frameworks NVIDIA Triton Inference Server KServe Ray Serve BentoML TensorFlow Serving 7.4 Deployment Patterns Batch Inference Online and Real-Time Inference Streaming Inference Edge and On-Device Deployment LiteRT · ONNX Runtime · Core ML 7.5 Release Strategies A/B Testing Canary Releases Shadow Deployment Blue-Green Deployment 7.6 ML System Design Microservices Architecture Event-Driven Architecture Scalability and High Availability 8.1 Infrastructure Monitoring Prometheus Grafana Logging Stacks ELK · Loki · OpenSearch 8.2 Logging, Tracing, and Alerting Structured Logging Distributed Tracing OpenTelemetry · Jaeger Alerting and On-Call Alertmanager · PagerDuty SLOs and Incident Response 8.3 ML Model Monitoring Data Drift and Concept Drift Prediction and Performance Monitoring Evidently AI Arize AI Fiddler 8.4 Continuous Training Automated Retraining Pipelines Retraining Triggers Schedule · Drift · Performance Feedback Loops and Label Collection 8.5 Security and Governance Adversarial Robustness Data Privacy and Anonymization Supply Chain Security Image Scanning · SBOMs · Model Signing Secrets Management Vault · Cloud KMS 8.6 Responsible AI and Compliance Bias and Fairness Auditing Model Explainability SHAP · LIME Model Cards and Documentation Regulatory Compliance EU AI Act · GDPR · HIPAA 9.1 LLM Fine-Tuning Parameter-Efficient Fine-Tuning LoRA · QLoRA · PEFT Fine-Tuning Tooling Hugging Face TRL · Unsloth · Axolotl Preference Alignment DPO · RLHF 9.2 LLM Serving and Inference Inference Engines vLLM · SGLang · TensorRT-LLM Local and Edge Serving Ollama · llama.cpp LLM Quantization AWQ · GPTQ · GGUF · FP8 KV Cache and Batching PagedAttention · Continuous Batching AI Gateways and Routing LiteLLM · Rate Limiting · Fallbacks 9.3 Retrieval-Augmented Generation RAG Pipelines Chunking · Embeddings · Reranking Vector Databases Milvus · Qdrant · Weaviate · Pinecone · pgvector RAG Frameworks LangChain · LlamaIndex 9.4 Prompt Management and Observability Prompt Versioning and Templates LLM Tracing LangSmith · Langfuse · Arize Phoenix Token Usage and Cost Tracking 9.5 LLM Evaluation and Guardrails Evaluation Frameworks Ragas · DeepEval · TruLens LLM-as-a-Judge Guardrails NeMo Guardrails · Llama Guard Prompt Injection Defenses 9.6 Agentic Systems Agent Frameworks LangGraph · CrewAI · OpenAI Agents SDK Tool Calling and MCP Agent Observability and Evaluation 10.1 Capstone Projects End-to-End ML Pipeline Data · Training · Registry · Serving · Monitoring Kubernetes Model Serving Platform KServe · Autoscaling · Canary Releases Production RAG Service Vector Database · Evaluation · Tracing Drift Detection and Auto-Retraining 10.2 Portfolio and Visibility Documented GitHub Repositories Architecture Diagrams and Write-Ups Open-Source Contributions Technical Blog Posts 10.3 Certifications (Optional) AWS ML Engineer Associate Google Professional ML Engineer Kubernetes Certifications CKA · CKAD HashiCorp Terraform Associate 10.4 Interview Preparation ML System Design Interviews Coding and Debugging Rounds Infrastructure and Kubernetes Scenarios Behavioral and Incident Stories