Back to RoadmapsMLOps & AI Infrastructure
Build and operate the infrastructure that powers machine learning systems at scale, from training pipelines to production serving
10 milestones in this roadmap
Step 1beginner4-5 weeks
Software Engineering for ML
Apply software engineering best practices to ML projects with testing, packaging, and clean code
Curriculum
- 1Python Packaging: setuptools, pyproject.toml & Virtual Environments
- 2Testing for ML: pytest, Fixtures & Property-Based Testing
- 3Git Workflows for ML Projects & Notebook Versioning
- 4Clean Code: Refactoring Notebooks into Modules
- 5Dependency Management: Poetry, pip-tools & Conda
Tools & Platforms
PythonpytestGitPoetrypre-commitmypy
Step 1beginner4-5 weeks
Software Engineering for ML
Apply software engineering best practices to ML projects with testing, packaging, and clean code
Curriculum
- 1Python Packaging: setuptools, pyproject.toml & Virtual Environments
- 2Testing for ML: pytest, Fixtures & Property-Based Testing
- 3Git Workflows for ML Projects & Notebook Versioning
- 4Clean Code: Refactoring Notebooks into Modules
- 5Dependency Management: Poetry, pip-tools & Conda
Step 2intermediate5-6 weeks
Data Pipeline Engineering
Build ML data pipelines with data versioning, feature stores, and validation frameworks
Curriculum
- 1Data Versioning: DVC, Git LFS & Lakefs
- 2Feature Stores: Feast, Offline/Online Serving & Point-in-Time
- 3Data Validation: Great Expectations & Pandera
- 4Training-Serving Skew Prevention
- 5
Step 3intermediate4-6 weeks
Experiment Tracking
Track experiments systematically with MLflow, W&B, and automated hyperparameter tuning
Curriculum
- 1MLflow: Tracking Server, Model Registry & Stages
- 2Weights & Biases: Logging, Sweeps & Reports
- 3Hyperparameter Optimization: Grid, Random & Bayesian
- 4Optuna: Bayesian Optimization & Hyperband Pruning
- 5
Step 4intermediate5-7 weeks
Model Training at Scale
Scale training with distributed computing, DeepSpeed, GPU clusters, and mixed precision
Curriculum
- 1Data Parallelism: DDP & Gradient Synchronization
- 2Model Parallelism: Pipeline & Tensor Parallelism
- 3DeepSpeed: ZeRO Stages & CPU/NVMe Offloading
- 4Mixed Precision Training: FP16, BF16 & Loss Scaling
- 5
Step 5intermediate5-7 weeks
Model Serving & Inference
Deploy models with REST/gRPC APIs, TensorFlow Serving, and NVIDIA Triton for production inference
Curriculum
- 1Model Serialization: ONNX, TorchScript & SavedModel
- 2REST & gRPC API Design for ML Serving
- 3Batch vs Real-Time Inference Architectures
- 4TensorFlow Serving: Versioning & Batching
- 5
Step 6intermediate4-5 weeks
Containerization for ML
Build GPU-enabled containers for ML with Docker, NVIDIA Container Toolkit, and K8s GPU scheduling
Curriculum
- 1Docker for ML: GPU Images, CUDA & Large Models
- 2NVIDIA Container Toolkit: GPU Passthrough & MIG
- 3Container Optimization: Multi-Stage Builds for ML
- 4Model Packaging: Container Image Best Practices
- 5
Step 7advanced6-8 weeks
ML Pipelines & Orchestration
Automate ML lifecycles with Kubeflow, Vertex AI, SageMaker Pipelines, and ZenML
Curriculum
- 1Kubeflow Pipelines: Components, DSL & Caching
- 2Google Vertex AI: Managed ML Pipelines
- 3AWS SageMaker Pipelines: Step Functions for ML
- 4ZenML: Stack Abstraction & Pipeline Steps
- 5
Step 8advanced5-6 weeks
Monitoring & Observability for ML
Monitor production models for data drift, concept drift, and prediction quality degradation
Curriculum
- 1Data Drift Detection: PSI, KL Divergence & Statistical Tests
- 2Model Drift & Concept Drift: Detection Strategies
- 3Prediction Quality Monitoring & Ground Truth Feedback
- 4Evidently: Reports, Dashboards & Test Suites
- 5
Step 9advanced6-8 weeks
LLMOps
Deploy and operate LLM infrastructure with vLLM, vector databases, RAG, and fine-tuning pipelines
Curriculum
- 1LLM Serving: vLLM, Continuous Batching & PagedAttention
- 2Prompt Management: Versioning, A/B Testing & Registries
- 3Vector Databases: Pinecone, Weaviate & Milvus Operations
- 4RAG Infrastructure: Chunking, Retrieval & Evaluation
- 5
Step 10advanced5-7 weeks
Platform & Cost Optimization
Optimize ML platform costs with GPU scheduling, auto-scaling, and multi-model serving
Curriculum
- 1GPU Scheduling: Time-Slicing, MPS & MIG
- 2Auto-Scaling: Scale to Zero, Queue-Based & Custom HPA
- 3Cost Monitoring, FinOps & Chargeback Models
- 4Multi-Model Serving: GPU Sharing & Model Multiplexing
- 5
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Tools & Platforms
PythonpytestGitPoetrypre-commitmypy
Data Labeling Pipelines & Active Learning
Tools & Platforms
DVCFeastGreat ExpectationsPanderaLabel StudioDelta Lake
Step 2intermediate5-6 weeks
Data Pipeline Engineering
Build ML data pipelines with data versioning, feature stores, and validation frameworks
Curriculum
- 1Data Versioning: DVC, Git LFS & Lakefs
- 2Feature Stores: Feast, Offline/Online Serving & Point-in-Time
- 3Data Validation: Great Expectations & Pandera
- 4Training-Serving Skew Prevention
- 5Data Labeling Pipelines & Active Learning
Tools & Platforms
DVCFeastGreat ExpectationsPanderaLabel StudioDelta Lake
Reproducibility: Environment Capture & Seed Management
Tools & Platforms
MLflowWeights & BiasesOptunaRay TuneNeptuneClearML
Step 3intermediate4-6 weeks
Experiment Tracking
Track experiments systematically with MLflow, W&B, and automated hyperparameter tuning
Curriculum
- 1MLflow: Tracking Server, Model Registry & Stages
- 2Weights & Biases: Logging, Sweeps & Reports
- 3Hyperparameter Optimization: Grid, Random & Bayesian
- 4Optuna: Bayesian Optimization & Hyperband Pruning
- 5Reproducibility: Environment Capture & Seed Management
Tools & Platforms
MLflowWeights & BiasesOptunaRay TuneNeptuneClearML
Spot Instances, Checkpointing & Fault Tolerance
Tools & Platforms
DeepSpeedPyTorch DDPHorovodRay TrainNVIDIA NCCLHugging Face Accelerate
Step 4intermediate5-7 weeks
Model Training at Scale
Scale training with distributed computing, DeepSpeed, GPU clusters, and mixed precision
Curriculum
- 1Data Parallelism: DDP & Gradient Synchronization
- 2Model Parallelism: Pipeline & Tensor Parallelism
- 3DeepSpeed: ZeRO Stages & CPU/NVMe Offloading
- 4Mixed Precision Training: FP16, BF16 & Loss Scaling
- 5Spot Instances, Checkpointing & Fault Tolerance
Tools & Platforms
DeepSpeedPyTorch DDPHorovodRay TrainNVIDIA NCCLHugging Face Accelerate
NVIDIA Triton: Multi-Model Serving & Dynamic Batching
Tools & Platforms
NVIDIA TritonTensorFlow ServingTorchServeBentoMLFastAPIgRPC
Step 5intermediate5-7 weeks
Model Serving & Inference
Deploy models with REST/gRPC APIs, TensorFlow Serving, and NVIDIA Triton for production inference
Curriculum
- 1Model Serialization: ONNX, TorchScript & SavedModel
- 2REST & gRPC API Design for ML Serving
- 3Batch vs Real-Time Inference Architectures
- 4TensorFlow Serving: Versioning & Batching
- 5NVIDIA Triton: Multi-Model Serving & Dynamic Batching
Tools & Platforms
NVIDIA TritonTensorFlow ServingTorchServeBentoMLFastAPIgRPC
Kubernetes GPU Scheduling & Resource Management
Tools & Platforms
DockerNVIDIA Container ToolkitKubernetesNVIDIA GPU OperatorKanikoBuildKit
Step 6intermediate4-5 weeks
Containerization for ML
Build GPU-enabled containers for ML with Docker, NVIDIA Container Toolkit, and K8s GPU scheduling
Curriculum
- 1Docker for ML: GPU Images, CUDA & Large Models
- 2NVIDIA Container Toolkit: GPU Passthrough & MIG
- 3Container Optimization: Multi-Stage Builds for ML
- 4Model Packaging: Container Image Best Practices
- 5Kubernetes GPU Scheduling & Resource Management
Tools & Platforms
DockerNVIDIA Container ToolkitKubernetesNVIDIA GPU OperatorKanikoBuildKit
Pipeline Design: Gating, Retraining Triggers & Caching
Tools & Platforms
KubeflowVertex AI PipelinesSageMaker PipelinesZenMLMetaflowFlyte
Step 7advanced6-8 weeks
ML Pipelines & Orchestration
Automate ML lifecycles with Kubeflow, Vertex AI, SageMaker Pipelines, and ZenML
Curriculum
- 1Kubeflow Pipelines: Components, DSL & Caching
- 2Google Vertex AI: Managed ML Pipelines
- 3AWS SageMaker Pipelines: Step Functions for ML
- 4ZenML: Stack Abstraction & Pipeline Steps
- 5Pipeline Design: Gating, Retraining Triggers & Caching
Tools & Platforms
KubeflowVertex AI PipelinesSageMaker PipelinesZenMLMetaflowFlyte
WhyLabs: Data Profiling & Anomaly Detection
Tools & Platforms
EvidentlyWhyLabsNannyMLArizePrometheusGrafana
Step 8advanced5-6 weeks
Monitoring & Observability for ML
Monitor production models for data drift, concept drift, and prediction quality degradation
Curriculum
- 1Data Drift Detection: PSI, KL Divergence & Statistical Tests
- 2Model Drift & Concept Drift: Detection Strategies
- 3Prediction Quality Monitoring & Ground Truth Feedback
- 4Evidently: Reports, Dashboards & Test Suites
- 5WhyLabs: Data Profiling & Anomaly Detection
Tools & Platforms
EvidentlyWhyLabsNannyMLArizePrometheusGrafana
Fine-Tuning Pipelines: LoRA Serving & Adapter Management
Tools & Platforms
vLLMPineconeWeaviateLangChainLlamaIndexHugging Face TGI
Step 9advanced6-8 weeks
LLMOps
Deploy and operate LLM infrastructure with vLLM, vector databases, RAG, and fine-tuning pipelines
Curriculum
- 1LLM Serving: vLLM, Continuous Batching & PagedAttention
- 2Prompt Management: Versioning, A/B Testing & Registries
- 3Vector Databases: Pinecone, Weaviate & Milvus Operations
- 4RAG Infrastructure: Chunking, Retrieval & Evaluation
- 5Fine-Tuning Pipelines: LoRA Serving & Adapter Management
Tools & Platforms
vLLMPineconeWeaviateLangChainLlamaIndexHugging Face TGI
ML A/B Testing Infrastructure & Automated Rollback
Tools & Platforms
KubernetesNVIDIA MIGKEDAKNativeKubecostSeldon Core
Step 10advanced5-7 weeks
Platform & Cost Optimization
Optimize ML platform costs with GPU scheduling, auto-scaling, and multi-model serving
Curriculum
- 1GPU Scheduling: Time-Slicing, MPS & MIG
- 2Auto-Scaling: Scale to Zero, Queue-Based & Custom HPA
- 3Cost Monitoring, FinOps & Chargeback Models
- 4Multi-Model Serving: GPU Sharing & Model Multiplexing
- 5ML A/B Testing Infrastructure & Automated Rollback
Tools & Platforms
KubernetesNVIDIA MIGKEDAKNativeKubecostSeldon Core