MLOps training

Lesson 1 • DevOps Principles Applied to ML

Translates CI/CD, infrastructure-as-code, and feedback loops into ML contexts. Grounds MLOps vocabulary in familiar DevOps concepts.

Lesson 2 • The ML Lifecycle Overview

Maps the end-to-end journey from data ingestion to model retirement. Provides the mental model that all subsequent chapters build upon.

Lesson 3 • Business Value and ROI of MLOps

Quantifies how MLOps reduces time-to-production and model failure rates. Equips learners to build a business case for MLOps investment.

Lesson 4 • Key Roles and Responsibilities

Defines the data scientist, ML engineer, and platform engineer roles within an MLOps team. Clarifies collaboration boundaries to reduce friction.

Lesson 1 • Data Lineage and Governance

Tracks data provenance from source to model artifact for auditability. Supports compliance with data governance and privacy requirements.

Lesson 2 • Data Validation and Quality Checks

Implements schema validation, statistical drift detection, and anomaly alerts. Prevents corrupt or shifted data from silently degrading models.

Lesson 3 • Data Pipeline Architecture

Covers batch and streaming ingestion patterns and their trade-offs. Connects data reliability directly to downstream model quality.

Lesson 4 • Feature Stores and Reusability

Introduces centralized feature stores for consistent training and serving features. Reduces duplication and training-serving skew across teams.

Lesson 1 • Reproducible Training Environments

Uses containerization and environment pinning to guarantee identical training conditions. Eliminates environment drift as a source of irreproducibility.

Lesson 2 • Collaboration in Experimentation

Establishes shared experiment namespaces and review workflows for team-based research. Reduces duplicated effort and accelerates knowledge sharing.

Lesson 3 • Experiment Tracking Fundamentals

Defines what constitutes a trackable experiment and why ad hoc logging fails at scale. Sets the foundation for structured experimentation practices.

Lesson 4 • Comparing and Selecting Experiments

Applies statistical and visual comparison methods to select the best model run. Builds objective decision criteria beyond single-metric selection.

Lesson 1 • Model Serialization Formats

Compares pickle, ONNX, SavedModel, and custom formats for portability and performance. Guides format selection based on deployment target requirements.

Lesson 2 • Approval Workflows and Governance

Implements gated promotion workflows requiring human sign-off before production deployment. Enforces accountability and reduces unauthorized model releases.

Lesson 3 • Model Registry Architecture

Explains registry components: metadata store, artifact store, and lifecycle state machine. Positions the registry as the single source of truth for production models.

Lesson 4 • Model Versioning and Lineage

Links each model version to its training data, code commit, and hyperparameters. Enables full reproducibility and root-cause analysis of model regressions.

Lesson 1 • Testing Strategies for ML Systems

Applies unit, integration, behavioral, and data tests across the ML pipeline. Builds a layered testing strategy that catches failures at the earliest stage.

Lesson 2 • Continuous Delivery for Models

Automates packaging, registry promotion, and deployment manifest generation after CI passes. Decouples model release from manual engineering intervention.

Lesson 3 • Automated Model Training in CI

Runs parameterized training jobs within CI pipelines using compute-efficient strategies. Balances thoroughness with pipeline execution time constraints.

Lesson 4 • Pipeline Orchestration Tools

Compares DAG-based orchestrators for scheduling and dependency management in ML pipelines. Guides tool selection based on scale and team expertise.

Lesson 1 • Inference Optimization Techniques

Applies quantization, batching, and model compilation to reduce latency and cost. Connects optimization choices to hardware and SLA constraints.

Lesson 2 • REST API Model Serving

Builds production-grade REST endpoints with input validation, versioning, and health checks. Covers request handling patterns for high-concurrency environments.

Lesson 3 • Progressive Deployment Strategies

Implements canary, blue-green, and shadow deployments to reduce production risk. Enables safe model rollouts with automated rollback triggers.

Lesson 4 • Containerized Deployment on Kubernetes

Deploys model-serving containers to Kubernetes with autoscaling and resource limits. Ensures reliable, self-healing inference services under variable load.

Lesson 1 • Data and Concept Drift Detection

Applies statistical tests and distance measures to detect input and label distribution shifts. Enables early warning before model accuracy degrades significantly.

Lesson 2 • Logging and Distributed Tracing

Instruments inference services with structured logs and distributed traces for root-cause analysis. Connects observability tooling to the broader monitoring pipeline.

Lesson 3 • Automated Retraining Triggers

Connects monitoring signals to automated retraining pipelines using schedule and drift-based triggers. Closes the feedback loop between production monitoring and model improvement.

Lesson 4 • Monitoring Strategy and Metrics

Defines operational, data quality, and model performance metrics for a complete monitoring plan. Aligns monitoring scope with business-critical model behaviors.

Lesson 1 • Security and Access Control

Applies least-privilege access, secrets management, and network segmentation to ML workloads. Protects sensitive training data and model artifacts from unauthorized access.

Lesson 2 • Cost Management and Resource Optimization

Tracks and optimizes compute, storage, and serving costs across the ML platform. Enables data-driven resource allocation decisions at organizational scale.

Lesson 3 • Infrastructure as Code for ML

Provisions ML infrastructure declaratively using IaC tools for repeatability and auditability. Eliminates configuration drift between environments through version-controlled infrastructure.

Lesson 4 • Platform Roadmap and Adoption Strategy

Plans phased platform rollouts with change management and internal developer experience improvements. Drives organization-wide MLOps adoption through enablement and feedback loops.

Lesson 5 • Model Risk and Compliance Frameworks

Implements model risk management practices including documentation, validation, and audit trails. Aligns MLOps workflows with regulatory expectations for model governance.