MLOps: From Model to Production Roadmap

Comprehensive, production-grade learning path for Mlops, architected with foundational-to-advanced pedagogical progression.

Phase 1 Phase 0: Orientation & Mental Models

Introduction to the MLOps ecosystem, defining the role of an MLOps engineer, and understanding the lifecycle differences between traditional software engineering and machine learning systems.

Milestone What is MLOps? Principles & Components: TinyMLOps Fleet Lifecycle Management

Process for lifecycle management, performance monitoring, and automated model updates for machine learning on millions of embedded edge devices.

Milestone What is MLOps? Principles & Components: Agile Principles

Master the core principles of agile software development, such as prioritizing customer satisfaction and adapting to change.

Milestone Roles & Responsibilities in ML Teams: Systems Thinking in ML Teams

Crosscutting science concepts: Patterns, Cause & Effect, Scale, Energy & Matter, Systems & Models.

Milestone Roles & Responsibilities in ML Teams: Role-Based Access Control

Network security design principle 'Never trust, always verify' with continuous authentication and least-privilege access.

Milestone The ML Lifecycle vs. SDLC: Deterministic vs Probabilistic Outputs

Analysis of the reasoning differences between deterministic rule-based systems and probabilistic data-driven systems (Data-Driven / Explainable AI).

Phase 2 Phase 1: Foundations & First Pipeline

Establishing the coding environment, version control basics, and executing the first simple ML experiment with tracking. This phase focuses on 'Quick Wins' to build confidence before diving into infrastructure.

Milestone Programming Fundamentals for ML (Python & Bash): First-Class Data and Functions

Exploring the concept of functions as first-class citizens and higher-order functions that can take other functions as arguments or return a function.

Milestone Version Control Systems (Git & GitHub): Version Control Workflow

Understand and apply a Git workflow, such as Git Flow, to manage branches (feature, develop, release) effectively within a team.

📚 Prerequisites: ZERO_TRUST_ARCHITECTURE, AGILE_PRINCIPLES

Milestone Experiment Tracking Basics (MLFlow): Structured Experiment State Logging

Architecture for orchestrating tasks for autonomous agents, including problem decomposition (planning), tool selection (tool use), and self-reflection on results.

📚 Prerequisites: TINY_MLOPS_LIFECYCLE_MANAGEMENT

Phase 3 Phase 2: Infrastructure as Code & Containerization

Moving from local notebooks to reproducible environments. Learners will containerize their applications and provision cloud infrastructure using declarative code, bridging the gap between development and deployment.

Milestone Infrastructure as Code (Terraform): Declarative Infrastructure Provisioning

Milestone Infrastructure as Code (Terraform): Immutable Infrastructure Principles

Milestone Containerization (Docker & Kubernetes Basics): Automated Workload Orchestration

Phase 4 Phase 3: CI/CD & Data Engineering Pipelines

Automating the flow of data and code. This phase introduces continuous integration/deployment pipelines and robust data ingestion patterns, ensuring models are trained on fresh, validated data.

Milestone Data Engineering Fundamentals (Pipelines & Warehouses): Data Cleaning Techniques

Master basic techniques for handling missing values, noise, and inconsistent data.

Milestone Data Engineering Fundamentals (Pipelines & Warehouses): Relational Data Retrieval

Use the SELECT statement syntax to query and retrieve data from one or more columns in a table.

Milestone Orchestration Basics (Airflow/KubeFlow Intro): Agent Orchestration Patterns

The learner will be able to distinguish and apply patterns for orchestrating multiple agents, such as supervisor-led, peer-to-peer, pipeline, and debate patterns, to build robust multi-agent workflows.

📚 Prerequisites: JIT_ORCHESTRATION_AND_SCALING

Phase 5 Phase 4: Model Serving & Observability

Deploying models to production and monitoring their health. This phase covers serving architectures, real-time monitoring, and handling drift, which are critical for maintaining production reliability.

Milestone Model Training & Serving Strategies: Edge Model Quantization and Compression

Techniques for compressing and reducing numerical precision of machine learning models (from 32-bit to 8-bit or 4-bit) to optimize size and inference speed on edge devices.

Milestone Monitoring & Observability (Prometheus, Grafana): System State Monitoring and Visualization

Using Observable Object (@StateObject, @ObservedObject) to manage and share complex class-based state across views.

📚 Prerequisites: SQL_SELECT, DATA_CLEANING_TECHNIQUES

Milestone Data Lineage & Drift Detection: Bias in AI

Understanding how bias in training data or algorithm design can lead to unfair decisions from AI systems.

Phase 6 Phase 5: Advanced Operations & Edge AI

Exploring specialized deployment scenarios including edge computing and advanced explainability. This phase addresses constraints like low latency, offline capabilities, and regulatory compliance.

Milestone Explainable AI (SHAP, LIME): AI Governance & Active Red-Teaming

AI governance processes, proactive safety testing (Red-Teaming), and establishing guardrails for autonomous systems.

📚 Prerequisites: AI_BIAS, EDGE_MODEL_QUANTIZATION

Milestone Edge AI Deployment (TFLite, PyTorch Mobile): TinyML Ultra-Low Power Inference

Principles of designing and executing machine learning algorithms on ultra-low-power microcontrollers with limited RAM/memory resources.

Milestone Security & Governance in MLOps: Threat Identification and Classification

The learner will be able to identify potential cybersecurity threats and classify them according to their nature and source.

📚 Prerequisites: SHARED_OBSERVABLE_STATE