Build Strong Technical Confidence with MLOps Certification in Abu Dhabi

Module 1: MLOps Foundations — Principles, Architecture & ML Lifecycle

2 hrs

• Why 87% of ML projects fail to reach production — the technical debt crisis
• MLOps defined — applying DevOps principles to the ML lifecycle
• MLOps vs. DevOps vs. DataOps — how they relate and where they differ
• End-to-end ML workflow: problem definition, data, experimentation, training, evaluation, deployment, monitoring
• Three phases of MLOps maturity — manual, pipeline-automated, and CI/CD automated
• Key roles in an MLOps team — ML Engineer, Data Engineer, Platform Engineer, and Data Scientist
• The ML platform stack — infrastructure, orchestration, serving, and observability layers
• Batch vs. online learning architectures; feature, training, and inference pipelines
• Navigating the MLOps ecosystem — open-source vs. managed platforms

• Designing end-to-end ML system architectures for any scale and use case
• Auditing a real ML project to identify where it would fail in production
• Understanding MLOps maturity levels and planning the right adoption path
• Activity: Audit a real ML project — identify where it would fail in production and design the remediation plan

Module 2: Data Engineering for MLOps — Versioning, Pipelines & Feature Stores

2 hrs

• DVC (Data Version Control) — tracking datasets, models, and pipeline stages
• Data lineage — tracing every transformation from raw data to model input
• Delta Lake and Apache Iceberg — ACID transactions and time-travel for ML datasets
• Pipeline orchestration — Apache Airflow, Prefect, and Dagster compared
• Great Expectations and Pandera — automated data quality validation in pipelines
• Handling schema drift and schema evolution in production pipelines
• Feature stores — Feast and Hopsworks — offline vs. online feature serving
• Feature reuse — eliminating training-serving skew and duplicated engineering effort
• Point-in-time correct feature lookups — preventing data leakage in training
• Embedding pipelines — generating and storing vector embeddings at scale; vector databases

• Versioning data, code, and models using DVC, MLflow, and Git for full reproducibility
• Building robust data pipelines with automated quality validation
• Implementing feature stores to eliminate training-serving skew
• Activity: Build a versioned data pipeline with DVC and add automated data quality checks with Great Expectations

Module 3: Experiment Tracking & Model Registry

2 hrs

• Why ad-hoc experimentation is unsustainable — the reproducibility crisis in ML
• What to track — parameters, metrics, artifacts, code versions, and environment
• MLflow architecture — Tracking Server, Model Registry, Projects, and Models
• Logging experiments with mlflow.log_param, log_metric, log_artifact; autologging
• Comparing runs in the MLflow UI — visualizing metric trends and parameter importance
• Model Registry — registering models in staging, production, and archived states
• Model aliases and tagging — managing champion and challenger models
• Model metadata — storing descriptions, schema, and validation results
• Weights & Biases (W&B), Neptune.ai, and Comet ML — alternative tracking tools

• Tracking every experiment with MLflow and transitioning models through lifecycle stages
• Registering, versioning, and promoting models from staging to production
• Comparing and selecting the right experiment tracking tool for your team
• Activity: Instrument a full training run with MLflow, register the best model, and transition it to production

Module 4: Containerization & Reproducibility — Docker & Kubernetes for ML

2 hrs

• Dockerfile best practices for ML — base images, layer caching, and image size optimization
• Multi-stage builds — separating training and inference environments
• Docker Compose — orchestrating multi-container ML workflows locally
• Container registries — Docker Hub, AWS ECR, GCP Artifact Registry, and Azure ACR
• Kubernetes fundamentals — pods, deployments, services, and namespaces for ML engineers
• Deploying ML models to Kubernetes — ReplicaSets and rolling updates
• GPU allocation, memory limits, and node selectors for ML workloads
• Kubernetes autoscaling — HPA and KEDA for demand-driven inference scaling
• Kubeflow Pipelines — building and visualizing ML pipelines on Kubernetes
• Ray — distributed ML training and hyperparameter tuning at scale

• Containerizing ML workloads and deploying ML services with Docker and Kubernetes
• Managing GPU resources and autoscaling inference workloads in Kubernetes clusters
• Building and running ML pipelines on Kubernetes with Kubeflow
• Activity: Containerize a model training job and inference service, deploy to a local Kubernetes cluster with minikube

Module 5: CI/CD for Machine Learning — Automated Training & Deployment

2 hrs

• Continuous Integration for ML — what triggers a new training run
• Continuous Delivery vs. Continuous Deployment — choosing the right strategy for models
• Testing in ML — unit tests, integration tests, and model quality gates
• GitHub Actions: workflow YAML, triggers, jobs, steps, and secrets
• Automated data validation on pull request — catching data issues before training
• Automated model training triggered by data changes or scheduled cron jobs
• Model evaluation gates — automatically rejecting models that underperform baseline
• Automated Docker build and push to container registry on merge
• Shadow mode testing, canary releases, and blue-green deployment for ML models
• ArgoCD — declarative continuous delivery for Kubernetes-based ML systems
• Rollback strategies — reverting to a previous model version in under 2 minutes

• Building a complete GitHub Actions CI/CD pipeline that trains, tests, and deploys a model automatically
• Implementing model testing gates to prevent accuracy regression in production
• Executing canary releases and blue-green deployments for safe model rollouts
• Activity: Build a complete GitHub Actions CI/CD pipeline that trains, tests, and deploys a model automatically

Module 6: Model Serving — APIs, Batch Inference & Real-Time Prediction

2 hrs

• Online inference vs. batch inference vs. streaming inference — choosing the right pattern
• Latency vs. throughput tradeoffs — optimizing for your use case
• Model serialization — pickle, joblib, ONNX, TorchScript, and SavedModel
• FastAPI for model serving — production-grade REST API with Pydantic validation
• Async inference — handling concurrent requests without blocking
• BentoML, Triton Inference Server, TorchServe, and Seldon Core — dedicated serving frameworks
• Model quantization — INT8 and FP16 inference for 4x speedup without accuracy loss
• Dynamic batching — maximizing GPU utilization for throughput-intensive workloads
• Caching strategies — reducing redundant inference calls with intelligent result caching
• API versioning and backward compatibility for deployed models

• Building low-latency inference APIs and optimizing models for throughput and cost
• Deploying models with BentoML, Triton, and FastAPI at production scale
• Benchmarking latency and throughput under load to validate serving performance
• Activity: Deploy a model as a FastAPI service, containerize it, and benchmark latency and throughput under load

Module 7: Model Monitoring, Drift Detection & Automated Retraining

2 hrs

• Data drift — when the statistical distribution of incoming data shifts over time
• Concept drift — when the relationship between features and labels changes
• Model staleness — when the world changes faster than the model’s training data
• Four pillars of ML observability — data quality, model performance, infrastructure, and business KPIs
• Prometheus and Grafana — metrics collection and dashboard creation for ML systems
• Structured logging for ML — capturing prediction context for debugging and auditing
• Statistical tests for drift — KS test, PSI, Wasserstein distance, and chi-squared test
• EvidentlyAI — production-ready drift detection and monitoring reports
• WhyLabs — AI observability platform with automated drift alerts
• Scheduled retraining vs. trigger-based retraining; champion-challenger frameworks
• Human-in-the-loop approval workflows for high-stakes model updates

• Detecting data drift, concept drift, and performance degradation with automated alerts
• Instrumenting live models with EvidentlyAI and Grafana dashboards
• Designing automated retraining pipelines triggered by drift detection
• Activity: Instrument a live model with EvidentlyAI, simulate drift, trigger an automated retraining pipeline

Module 8: Cloud MLOps Platforms & Capstone Project

2 hrs

• AWS SageMaker — Pipelines, Model Registry, Model Monitor, and Feature Store
• Google Cloud Vertex AI — AutoML, Pipelines, Model Monitoring, and Workbench
• Azure Machine Learning — Designer, Pipelines, Endpoints, and Responsible AI dashboard
• Databricks MLflow on the Lakehouse — unified analytics and ML platform
• Choosing between cloud-native and open-source MLOps — a decision framework
• Cost estimation and FinOps for ML workloads — avoiding runaway cloud bills
• Spot and preemptible instances for training — cutting GPU costs by 70–90%
• LLMOps — prompt versioning, LLM evaluation frameworks (RAGAS, DeepEval), and LoRA adapter management
• Capstone: build and deploy a complete end-to-end MLOps pipeline — data versioning, experiment tracking, CI/CD, serving, and monitoring

• Navigating AWS SageMaker, GCP Vertex AI, and Azure ML for enterprise MLOps
• Managing LLM applications with prompt versioning and automated quality scoring
• Delivering a production-ready MLOps portfolio project on GitHub
• Activity: Graduates receive an MLOps Engineering certificate and a portfolio-ready GitHub repository