MLOps Fundamentals
MLOps Fundamentals teaches practical operations for production ML, covering experiment tracking, data versioning, model packaging, serving, and CI/CD, and equips learners with patterns, anti‑patterns, and decision frameworks for reliable deployment.
Who Should Take This
Data scientists, ML engineers, and dev‑ops professionals who have built models but lack systematic production workflows should enroll. The course suits early‑career to mid‑level practitioners seeking to standardize pipelines, avoid common pitfalls, and accelerate model delivery at scale across teams.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
61 learning goals
1
Experiment Tracking and Reproducibility
6 topics
Describe experiment tracking concepts including parameters, metrics, artifacts, and reproducibility requirements and explain why systematic tracking prevents lost or irreproducible results
Apply experiment tracking tools including MLflow, Weights & Biases, and TensorBoard to log hyperparameters, training metrics, model artifacts, and dataset versions
Apply experiment comparison and analysis including metric visualization, hyperparameter sweep analysis, and artifact diffing to identify the best-performing model configuration
Analyze experiment organization strategies including project structure, naming conventions, tagging, and team collaboration patterns for multi-researcher ML projects
Apply reproducibility practices including random seed management, deterministic training, environment capture, and how to ensure identical results across experiment reruns
Analyze experiment tracking at organizational scale including shared tracking servers, access control, experiment archiving, and cross-team collaboration patterns for ML research
2
Data Management and Versioning
7 topics
Describe data versioning concepts including dataset snapshots, lineage tracking, and why reproducible ML requires versioned data alongside versioned code
Apply data versioning tools including DVC, Delta Lake, and LakeFS to track dataset changes, create reproducible pipelines, and enable data rollback
Describe feature stores including online and offline stores, feature computation, point-in-time correctness, and how feature stores prevent training-serving skew
Apply data quality monitoring including schema validation, distribution drift detection, missing value alerts, and data unit testing to catch data issues before they affect model training
Analyze data pipeline design patterns including batch versus streaming ingestion, idempotent transformations, and data contract enforcement between producers and consumers
Apply synthetic data generation pipelines including generating labeled training data, privacy-preserving synthetic datasets, and validating that synthetic data preserves statistical properties
Apply label management including annotation pipelines, label versioning, consensus mechanisms for multi-annotator workflows, and how label quality directly impacts model performance
3
Model Packaging and Registry
5 topics
Describe model serialization formats including pickle, ONNX, TorchScript, SavedModel, and explain the trade-offs between framework-specific and framework-agnostic model formats
Apply model packaging with dependency management including conda environments, Docker containers, and MLflow model packaging to ensure reproducible model loading and inference
Apply model registry workflows including staging, production, and archived model versions, approval gates, and automated promotion pipelines for governed model deployment
Analyze model artifact management at scale including storage optimization, garbage collection of stale models, and access control policies for multi-team model registries
Apply model signature validation including input-output schema enforcement, data type checking, and how signature validation prevents serving errors from mismatched model inputs
4
Model Serving and Inference
7 topics
Describe model serving patterns including real-time inference, batch prediction, streaming inference, and embedded models and explain when each pattern is appropriate
Apply REST and gRPC model serving using frameworks including TensorFlow Serving, TorchServe, Triton Inference Server, and FastAPI model endpoints
Apply model optimization for serving including quantization, pruning, ONNX Runtime, TensorRT, and distillation to reduce latency and cost in production inference
Apply autoscaling and load balancing for model serving including horizontal pod autoscaling, request batching, and GPU sharing to handle variable inference traffic
Analyze serving architecture trade-offs including serverless versus dedicated instances, edge versus cloud inference, and cost-latency optimization for different traffic patterns
Apply A/B testing for model serving including traffic splitting, statistical significance calculation, guard rails, and how to measure the business impact of model version upgrades
Describe model serving architectures for LLMs including KV-cache management, continuous batching, speculative decoding, and the unique infrastructure challenges of serving large language models
5
CI/CD for Machine Learning
6 topics
Describe CI/CD for machine learning including the three levels of MLOps maturity from manual processes to fully automated training, validation, and deployment pipelines
Apply ML pipeline orchestration using tools including Kubeflow Pipelines, Apache Airflow, Prefect, and Dagster to define reproducible multi-step training workflows
Apply automated model validation gates including performance threshold checks, data quality validation, fairness metric evaluation, and A/B test readiness checks before deployment
Apply deployment strategies for ML models including blue-green deployments, canary releases, shadow mode testing, and progressive rollouts with automatic rollback triggers
Analyze end-to-end MLOps pipeline design including trigger mechanisms, caching strategies, pipeline versioning, and infrastructure cost optimization for training workflows
Apply feature flag management for ML models including gradual rollout, model version toggling, and how feature flags enable safe experimentation in production ML systems
6
Model Monitoring and Observability
6 topics
Describe model monitoring concepts including data drift, concept drift, prediction drift, and the distinction between statistical drift detection and performance degradation monitoring
Apply data drift detection methods including Population Stability Index, Kolmogorov-Smirnov test, Jensen-Shannon divergence, and multivariate drift detection for high-dimensional features
Apply model performance monitoring including tracking accuracy, latency, throughput, error rates, and custom business metrics in production with alerting thresholds and dashboards
Apply feedback loop design including ground truth collection, delayed label handling, active learning for annotation prioritization, and continuous retraining triggers based on drift signals
Analyze monitoring strategy design including the selection of appropriate drift metrics per feature type, alert fatigue management, and root cause analysis workflows for model degradation
Apply explainability monitoring including tracking feature importance stability, SHAP value drift, and how explanation consistency signals model reliability in production
7
ML Infrastructure
6 topics
Describe ML infrastructure components including GPU and TPU accelerators, distributed training frameworks, cloud ML services, and the compute requirements for training versus inference
Apply containerized ML workflows including Docker for reproducible environments, Kubernetes for orchestration, and GPU scheduling for shared compute clusters
Apply infrastructure as code for ML including Terraform, Pulumi, and cloud-native tools to provision training clusters, model endpoints, and monitoring infrastructure repeatably
Analyze ML infrastructure cost optimization including spot instance strategies, right-sizing GPU instances, training job scheduling, and reserved capacity planning for predictable workloads
Apply ML workload scheduling including priority queues, preemption strategies, resource quotas, and multi-tenant cluster management for shared ML training infrastructure
Describe edge ML deployment including model optimization for mobile and IoT devices, on-device inference frameworks, and the architectural patterns for edge-cloud hybrid ML systems
8
Testing ML Systems
6 topics
Describe ML testing categories including unit tests for data processing, integration tests for pipelines, model quality tests, and infrastructure tests and how they differ from software testing
Apply data validation testing including great expectations, schema validation, distribution assertions, and referential integrity checks for ML training data
Apply model validation testing including invariance tests, directional expectation tests, minimum functionality tests, and behavioral testing to verify model correctness beyond aggregate metrics
Analyze testing strategy design for ML systems including test pyramid adaptation, flaky test management due to model non-determinism, and regression testing across model versions
Apply load testing for ML services including inference latency profiling, throughput benchmarking, and stress testing to establish service level objectives for model endpoints
Apply shadow testing and canary analysis including deploying models in shadow mode to compare outputs against production models before promoting to live traffic
9
ML Governance and Compliance
6 topics
Describe ML governance concepts including model cards, datasheets for datasets, audit trails, and regulatory requirements for ML systems in finance, healthcare, and hiring
Apply model documentation practices including automated model cards, performance disaggregation by demographic group, and intended use specifications for responsible deployment
Apply fairness evaluation including demographic parity, equalized odds, calibration across groups, and how to select appropriate fairness metrics for different application contexts
Analyze the tension between ML system performance, explainability, and regulatory compliance and evaluate strategies for building auditable ML pipelines that satisfy governance requirements
Apply lineage tracking including end-to-end provenance from training data through feature engineering to model prediction and how lineage supports debugging and compliance audits
Describe responsible AI tooling including Fairlearn, AI Fairness 360, What-If Tool, and how organizations integrate fairness tooling into their MLOps pipelines as standard practice
10
ML Platform Design
6 topics
Describe ML platform architecture including self-service model training, centralized feature stores, shared model registries, and how platform teams enable data science productivity
Apply ML platform tooling comparisons including SageMaker, Vertex AI, Azure ML, Databricks, and open-source alternatives to evaluate managed versus self-hosted platform trade-offs
Analyze ML platform adoption challenges including organizational change management, standardization versus flexibility, skill gaps, and the build versus buy decision for ML infrastructure
Apply ML platform observability including tracking platform adoption metrics, identifying bottlenecks in the ML development lifecycle, and measuring time-to-production for new models
Describe the ML platform maturity model including manual ML (level 0), ML pipeline automation (level 1), and CI/CD pipeline automation (level 2) and how organizations progress through stages
Analyze the total cost of ownership for ML platforms including infrastructure costs, engineering time, maintenance burden, and how to justify ML platform investment to business stakeholders
Hands-On Labs
15 labs
~420 min total
Console Simulator
Code Sandbox
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
Scope
Included Topics
- Experiment tracking (MLflow, W&B), data versioning (DVC, Delta Lake), feature stores, model serialization and registry, model serving (TF Serving, Triton, TorchServe), CI/CD for ML (Kubeflow, Airflow), model monitoring and drift detection, ML infrastructure (GPU/TPU, K8s, IaC), ML testing strategies, governance and compliance, ML platform design
Not Covered
- Specific cloud provider ML services in depth (covered in certification tracks)
- Model training algorithms and architecture design (covered in ML/DL domains)
- Data engineering pipeline design (covered in Data Engineering domain)
- Business strategy and ROI analysis for ML projects
- Research ML experimentation patterns
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