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Become an MLOps Engineer
Build automated ML pipelines that move models from notebook to reliable, monitored production systems.
CREATED BY
R
Rhea B. [PLACEHOLDER] ★ 4.9
Product Manager at FinUPI | 6+ years of experience
About this Path
Designed for engineers with ML or DevOps backgrounds who want to own the full model lifecycle in production. You will instrument training pipelines, build feature stores, deploy models with canary rollouts, and catch drift before it hurts business metrics. Employers expect hands-on Kubeflow, MLflow, and Terraform experience at this level.
Path Overview
Advanced LevelCertificate of CompletionAbout 68 hours to completeEnglish language20+ curated videosLearn online at your own pace5 modules with resourcesGamified & interactive
Path Curriculum
MLflow tracking and model registry
Log parameters, metrics, and artifacts; promote models through staging to production.
Kubeflow Pipelines end-to-end
Build containerized DAG pipelines with artifact passing, caching, and retry policies.
Data versioning with DVC
Version datasets alongside code, push to S3 remotes, and reproduce any experiment.
Reproducibility and lineage
Trace any production prediction back to the exact dataset version and training run.
Training-serving skew root causes
Identify and eliminate the most common source of silent ML bugs in production.
Feast feature store setup
Define feature views, materialize to Redis online store, and serve at low latency.
Streaming features with Kafka and Flink
Compute real-time aggregations and push to the online store within seconds.
Feature governance and discovery
Catalog features with metadata, ownership, and freshness SLAs for multi-team reuse.
Containerizing models with BentoML
Package model, pre/post-processing, and dependencies into a single OCI image.
High-performance serving with Triton
Run concurrent model ensembles on GPU with dynamic batching and TensorRT optimization.
Canary and shadow deployments
Route 5% of traffic to a new model, compare metrics, and promote or roll back automatically.
Online A/B experimentation
Assign users to model variants, collect outcome metrics, and run statistical significance tests.
Data drift with Evidently AI
Generate HTML reports and Grafana panels for feature distribution shifts over time.
Model performance monitoring
Track precision, recall, and business KPIs with delayed ground-truth ingestion pipelines.
Automated retraining triggers
Fire Kubeflow pipelines when drift scores or metric thresholds breach defined SLOs.
Alerting and incident response
Wire PagerDuty alerts to model quality signals and define runbooks for on-call engineers.
Terraform for ML infrastructure
Provision SageMaker domains, Vertex AI pipelines, and S3/GCS buckets with version-controlled IaC.
Kubernetes for ML workloads
Configure GPU node pools, resource quotas, and Karpenter autoscaling for training jobs.
CI/CD for ML with GitHub Actions
Build pipelines that retrain, evaluate, and gate model promotion on every data or code change.
Cost optimization and resource scheduling
Use spot instances, preemptible VMs, and job queuing to cut training costs by 60 percent.
What you'll learn
- ✓Design end-to-end ML pipelines on Kubeflow Pipelines and Apache Airflow with artifact lineage and reproducibility guarantees.
- ✓Build and serve a feature store using Feast or Tecton to eliminate training-serving skew across teams.
- ✓Package and deploy models with BentoML, Seldon Core, and Triton Inference Server behind a production-grade API.
- ✓Implement A/B testing, shadow deployments, and canary rollouts for zero-downtime model updates.
- ✓Monitor model performance and detect data drift using Evidently AI, Whylogs, and Grafana dashboards.
- ✓Provision MLOps infrastructure as code using Terraform and Helm on AWS SageMaker and GCP Vertex AI Pipelines.