Specialization & Learning Path · 2026-07-18
Gradient to Production: MLOps & Model Serving
Coursera
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Course 1/15Optimize ML Dev: Version, Reproduce, and Save
Verify credential ↗2026-07-17Grade: 100%ansrsource instructorsID: 62M3MFPZR56Z
Version ControlReproducibilityML Development
Course 2/15Build Testable Python Packages for AI
Verify credential ↗2026-07-17Grade: 97.50%ansrsource instructorsID: 01G8V3NVX44B
Python PackagingAutomated Testingpytest
Course 3/15Debug ML Code: Fix, Trace & Evaluate
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DebuggingTracingModel Evaluation
Course 4/15Engineer, Validate, and Govern ML Data
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Data EngineeringData ValidationData Governance
Course 5/15Orchestrate, Analyze, and Evaluate ML Pipelines
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ML PipelinesApache AirflowOrchestration
Course 6/15Automate ML Pipelines for Peak Performance
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Pipeline AutomationPerformance OptimizationMLOps
Course 7/15Evaluate, Analyze, and Model Performance
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Performance MetricsBenchmarkingModel Evaluation
Course 8/15Develop Production-Ready ML APIs with MLOps
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FastAPIAPI DesignModel Serving
Course 9/15Deploy & Optimize ML Services Confidently
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Model DeploymentLoad TestingLocust
Course 10/15Deploy, Manage, and Orchestrate Your Models
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DockerKubernetesContainer Orchestration
Course 11/15Automate and Evaluate ML Pipeline Tests
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Regression TestingCI/CDGitHub Actions
Course 12/15Deconstruct AI: Complex ML Problems
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Problem DecompositionDecision IntelligenceML System Design
Course 13/15Validate, Analyze, and Monitor ML Models
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Model ValidationDrift DetectionModel Monitoring
Course 14/15Integrate, Scale, and Monitor ML Microservices
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MicroservicesScalabilityObservability
Course 15/15Document AI: Project & API Writing
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Software DocumentationAPI DocumentationTechnical Writing
Overview
A 15-course intermediate specialization covering the full production ML lifecycle: data pipelines, testable Python packages, model validation, inference APIs, containers, orchestration, CI/CD, monitoring, and documentation.
Practical application
Built production-oriented engineering discipline for deploying reliable ML services with FastAPI, Docker, Kubernetes, Airflow, automated tests, observability, and operational performance benchmarks.