Will I learn model governance?

Yes. You will explore approval workflows, lifecycle management, compliance, bias, and fairness checks .

Does the course cover model explainability?

Yes. You will learn how to generate and interpret SHAP and LIME explanations.

Will I work with production monitoring?

Yes. You will monitor model performance, data quality, operational metrics, and data drift.

Does the course include continuous learning?

Yes. It covers incremental retraining, online learning, and active learning.

Will I learn cost optimization?

Yes. You will analyze ML costs and define SLAs, SLOs, and operational dashboards.

What can I do after completing the course?

You will be able to design and operate scalable, governed, explainable, and reliable enterprise ML systems.

Is this course is part of any specialization?

Yes, this course is the part of the specialization MLOps: Build & Deploy ML Systems .

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Model Deployment and Monitoring

Q: Which tools are covered?

The course includes Amazon SageMaker, SageMaker Feature Store, SageMaker Clarify, SHAP, and LIME.

This course is part of MLOps: Build & Deploy ML Systems Specialization

Instructor: Edureka

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Explain how feature pipelines and feature stores ensure consistency between model training and real-time inference.
Analyze how feature stores and distributed training improve production efficiency.
Evaluate how governance, explainability, and monitoring support trustworthy machine learning systems.
Apply continuous learning, cost optimization, and service-level objectives to improve operational reliability.

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Build your subject-matter expertise

This course is part of the MLOps: Build & Deploy ML Systems Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

This course provides an advanced exploration of MLOps, focusing on how enterprise machine learning systems are scaled, governed, optimized, monitored, and continuously improved in production.

Through hands-on demonstrations and practical exercises, learners will use Amazon SageMaker, SageMaker Feature Store, SageMaker Clarify, SHAP, LIME, distributed training, hyperparameter tuning, monitoring dashboards, and active learning techniques to create dependable production workflows. By the end of this course, learners will be able to: - Compare managed ML platforms with self-managed infrastructure - Design scalable feature stores and distributed training processes - Implement model approval, governance, bias, and fairness checks - Monitor model performance, data quality, and drift - Apply continuous learning and cost optimization strategies This course is designed for experienced ML engineers, MLOps engineers, AI engineers, platform engineers, and data scientists who want to build and operate enterprise-scale ML systems. Prior knowledge of MLOps, cloud platforms, Kubernetes, model deployment, and monitoring is recommended.

Build a strong foundation in managed ML platforms and scalable ML infrastructure by learning how production ML workflows are migrated, orchestrated, and optimized. Explore how managed platforms reduce operational overhead, support feature reuse, and enable distributed training. Apply these concepts through hands-on exercises to build reliable and scalable production ML systems.

What's included

11 videos5 readings4 assignments

11 videosTotal 53 minutes

Specialization Overview4 minutes
Course Introduction4 minutes
Cloud Platforms Comparison5 minutes
Demonstration: Migrate Fraud Detection from Kubeflow to SageMaker6 minutes
Demonstration: SageMaker's Integrated MLOps Features7 minutes
SageMaker Feature Store Architecture3 minutes
Demonstration: Set Up SageMaker Feature Store for Fraud Detection4 minutes
Demonstration: Feature Versioning and Data Quality Monitoring5 minutes
Distributed Training and HPO3 minutes
Run Distributed Training with SageMaker6 minutes
Hyperparameter Optimization at Scale6 minutes

5 readingsTotal 50 minutes

Course Syllabus10 minutes
Evaluating Platform Readiness for Enterprise MLOps10 minutes
Designing Reliable Feature Stores for Production ML Systems10 minutes
Choosing the Right Training Strategy for Large-Scale ML Workloads10 minutes
Module Summary: Managed ML Platforms and Scaling ML Infrastructure10 minutes

4 assignmentsTotal 33 minutes

Knowledge Check: Managed ML Platforms and Scaling ML Infrastructure15 minutes
Practice Quiz: Cloud-Native ML Platforms vs. Self-Managed Systems6 minutes
Practice Quiz: Managed Feature Stores and Real-Time Feature Serving6 minutes
Practice Quiz: Automated Training Pipelines and Distributed Training6 minutes

Build practical skills in model governance, explainability, and production monitoring by learning how ML systems are approved, audited, explained, and monitored in production environments. Explore how governance workflows, bias detection, explainability methods, failure debugging, and drift monitoring improve trust, compliance, and reliability. Apply these concepts through hands-on activities to manage model approvals, detect fairness issues, explain predictions, debug failures, and monitor production ML performance.

What's included

9 videos4 readings4 assignments

9 videosTotal 44 minutes

Model Governance Frameworks4 minutes
Demonstration: Implement Model Approval Workflow in SageMaker5 minutes
Demonstration: Bias and Fairness Detection with SageMaker3 minutes
SHAP and LIME for Model Explainability4 minutes
Demonstration: Generate SHAP Explanations for Fraud Predictions7 minutes
Demonstration: Interpret and Export SHAP Explanations for Fraud Predictions3 minutes
Demonstration: Debug Model Failures and Root Cause Analysis6 minutes
ML-Specific Monitoring Metrics4 minutes
Demonstration: Set Up Comprehensive Model Monitoring Dashboard7 minutes

4 readingsTotal 40 minutes

Building Trustworthy Model Governance Workflows for Production ML10 minutes
Turning Model Explanations into Actionable Production Insights10 minutes
Monitoring Data, Models, and Business Impact Together10 minutes
Module Summary: Model Governance, Explainability, and Monitoring in Production10 minutes

4 assignmentsTotal 33 minutes

Knowledge Check: Model Governance, Explainability, and Monitoring in Production15 minutes
Practice Quiz: Model Governance and Lifecycle Management6 minutes
Practice Quiz: Explainability and Model Debugging in Production6 minutes
Practice Quiz: Production Model Monitoring and Data Quality6 minutes

Develop practical expertise in production patterns and continuous learning strategies for enterprise ML systems. Learn how ensemble architectures, model routing, online learning, active learning, cost optimization, and SLOs support reliable model operations at scale. Apply these concepts to build multi-model systems, implement incremental retraining, optimize production costs, and define operational dashboards for production-ready ML systems.

What's included

9 videos4 readings4 assignments

9 videosTotal 37 minutes

Ensemble and Multi-Model Architectures3 minutes
Demonstration: Build an Ensemble Fraud Detection System5 minutes
Demonstration: Save and Validate the Ensemble Fraud Detection System3 minutes
Online Learning and Active Learning4 minutes
Demonstration: Implement Incremental Model Retraining5 minutes
Demonstration: Active Learning for Cost-Effective Labeling4 minutes
Cost Optimization and SLAs3 minutes
Demonstration: Cost Analysis and Optimization3 minutes
Demonstration: SLOs and Operational Dashboards5 minutes

4 readingsTotal 40 minutes

Deciding When to Use Ensemble Models in Production ML10 minutes
Progressive Delivery Patterns for ML Models10 minutes
Inference Optimization Techniques for Production ML10 minutes
Module Summary: Advanced Production Patterns and Continuous Learning10 minutes

4 assignmentsTotal 33 minutes

Knowledge Check: Advanced Production Patterns and Continuous Learning15 minutes
Practice Quiz: Multi-Model Systems and Model Orchestration6 minutes
Practice Quiz: Online Learning and Continuous Retraining6 minutes
Practice Quiz: Cost Optimization and Operational Excellence6 minutes

This final module assesses your ability to design reliable enterprise fraud detection systems using managed ML, governance, monitoring, explainability, retraining, and cost optimization.

What's included

1 video1 reading1 assignment

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Instructor

Edureka

225 Courses202,769 learners

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Frequently asked questions

To learn how enterprise ML systems are scaled, governed, monitored, and continuously improved.

It is designed for experienced ML engineers, MLOps engineers, AI engineers, and data scientists.

Knowledge of MLOps, cloud platforms, Kubernetes, model deployment, and monitoring is recommended.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.