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

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

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

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

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

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

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

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.

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

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kurs ist nicht verfügbar in Deutsch (Deutschland)

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Model Deployment and Monitoring

Q: [hidden]

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

Dieser Kurs ist Teil von Spezialisierung „MLOps: Build & Deploy ML Systems“

Dozent: Edureka

Bei Mehr erfahren enthalten

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

7 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

7 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: MLOps (Machine Learning Operations)
Kategorie: Artificial Intelligence
Kategorie: Distributed Computing
Kategorie: Scalability
Kategorie: Cost Reduction
Kategorie: System Monitoring
Kategorie: Continuous Monitoring
Kategorie: Managed Services
Kategorie: Site Reliability Engineering
Kategorie: Feature Engineering
Kategorie: Machine Learning Algorithms
Kategorie: Responsible AI

Werkzeuge, die Sie lernen werden

Kategorie: Amazon CloudWatch
Kategorie: AWS SageMaker
Kategorie: Scikit Learn (Machine Learning Library)
Kategorie: Fraud detection
Kategorie: Pandas (Python Package)
Kategorie: Python Programming
Kategorie: Amazon S3

Wichtige Details

Zertifikat zur Vorlage

Zu Ihrem LinkedIn-Profil hinzufügen

Kürzlich aktualisiert!

Juli 2026

Bewertungen

13 Aufgaben

Unterrichtet in Englisch

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

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Erweitern Sie Ihre Fachkenntnisse

Dieser Kurs ist Teil der Spezialisierung Spezialisierung „MLOps: Build & Deploy ML Systems“

Wenn Sie sich für diesen Kurs anmelden, werden Sie auch für diese Spezialisierung angemeldet.

Lernen Sie neue Konzepte von Branchenexperten
Gewinnen Sie ein Grundverständnis bestimmter Themen oder Tools
Erwerben Sie berufsrelevante Kompetenzen durch praktische Projekte
Erwerben Sie ein Berufszertifikat zur Vorlage

In diesem Kurs gibt es 4 Module

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.

Das ist alles enthalten

11 Videos5 Lektüren4 Aufgaben

11 VideosInsgesamt 53 Minuten

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

5 LektürenInsgesamt 50 Minuten

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

4 AufgabenInsgesamt 33 Minuten

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

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.

Das ist alles enthalten

9 Videos4 Lektüren4 Aufgaben

9 VideosInsgesamt 44 Minuten

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

4 LektürenInsgesamt 40 Minuten

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

4 AufgabenInsgesamt 33 Minuten

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

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.

Das ist alles enthalten

9 Videos4 Lektüren4 Aufgaben

9 VideosInsgesamt 37 Minuten

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

4 LektürenInsgesamt 40 Minuten

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

4 AufgabenInsgesamt 33 Minuten

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

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

Das ist alles enthalten

1 Video1 Lektüre1 Aufgabe

Erwerben Sie ein Karrierezertifikat.

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Dozent

Edureka

250 Kurse228.058 Lernende

von

Edureka

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Häufig gestellte Fragen

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.