Coursera

Agentic AI Performance & Reliability Specialization

Coursera

Agentic AI Performance & Reliability Specialization

Build Reliable Production AI Systems.

Deploy, monitor, and optimize AI models with automated pipelines and real-time performance tracking.

LearningMate

Instructor: LearningMate

Access provided by Hult International Business School

Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Build automated MLOps pipelines for deploying, monitoring, and retraining AI models in production environments

  • Implement real-time anomaly detection and performance monitoring systems with KPI dashboards and automated alerts.

  • Design feedback loops and reproducible workflows to ensure AI reliability and continuous improvement at scale.

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Taught in English
Recently updated!

December 2025

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Specialization - 8 course series

What you'll learn

  • Partition data fairly, monitor models for drift using PSI/KL divergence, and build automated retraining pipelines for reliable, production-grade AI.

Skills you'll gain

Category: Data Integrity
Category: Data Maintenance
Category: Machine Learning
Category: Data Quality
Category: Artificial Intelligence
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: System Monitoring
Category: MLOps (Machine Learning Operations)

What you'll learn

  • Evaluate model optimization trials, build automated CI/CD pipelines, and confidently deploy production-ready machine learning models.

Skills you'll gain

Category: CI/CD
Category: MLOps (Machine Learning Operations)
Category: Model Deployment
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Process Optimization
Category: Performance Analysis
Category: Verification And Validation
Category: Business Metrics
Category: Key Performance Indicators (KPIs)
Category: Continuous Integration
Category: Scalability
Category: Application Deployment
Category: Performance Measurement
Category: Data-Driven Decision-Making
Category: Continuous Deployment
Category: Model Evaluation
Category: DevOps
Category: Automation

What you'll learn

  • Implement real-time anomaly detection to find critical outliers and differentiate true system failures from benign data drift in AI systems.

Skills you'll gain

Category: MLOps (Machine Learning Operations)
Category: Anomaly Detection
Category: Exploratory Data Analysis
Category: Threat Detection
Category: Statistical Analysis
Category: Trend Analysis
Category: Event Monitoring
Category: Performance Tuning
Category: Unsupervised Learning
Category: Time Series Analysis and Forecasting
Category: Real Time Data
Category: System Monitoring
Category: Continuous Monitoring
Category: Statistical Methods

What you'll learn

  • Design automated feedback loops to capture human insights, analyze model performance, and retrain AI to meet specific operational goals.

Skills you'll gain

Category: Model Evaluation
Category: MLOps (Machine Learning Operations)
Category: Statistical Modeling
Category: Statistical Methods
Category: Performance Analysis
Category: Human Machine Interfaces
Category: Applied Machine Learning
Category: Performance Metric
Category: Sampling (Statistics)
Category: Anomaly Detection
Category: Predictive Analytics
Category: Model Deployment
Category: Artificial Intelligence and Machine Learning (AI/ML)

What you'll learn

  • Aggregate agent performance data and apply statistical A/B tests to objectively measure and validate improvements in AI systems.

Skills you'll gain

Category: Statistical Hypothesis Testing
Category: Data Analysis
Category: Statistical Inference
Category: LangChain
Category: Business Intelligence
Category: Agentic systems
Category: Data-Driven Decision-Making
Category: Key Performance Indicators (KPIs)
Category: Performance Metric
Category: AI Workflows
Category: Data Transformation
Category: CrewAI
Category: Correlation Analysis
Category: Business Metrics
Category: Event Monitoring
Category: Statistical Analysis
Category: Descriptive Analytics
Category: Performance Testing
Category: Statistical Methods
Category: Generative AI Agents

What you'll learn

  • Design dashboards and automated alerts, translating complex AI performance data into clear, actionable insights for stakeholders.

Skills you'll gain

Category: Data Visualization
Category: Data Storytelling
Category: Key Performance Indicators (KPIs)
Category: System Monitoring
Category: Continuous Monitoring
Category: Performance Metric
Category: Data Visualization Software
Category: Business Intelligence
Category: Cost Management
Category: Budget Management
Category: Performance Analysis
Category: Decision Making
Category: Dashboard

What you'll learn

  • Develop core data preparation and exploration skills for AI. Implement data validation and visualization to ensure high-quality data for models.

Skills you'll gain

Category: Data Preprocessing
Category: Data Visualization
Category: Performance Tuning

What you'll learn

  • Learners will apply statistical analysis for sampling and build reproducible data workflows using parameterization and data versioning.

Skills you'll gain

Category: Jupyter
Category: Analytical Skills
Category: Data Collection
Category: Data Science
Category: Analytics
Category: Data Analysis
Category: Software Documentation
Category: Sample Size Determination
Category: Data Strategy
Category: Data Management
Category: Statistical Analysis
Category: Version Control
Category: Research and Design
Category: Data-Driven Decision-Making
Category: Data Mining
Category: MLOps (Machine Learning Operations)

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Instructor

LearningMate
214 Courses 12,563 learners

Offered by

Coursera

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