Agentic AI Performance & Reliability

Analyze Agent Performance: Build and Test is an intermediate course for data analysts, ML engineers, and developers tasked with optimizing AI systems. In a world where agentic AI is increasingly common, it is not enough to build an agent—you must prove its effectiveness. This course equips you with the data-driven skills to measure, monitor, and improve AI agents built with frameworks like LangChain, Autogen, and CrewAI. You will learn to transform raw, noisy logs into actionable KPIs by applying data aggregation techniques with SQL and dbt. Through hands-on labs, you will design and execute controlled A/B experiments, comparing agent versions to identify meaningful improvements. You will master core statistical methods, including the Chi-square test, to determine whether your results are statistically significant or just random chance. You will be able to move beyond correlation to causation, making objective, evidence-based recommendations on deploying agent enhancements.

Automate, Analyze, and AI Feedback is an intermediate-level course for MLOps professionals and data scientists who need to build AI systems that do not just launch, but last. In the real world, even the best models degrade over time due to model drift. This course teaches you to combat this by creating automated, self-improving systems that learn from operational experience. You will learn to design and deploy Human-in-the-Loop (HITL) pipelines that identify low-confidence predictions, route them for expert human review, and schedule automated retraining with the new, high-quality data. Moving beyond simple accuracy, you will master advanced model evaluation techniques. Through hands-on labs, you will generate and analyze Precision-Recall (PR) curves, apply resampling methods to ensure your model generalizes well, and select the optimal decision threshold that balances competing business objectives, like maximizing recall while minimizing false alarms. This course will equip you to build resilient MLOps systems that turn human expertise into a continuous source of model improvement.

"Clean, Analyze, and Visualize Your Data" is an intermediate course designed for aspiring AI and data professionals who understand that world-class models are built on high-quality data. In this course, you will move beyond theory and gain hands-on experience in the essential, practical skills of data preparation and exploration. You will learn to implement systematic data cleaning and validation routines using industry-standard tools like Pandera to ensure your datasets are reliable and ready for processing. Through guided labs in a Jupyter environment, you will master statistical visualization and dimensionality reduction techniques, such as t-SNE, to transform complex, high-dimensional data into clear, interpretable plots. These visualizations will empower you to uncover hidden patterns, identify anomalies, and diagnose issues—like misrouted data clusters—that could impact model accuracy. By the end of this course, you will not just know how to clean data, but you will understand how to analyze and visualize it to derive insights, ensuring your AI development is built on a solid, well-understood foundation.

Detect AI Anomalies: Real-Time Outliers is an intermediate course for MLOps engineers and data scientists tasked with ensuring AI systems are reliable in production. Static alerts fail when data is dynamic, leaving systems vulnerable to silent failures. This course teaches you to build an intelligent early warning system that catches critical issues before they escalate. You will learn to apply statistical methods like Z-score and Exponentially Weighted Moving Average (EWMA) on streaming data to detect sudden outliers with dynamic thresholds. You will then go beyond simple statistics, using unsupervised learning models like Isolation Forest to uncover subtle, complex anomalies that other methods miss. Through hands-on labs, you will master the crucial skill of contextual analysis—learning to differentiate a true system failure from benign data drift. You will tune model parameters to minimize false positives, reduce alert fatigue, and build the robust monitoring pipelines that are the foundation of modern MLOps.

Evaluate and Reproduce Data Findings Fast is an intermediate-level course designed for data scientists, analysts, and ML/AI practitioners who need to ensure their analytical work is both efficient and trustworthy. In today’s fast-paced environment, analyses that cannot be easily reproduced create bottlenecks, erode confidence, and slow down team innovation. This course equips you with the essential skills to tackle two critical questions: "Have we collected enough data?" and "Can others trust and replicate our findings?" You will work through hands-on labs, real-world case studies, and interactive exercises to master the core principles of analytical rigor. You will learn to apply statistical power analysis to make strategic decisions about sample sizes, preventing wasted resources on excessive data collection. Furthermore, you will build fully reproducible workflows from the ground up using industry-standard tools, including parameterizing Jupyter notebooks with Papermill and managing datasets with Data Version Control (DVC). By the end of this course, you will be able to move beyond simple scripts to deliver robust, transparent, and automated analytical projects. Whether you are justifying a data strategy to stakeholders or ensuring your model can be validated by peers, this course provides the practical foundation needed to accelerate data-driven work and build a culture of trust and reproducibility.

Your high-accuracy ML model performs beautifully on the test set but fails silently in production. This is model drift, the unspoken crisis where models trained on yesterday’s data are unprepared for today's reality. This course, Partition & Monitor AI Models Effectively, is for data scientists and ML engineers who know deployment is just the beginning. You will move beyond model building and into model reliability, creating robust AI systems that stand the test of time. Master the three pillars of MLOps reliability. Learn fair data partitioning with stratified and time-series splits to prevent data leakage and ensure honest evaluation. Implement continuous monitoring to detect data and concept drift using metrics like Population Stability Index (PSI) and KL Divergence. Finally, design automated retraining pipelines, creating self-healing systems that adapt to new data with minimal intervention. Through hands-on labs, you will build a Model Reliability Toolkit, proving your ability to maintain production-grade AI. Stop building disposable models and start engineering AI systems that deliver lasting value by owning the entire model lifecycle.

Visualize and Alert AI Performance KPIs is an intermediate course designed for data analysts, ML engineers, and product managers responsible for the operational health of AI systems. In the world of AI, a model's success is not just its accuracy—it is its cost, latency, and real-world impact. This course teaches you how to translate complex performance data into clear, actionable insights for any stakeholder. You will learn to move beyond cluttered dashboards by applying data storytelling principles to design effective visualizations, transforming confusing charts into compelling narratives that drive decisions. Through hands-on labs, you will master the art of creating proactive monitoring systems. You will learn to define critical KPIs, set precise, meaningful thresholds for cost and performance, and configure automated alert rules in business intelligence tools that notify your team of issues in real-time. By the end of this course, you will be able to build dashboards that empower leadership and create an automated defense that protects your AI systems from budget overruns and performance degradation.