AI systems in production require continuous monitoring, evaluation, and optimization to ensure reliability, fairness, and efficiency. This course equips professionals with essential knowledge and tools to establish AI observability, evaluate model performance, detect drifts, and optimize AI applications for scalability and robustness.
Skills You'll Acquire
AI Observability
Model Evaluation
Drift Detection
AI Debugging
Performance Optimization
Instructor-led sessions (live or virtual)
Hands-on labs and real-world case studies
Interactive discussions and assessments
Project-based evaluation
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Basic understanding of AI/ML concepts
Familiarity with Python and ML frameworks (TensorFlow/PyTorch)
Exposure to MLOps is beneficial but not mandatory
Understand the fundamentals of AI observability and its importance
Implement monitoring solutions specifically for AI pipelines
Analyze model evaluation metrics and identify performance issues
Detect and mitigate data and concept drift in AI models
Optimize model performance using various tuning techniques
Utilize AI-specific observability tools such as Evidently AI, Arize AI, and WhyLabs
Apply best practices in debugging and troubleshooting AI systems
Curriculum
This Course contains 7 Modules.
What is AI observability and why it matters
Key components: Model Performance, Data Quality, and Explainability
Overview of AI-specific tools: Evidently AI, Arize AI, WhyLabs
Key metrics for classification, regression, and ranking models
Precision, Recall, F1-Score, AUC-ROC, MSE, RMSE, R-squared
Handling imbalanced datasets and selecting appropriate metrics
Setting up AI model monitoring pipelines
Identifying and handling model degradation
Performance dashboards and alerting systems using AI observability platforms
Understanding data drift, concept drift, and model drift
Statistical techniques for detecting drift
Strategies to retrain and recalibrate AI models
Identifying common AI failures and anomalies
Root cause analysis of model performance issues
Strategies for debugging deep learning models
Hyperparameter tuning (Bayesian Optimization, Grid & Random Search)
Model pruning, quantization, and compression
Deploying AI models efficiently with edge and cloud considerations
Real-world examples of AI observability implementations
Hands-on exercises using tools like Evidently AI, Arize AI, and WhyLabs
Group project: Building an AI monitoring and optimization framework
