• Understand the importance of ML/LLM observability

• Set up and integrate Arize into ML/LLM pipelines

• Monitor model performance and detect drift in real time

• Visualize embedding spaces and model behavior patterns

• Use Arize to conduct root cause and bias analysis

• Build dashboards to track accuracy, latency, and fairness

• Integrate with frameworks like Hugging Face and LangChain

• Apply NLP observability with token- and segment-level inspection

• Analyze prediction failures using slicing and drill-downs

• Optimize AI workflows through observability-driven insights

Course Syllabus

1. Introduction to ML Observability and Arize AI

2. Setting Up Arize: Account, SDK, and Model Ingestion

3. Core Concepts: Performance, Drift, and Explainability

4. Connecting ML & LLM Pipelines to Arize

5. Building Dashboards: Metrics, Filters, and Visuals

6. Embedding Space Visualizations & LLM Monitoring

7. Detecting Drift Across Features and Model Versions

8. Understanding and Analyzing Model Bias

9. Real-time Root Cause Analysis in Production Models

10. Integrating Arize with Hugging Face and LangChain

11. Use Case: Troubleshooting a Customer Support LLM

12. Best Practices for ML Monitoring and AI Governance

After completing the course, learners will receive a Uplatz Certificate of Completion confirming their ability to monitor and debug ML/LLM models using Arize AI. This certification demonstrates proficiency in observability, fairness, and post-deployment reliability in AI workflows. It is especially useful for MLOps engineers, data scientists, and AI product teams aiming to maintain high-performing and explainable systems. Holding this certificate enhances your credibility in building, deploying, and managing trusted AI solutions.

AI observability is becoming a core component of modern AI deployments. As companies increasingly rely on LLMs and machine learning in production, tools like Arize play a critical role in ensuring quality, compliance, and reliability.

By mastering Arize, you become eligible for roles like:

• ML/LLM Observability Engineer

• MLOps Engineer

• AI Infrastructure Specialist

• Responsible AI Analyst

• Data Scientist (Model Monitoring Focus)

• AI QA & Compliance Lead

These roles are crucial in organizations where AI is embedded in products and services, from e-commerce platforms and fintech systems to healthcare analytics and enterprise software. With Arize, you can confidently address model failures, prevent drift, and ensure ethical AI practices.

1. What is Arize used for?
   Arize is a platform for monitoring, debugging, and optimizing machine learning and LLM models in production.

2. What types of models can Arize monitor?
   Arize supports tabular models, computer vision, NLP, and large language models.

3. How does Arize detect data drift?
   It continuously compares feature distributions between training and live data to identify significant drift.

4. Can Arize help with LLM observability?
   Yes, Arize offers tools for monitoring token-level output, embedding behavior, and prediction patterns in LLMs.

5. What is root cause analysis in Arize?
   It helps pinpoint specific features or segments causing performance degradation or bias.

6. How do you visualize model embeddings in Arize?
   Arize offers 2D and 3D plots to explore similarity, clustering, and semantic meaning of model embeddings.

7. Does Arize support integrations with LangChain or Hugging Face?
   Yes, Arize integrates with popular frameworks to enable seamless monitoring of pipeline models.

8. How does Arize help with fairness and bias analysis?
   It provides slicing tools and demographic filters to evaluate model fairness across groups.

9. Is Arize used only post-deployment?
   No, Arize can be used during model development as well as in real-time production monitoring.

10. Why is model observability important in AI development?
    It ensures performance, trust, and explainability—key for scaling and governing AI responsibly.