SEO description update

docs/book/README.md

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 ---
-description: Open-source ML observabilty course.
+description: Free Open-source ML observability course for data scientists and ML engineers by Evidently AI.
 ---
 
 # Welcome!

docs/book/ml-observability-course/module-1-introduction/ml-lifecycle.md

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+---
+description: What can go wrong with data and machine learning services in production. Data quality issues, data drift, and concept drift.
+---
+
 # 1.1. ML lifecycle. What can go wrong with ML in production?
 
 {% embed url="https://www.youtube.com/watch?v=8I89FY2eelM&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=2" %}

docs/book/ml-observability-course/module-1-introduction/ml-monitoring-architectures.md

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+---
+description: ML monitoring architectures for backend and frontend and what to consider when choosing between them.
+---
+
 # 1.5. ML monitoring architectures
 
 {% embed url="https://www.youtube.com/watch?v=VVO6QFVbwTU&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=6" %}

docs/book/ml-observability-course/module-1-introduction/ml-monitoring-metrics.md

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+---
+description: A framework to organize ML monitoring metrics. Software system health, data quality, ML model quality, and business KPIs.
+---
+
 # 1.3. ML monitoring metrics. What exactly can you monitor?
 
 {% embed url="https://www.youtube.com/watch?v=DCFvZvpDks0&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=4" %}

docs/book/ml-observability-course/module-1-introduction/ml-monitoring-observability.md

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+---
+description: What ML monitoring is, the challenges of production ML monitoring, and how it differs from ML observability.
+---
+
 # 1.2. What is ML monitoring and observability?
 
 {% embed url="https://www.youtube.com/watch?v=Wfphz6TUikM&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=3" %}

docs/book/ml-observability-course/module-1-introduction/ml-monitoring-setup.md

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+---
+description: Key considerations for ML monitoring setup. Service criticality, retraining cadence, reference dataset, and ML monitoring architecture.
+---
+
 # 1.4. Key considerations for ML monitoring setup
 
 {% embed url="https://www.youtube.com/watch?v=LnfL9Nu0tm4&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=5" %}

docs/book/ml-observability-course/module-1-introduction/readme.md

@@ -1,5 +1,5 @@
 ---
-description: Introduction to ML monitoring and observability.
+description: Key concepts of machine learning monitoring and observability and how they fit in the ML lifecycle.
 ---
 
 # Module 1. Introduction

docs/book/ml-observability-course/module-2-ml-monitoring-metrics/data-drift-deep-dive.md

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+---
+description: A deep dive into data drift detection methods, how to choose the right approach for your use case, and what to do when the drift is detected.
+---
+
 # 2.7. Deep dive into data drift detection [OPTIONAL]
 
 {% embed url="https://www.youtube.com/watch?v=N47SHSP6RuY&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=13" %}

docs/book/ml-observability-course/module-2-ml-monitoring-metrics/data-prediction-drift-code-practice.md

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+---
+description: A code example walkthrough of detecting data drift and creating a custom method for drift detection using Evidently.
+---
+
 # 2.8. Data and prediction drift in ML [CODE PRACTICE]
 
 {% embed url="https://www.youtube.com/watch?v=oO1K4CaWxt0&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=14" %}

docs/book/ml-observability-course/module-2-ml-monitoring-metrics/data-prediction-drift-in-ml.md

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+---
+description: What data and prediction drift is, and how to detect distribution drift using statistical methods and rule-based checks.
+---
+
 # 2.6. Data and prediction drift in ML
 
 {% embed url="https://www.youtube.com/watch?v=bMYcB_5gP4I&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=12" %}

docs/book/ml-observability-course/module-2-ml-monitoring-metrics/data-quality-code-practice.md

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+---
+description: A code example walkthrough of data quality evaluation using Evidently Reports and Test Suites.
+---
+
 # 2.5. Data quality in ML [CODE PRACTICE]
 
 {% embed url="https://www.youtube.com/watch?v=_HKGrW2mVdo&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=11" %}

docs/book/ml-observability-course/module-2-ml-monitoring-metrics/data-quality-in-ml.md

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+---
+description: Types of production data quality issues, how to evaluate data quality, and interpret data quality metrics.
+---
+
 # 2.4. Data quality in machine learning
 
 {% embed url="https://www.youtube.com/watch?v=IRbmQGqzVZo&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=10" %}

docs/book/ml-observability-course/module-2-ml-monitoring-metrics/evaluate-ml-model-quality.md

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+---
+description: How to evaluate ML model quality directly and use early monitoring to detect potential ML model issues.
+---
+
 # 2.1. How to evaluate ML model quality
 
 {% embed url="https://www.youtube.com/watch?v=7Y819MAQTDg&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=7" %}

docs/book/ml-observability-course/module-2-ml-monitoring-metrics/ml-model-quality-code-practice.md

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+---
+description: A code example walkthrough of ML model quality evaluation using Python and the open-source Evidently library.
+---
+
 # 2.3. Evaluating ML model quality [CODE PRACTICE]
 
 {% embed url="https://www.youtube.com/watch?v=QWLw_lJ29k0&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=9" %}

docs/book/ml-observability-course/module-2-ml-monitoring-metrics/ml-quality-metrics-classification-regression-ranking.md

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+---
+description: Commonly used ML quality metrics for classification, regression, and ranking problems.
+---
+
 # 2.2. Overview of ML quality metrics. Classification, regression, ranking
 
 {% embed url="https://www.youtube.com/watch?v=4_LOXDWxCbw&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=8" %}

docs/book/ml-observability-course/module-2-ml-monitoring-metrics/readme.md

@@ -1,5 +1,5 @@
 ---
-description: Model quality, data quality, data drift for structured data.
+description: This module covers different aspects of the production ML model performance. You will learn how to apply data quality, model quality, and data drift metrics for structured data.
 ---
 
 # Module 2: ML monitoring metrics: model quality, data quality, data drift

docs/book/ml-observability-course/module-3-ml-monitoring-for-unstructured-data/introduction-nlp-llm-monitoring.md

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+---
+description: How to evaluate model quality for NLP and LLMs and monitor text data without labels using raw data, embeddings, and descriptors.
+---
+
 # 3.1. Introduction to NLP and LLM monitoring
 
 {% embed url="https://www.youtube.com/watch?v=tYA0h3mPeZE&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=15" %}

docs/book/ml-observability-course/module-3-ml-monitoring-for-unstructured-data/monitoring-data-drift-on-raw-text-data.md

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+---
+description: How to detect and evaluate raw text data drift using domain classifier and topic modeling.
+---
+
 # 3.2. Monitoring data drift on raw text data
 
 {% embed url="https://www.youtube.com/watch?v=wHyXSyVg5Ag&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=16" %}

docs/book/ml-observability-course/module-3-ml-monitoring-for-unstructured-data/monitoring-data-drift-with-descriptors.md

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+---
+description: What text descriptors are and how to use them to monitor text data quality and data drift.
+---
+
 # 3.3. Monitoring text data quality and data drift with descriptors
 
 {% embed url="https://www.youtube.com/watch?v=UwWGxyCHQSw&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=17" %}

docs/book/ml-observability-course/module-3-ml-monitoring-for-unstructured-data/monitoring-embeddings-drift.md

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+---
+description: Strategies for monitoring embedding drift using distance metrics, model-based drift detection, and share of drifted components.
+---
+
 # 3.4. Monitoring embeddings drift
 
 {% embed url="https://www.youtube.com/watch?v=0XtABbNYU7U&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=18" %}

docs/book/ml-observability-course/module-3-ml-monitoring-for-unstructured-data/monitoring-multimodal-datasets.md

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+---
+description: Strategies for monitoring data quality and data drift in multimodal datasets.
+---
+
 # 3.6. Monitoring multimodal datasets
 
 {% embed url="https://www.youtube.com/watch?v=b0a1iMlHgEs&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=20" %}

docs/book/ml-observability-course/module-3-ml-monitoring-for-unstructured-data/monitoring-text-data-code-practice.md

@@ -1,3 +1,7 @@
+---
+description: A code example walkthrough of unstructured data evaluations using the open-source Evidently Python library.
+---
+
 # 3.5. Monitoring text data [CODE PRACTICE]
 
 {% embed url="https://www.youtube.com/watch?v=RIultWCjYXo&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=19" %}

docs/book/ml-observability-course/module-3-ml-monitoring-for-unstructured-data/readme.md

@@ -1,5 +1,5 @@
 ---
-description: Monitoring NLP, LLM and embeddings.
+description: This module covers evaluating and monitoring the production performance for models that use unstructured data, including NLP, LLMs, and embeddings.
 ---
 
 # Module 3: ML monitoring for unstructured data

docs/book/ml-observability-course/module-4-designing-effective-ml-monitoring/choosing-ml-monitoring-deployment-architecture.md

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+---
+description: ML monitoring architectures and choosing the right architecture for your use case.
+---
+
 # 4.7. How to choose the ML monitoring deployment architecture
 
 {% embed url="https://youtu.be/Q1NUCDZFRbU?si=26GhKBdhFAIzxBgi" %}

docs/book/ml-observability-course/module-4-designing-effective-ml-monitoring/custom-metrics-evidently-code-practice.md

@@ -1,3 +1,7 @@
+---
+description: A code example walkthrough of creating a custom metric using the Evidently Python library.
+---
+
 # 4.6. Implementing custom metrics in Evidently [OPTIONAL]
 
 {% embed url="https://youtu.be/uEyoP-sPhyc?si=7hwr4LaJIeBZ-YLD" %}

docs/book/ml-observability-course/module-4-designing-effective-ml-monitoring/custom-metrics-ml-monitoring.md

@@ -1,3 +1,7 @@
+---
+description: Types of custom metrics. Business or product metrics, domain-specific metrics, and weighted metrics.
+---
+
 # 4.5. Custom metrics in ML monitoring
 
 {% embed url="https://youtu.be/PrFuzKLM66I?si=68EF7tepIyXxyMig" %}

docs/book/ml-observability-course/module-4-designing-effective-ml-monitoring/how-to-choose-reference-dataset-ml-monitoring.md

@@ -1,3 +1,7 @@
+---
+description: What a reference dataset is in ML monitoring, how to choose one for drift detection, and when to use multiple references.
+---
+
 # 4.4. How to choose a reference dataset in ML monitoring
 
 {% embed url="https://youtu.be/42J-C4WmkZc?si=Av1gwZXAkBZXDT70" %}

docs/book/ml-observability-course/module-4-designing-effective-ml-monitoring/how-to-prioritize-monitoring-metrics.md

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+---
+description: ML monitoring depth, metrics to collect when building a monitoring system, and how to prioritize them.
+---
+
 # 4.2. How to prioritize ML monitoring metrics
 
 {% embed url="https://youtu.be/jCXO4uuMHbs?si=9ss6_nK2bbLGq8Ph" %}

docs/book/ml-observability-course/module-4-designing-effective-ml-monitoring/logging-ml-monitoring.md

@@ -1,3 +1,7 @@
+---
+description: What a good ML monitoring system is, and how to set up the logging architecture to capture metrics for further analysis.
+---
+
 # 4.1. Logging for ML monitoring
 
 {% embed url="https://youtu.be/CtUsDcA3tB0?si=RNDR2uRZ7wc8NwxB" %}

docs/book/ml-observability-course/module-4-designing-effective-ml-monitoring/readme.md

@@ -1,5 +1,5 @@
 ---
-description: Key questions to consider when customizing ML monitoring for your model.
+description: This module reviews how to set up an ML monitoring system, considering the model risks, criticality, and deployment scenario.
 ---
 
 # Module 4: Designing effective ML monitoring

docs/book/ml-observability-course/module-4-designing-effective-ml-monitoring/when-to-retrain-ml-models.md

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+---
+description: Scheduled and trigger-based retraining and what to consider when making the retaining decision.
+---
+
 # 4.3. When to retrain machine learning models
 
 {% embed url="https://youtu.be/oqyyVp-t5A8?si=u-DeeyxCx9fBjs1_" %}