Machine learning: Refactor "advanced time series analysis" section

Author: amotl

docs/_include/card/timeseries-intro.md

@@ -22,11 +22,11 @@ for fast aggregations.
 Combine time series data with document data: CrateDB is all you need.
 ::::
 
-::::{grid-item-card} {material-outlined}`lightbulb;2em` Time Series: Advanced SQL
+::::{grid-item-card} {material-outlined}`lightbulb;2em` Time Series: Analyzing Weather Data
 :link: guide:timeseries-analysis-weather
 :link-type: ref
 :class-footer: text-smaller
-CrateDB provides enhanced features for querying time series data.
+CrateDB provides advanced SQL features for querying time series data.
 
 :::{rubric} What's Inside
 :::

docs/solution/machine-learning/index.md

@@ -20,7 +20,8 @@ and other applications.
 
 These applications can answer questions about specific sources of information,
 for example using techniques like Retrieval Augmented Generation (RAG).
-RAG is a technique for augmenting LLM knowledge with additional data.
+RAG is a technique for augmenting LLM knowledge with additional data,
+often private or real-time.
 :::
 
 ::::{grid} 2
@@ -52,6 +53,7 @@ using SQL: CrateDB is all you need.
 :link-type: ref
 LangChain is a framework for developing applications powered by language models,
 written in Python, and with a strong focus on composability.
+It supports retrieval-augmented generation (RAG).
 +++
 The LangChain adapter for CrateDB provides support to use CrateDB as a vector
 store database, to load documents using LangChain’s DocumentLoader, and also
@@ -61,10 +63,13 @@ supports LangChain’s conversational memory subsystem.
 ::::
 
 
-## Text-to-SQL and MCP
+(text-to-sql)=
+## Text-to-SQL
 
-The adapters enumerated below integrate CrateDB for Text-to-SQL purposes,
+:::{div} sd-text-muted
+Integrate CrateDB with Text-to-SQL solutions,
 and provide MCP and AI enterprise data integrations.
+:::
 
 ::::{grid} 2
 :gutter: 4
@@ -76,7 +81,7 @@ Text-to-SQL is a technique that converts natural language queries into SQL
 queries that can be executed by a database.
 :::
 
-:::{grid-item-card} MCP
+:::{grid-item-card} All about MCP
 :link: mcp
 :link-type: ref
 The Model Context Protocol (MCP), is an open protocol that enables seamless
@@ -92,8 +97,54 @@ MindsDB is the platform for customizing AI from enterprise data.
 ::::
 
 
+## Time series analysis
+
+:::{div} sd-text-muted
+Load and analyze data from database systems for
+time series anomaly detection and forecasting.
+:::
+
+::::{grid} 2
+:gutter: 4
+
+:::{grid-item-card} Statistical analysis and visualization on huge datasets
+:link: r-tutorial
+:link-type: ref
+Learn how to create a machine learning pipeline using R and CrateDB.
+:::
+
+:::{grid-item-card} Regression analysis with pandas and scikit-learn
+:link: scikit-learn
+:link-type: ref
+Use pandas and scikit-learn to run a regression analysis within a Jupyter Notebook.
+:::
+
+:::{grid-item-card} Build model for predictive maintenance with TensorFlow
+:link: tensorflow-tutorial
+:link-type: ref
+Learn how to build a machine learning model that will predict whether
+a machine will fail within a specified time window in the future.
+:::
+
+:::{grid-item-card} Advanced time series analysis with MLflow and PyCaret
+:link: ml-timeseries
+:link-type: ref
+Learn how to conduct advanced data analysis on large time series datasets
+with CrateDB, MLflow, and PyCaret:
+Anomaly detection and forecasting, time series decomposition,
+Exploratory data analysis (EDA).
+:::
+
+::::
+
+
 ## MLOps and model training
 
+:::{div} sd-text-muted
+CrateDB supports MLOps procedures through adapters to best-of-breed software
+frameworks.
+:::
+
 :::{div}
 Training a machine learning model, running it in production, and maintaining
 it, requires a significant amount of data processing and bookkeeping
@@ -103,16 +154,14 @@ Machine Learning Operations [MLOps] is a paradigm that aims to deploy and
 maintain machine learning models in production reliably and efficiently,
 including experiment tracking, and in the spirit of continuous development
 and DevOps.
-
-CrateDB supports MLOps procedures through adapters to best-of-breed software
-frameworks.
 :::
 
 ::::{grid} 2
 :gutter: 4
 
 :::{grid-item-card} MLflow
 :link: mlflow
+:link-type: ref
 MLflow is an open-source platform to manage the whole ML lifecycle,
 including experimentation, reproducibility, deployment, and a central
 model registry.
@@ -122,36 +171,29 @@ CrateDB can be used as a storage database for the MLflow Tracking subsystem.
 
 :::{grid-item-card} PyCaret
 :link: pycaret
+:link-type: ref
 PyCaret is an open-source, low-code machine learning library for Python
 that automates machine learning workflows (AutoML).
 +++
 CrateDB can be used as a storage database for training and production datasets.
 :::
 
-::::
-
-
-## Time-series anomaly detection and forecasting
-
-Load and analyze data from database systems.
-
-::::{grid} 2
-:gutter: 4
-
-:::{grid-item-card} Statistical analysis and visualization on huge datasets
-:link: r-tutorial
-Learn how to create a Machine Learning pipeline using R and CrateDB.
+:::{grid-item-card} Advanced time series analysis with MLflow and PyCaret
+:link: ml-timeseries
+:link-type: ref
+:columns: 12
+Learn how to conduct advanced data analysis on large time series datasets
+with CrateDB, MLflow, and PyCaret.
++++
+**What's inside:** Anomaly detection and forecasting, time series decomposition,
+exploratory data analysis (EDA).
 :::
 
-:::{grid-item-card} Regression analysis with pandas and scikit-learn
-:link: scikit-learn-tutorial
-Use pandas and scikit-learn to run a regression analysis within a Jupyter Notebook.
-:::
+::::
 
-:::{grid-item-card} Use TensorFlow and CrateDB for predictive maintenance
-:link: tensorflow-tutorial
-Learn how to build a machine learning model that will predict whether
-a machine will fail within a specified time window in the future.
-:::
 
-::::
+:::{toctree}
+:maxdepth: 1
+:hidden:
+time-series
+:::

docs/topic/timeseries/advanced.md renamed to docs/solution/machine-learning/time-series.md

@@ -1,16 +1,17 @@
+(ml-timeseries)=
 (timeseries-advanced)=
 (timeseries-analysis)=
-
 # Advanced Time Series Analysis
 
+:::{div} sd-text-muted
 Learn how to conduct advanced data analysis on large time series datasets
 with CrateDB.
+:::
 
 {tags-primary}`Anomaly detection`
 {tags-primary}`Forecasting / Prediction`
 {tags-primary}`Time series decomposition`
 {tags-primary}`Exploratory data analysis`
-{tags-primary}`Metadata integration`
 
 :::{include} /_include/links.md
 :::
@@ -169,102 +170,7 @@ identify patterns.
 ::::
 
 
-(timeseries-analysis-metadata)=
-## Metadata Integration
-
-CrateDB is particularly effective when you need to combine time series data
-with metadata, for instance, in scenarios where data like sensor readings
-or log entries, need to be augmented with additional context for more
-insightful analysis. See also [](#document).
-
-CrateDB supports effective time series analysis with fast aggregations, a
-rich set of built-in functions, and [JOIN](inv:crate-reference#sql_joins)
-operations.
-
-:::{rubric} Examples
-:::
-
-::::{info-card}
-
-:::{grid-item}
-:columns: auto 9 9 9
-**Analyzing Device Readings with Metadata Integration**
-
-This tutorial illustrates how to augment time series data with metadata, in
-order to enable more comprehensive analysis. It uses a time series dataset that
-captures various device readings, such as battery, CPU, and memory information.
-
-{{ '{}(#timeseries-objects)'.format(tutorial) }}
-:::
-
-:::{grid-item}
-:columns: 3
-
-{tags-primary}`Rich Time Series`
-{tags-primary}`Metadata`
-
-{tags-secondary}`SQL`
-:::
-
-::::
-
-
-(timeseries-analysis-sql)=
-## SQL Analysis
-
-CrateDB offers enhanced features for analysing time series data.
-
-**Examples**
-
-::::{info-card}
-
-:::{grid-item}
-:columns: 9
-**Analyzing Weather Data**
-
-Run aggregations with gap filling / interpolation, using common
-table expressions (CTEs) and LAG / LEAD window functions.
-
-Find maximum values using the MAX_BY aggregate function, returning
-the value from one column based on the maximum or minimum value of another
-column within a group.
-
-{{ '{}(#timeseries-analysis-weather)'.format(tutorial) }}
-:::
-
-:::{grid-item}
-:columns: 3
-
-{tags-primary}`Aggregations`
-{tags-primary}`Time series`
-
-{tags-secondary}`SQL`
-:::
-
-::::
-
-
-(timeseries-visualization)=
-## Visualization
-
-Similar to EDA, just applying [data and information visualization] can yield
-significant insights into the characteristics of your data. By using
-best-of-breed data visualization tools, initial data exploration is
-mostly your first encounter with the data.
-
-:::{rubric} Examples
-:::
-
-:::{include} /_include/card/timeseries-explore.md
-:::
-
-:::{include} /_include/card/timeseries-datashader.md
-:::
-
-
-
 [anomaly]: https://en.wikipedia.org/wiki/Anomaly_(natural_sciences)
-[Data and information visualization]: https://en.wikipedia.org/wiki/Data_and_information_visualization
 [Decomposition of time series]: https://en.wikipedia.org/wiki/Decomposition_of_time_series
 [Exploratory data analysis (EDA)]: https://en.wikipedia.org/wiki/Exploratory_data_analysis
 [forecasting]: https://en.wikipedia.org/wiki/Forecasting

docs/topic/timeseries/fundamentals.md

@@ -2,6 +2,14 @@
 (timeseries-fundamentals)=
 # Time Series Fundamentals with CrateDB
 
+:::{div} sd-text-muted
+Learn how to conduct fundamental data analysis on large time series datasets
+with CrateDB.
+:::
+
+{tags-primary}`Metadata integration`
+{tags-primary}`Advanced SQL for time series`
+
 :::{include} /_include/links.md
 :::
 
@@ -19,6 +27,9 @@ for your own explorations.
 :::{include} /_include/card/timeseries-explore.md
 :::
 
+:::{include} /_include/card/timeseries-datashader.md
+:::
+
 :::{include} /_include/card/timeseries-dask.md
 :::
 

docs/topic/timeseries/index.md

@@ -110,7 +110,6 @@ Optimizing storage for historic time series data.
 :hidden:
 
 Fundamentals <fundamentals>
-Advanced <advanced>
 video
 Long Term Store <longterm>
 :::