# CoreWeave Docs: Monolith AI

> Learn how to deploy, manage, and observe your AI training and inference workloads on CoreWeave.

## Monolith AI

- [Monolith Documentation](https://docs.coreweave.com/monolith-ai.md): Documentation for the Monolith AI platform: Manipulators, platform guides, and release notes

### Manipulators

- [Manipulators](https://docs.coreweave.com/monolith-ai/1814202376/Manipulators.md): Index of manipulator step documentation: Apply, Explore, Import and Export, Model, and Transform.
- [Advanced Tabular Model Options](https://docs.coreweave.com/monolith-ai/1814202569/Advanced+Tabular+Model+Options.md): Advanced options for tabular models: cross validation and hyperparameter optimisation settings.

#### Import and Export

- [Import and Export](https://docs.coreweave.com/monolith-ai/1814202998/Import+and+Export.md): Manipulators for importing and exporting data and models.
- [MF4 Loader](https://docs.coreweave.com/monolith-ai/1874527374/MF4+Loader.md): Import measurement data from MF4 (ASAM MDF4) files into a notebook, with optional channel filtering.

##### Tabular Loader

- [Tabular Loader](https://docs.coreweave.com/monolith-ai/1814204036/Tabular+Loader.md): For selecting raw data files from the File Manager.
- [Tabular - Manipulator configuration](https://docs.coreweave.com/monolith-ai/1814202169/Tabular+-+Manipulator+configuration.md): Import tabular CSV data into a notebook and configure file selection, headers, and separators.
- [Tabular - File selection](https://docs.coreweave.com/monolith-ai/1814203229/Tabular+-+File+selection.md): How to browse, select, deselect, and filter files in the importer file selection dialog.
- [Tabular - Recursive import](https://docs.coreweave.com/monolith-ai/1814202058/Tabular+-+Recursive+import.md): Import files recursively from folders by defining a File Pattern that selects matching files.

##### Global Import

- [Global Import](https://docs.coreweave.com/monolith-ai/1814204090/Global+Import.md): Import published data/models from other notebooks
- [Import Global Model](https://docs.coreweave.com/monolith-ai/1814202833/Import+Global+Model.md): Import a model exported from another notebook in the same team for further use.
- [Import Global Tabular](https://docs.coreweave.com/monolith-ai/1814202211/Import+Global+Tabular.md): Import a tabular dataset exported from another notebook within the same team.

##### Global Export

- [Global Export](https://docs.coreweave.com/monolith-ai/1814204110/Global+Export.md): Publish data and models for use in other notebooks.
- [Export Model](https://docs.coreweave.com/monolith-ai/1814202689/Export+Model.md): Export models from a notebook so they can be imported and used in other notebooks or teams.
- [Export Tabular](https://docs.coreweave.com/monolith-ai/1814203296/Export+Tabular.md): Export tabular data from a notebook so it can be imported and used in other notebooks.

#### Explore

- [Explore](https://docs.coreweave.com/monolith-ai/1814202547/Explore.md): Explore manipulator pages: Data Overview, Info and Notes, Intelligent Explorations, Plotting, Statistics.

##### Data Overview

- [Data Overview](https://docs.coreweave.com/monolith-ai/1814203952/Data+Overview.md): Understand data size and structure
- [Head](https://docs.coreweave.com/monolith-ai/1814202305/Head.md): Shows a tabular preview of the first N rows of a dataset.
- [Quick Overview](https://docs.coreweave.com/monolith-ai/1814202938/Quick+Overview.md): Get a report on rows, columns, data types, column headings, and a tabular preview of a dataset.
- [Unique Count](https://docs.coreweave.com/monolith-ai/1814202602/Unique+Count.md): Count the unique values in a column or unique combinations of values across columns.
- [Value Counts](https://docs.coreweave.com/monolith-ai/1814203100/Value+Counts.md): Counts the frequency of each distinct value of a column in your dataset.

##### Plotting

- [Plotting](https://docs.coreweave.com/monolith-ai/1814203988/Plotting.md): Plot tabular data with the 2D Point Plot and Line Plot steps.
- [2D Point Plot](https://docs.coreweave.com/monolith-ai/1814202647/2D+Point+Plot.md): Create a classic X-Y scatter plot, with optional coloring by a second or third parameter.
- [Line Plot](https://docs.coreweave.com/monolith-ai/1814204318/Line+Plot.md): Create a classic line plot to compare two or more parameters against each other.

##### Info and Notes

- [Info and Notes](https://docs.coreweave.com/monolith-ai/1814204005/Info+and+Notes.md): View metadata and insert notes: Data Types and Note steps.
- [Data Types](https://docs.coreweave.com/monolith-ai/1814203306/Data+Types.md): Evaluate and show the data types of the columns in a tabular dataset.
- [Note](https://docs.coreweave.com/monolith-ai/1814203017/Note.md): Add notes and comments to your notebooks, with formatting options for structure and context

##### Statistics

- [Statistics](https://docs.coreweave.com/monolith-ai/1814203976/Statistics.md): Explore the dataset as a whole with statistics plots.
- [Box and Whisker](https://docs.coreweave.com/monolith-ai/1814202310/Box+and+Whisker.md): Plot the distribution of numerical data as a box and whisker plot.
- [Distribution](https://docs.coreweave.com/monolith-ai/1814202121/Distribution.md): Plot the distribution of numerical columns as histograms or tabulate counts for categorical columns.
- [Plot Missing Data](https://docs.coreweave.com/monolith-ai/1814202063/Plot+Missing+Data.md): Create a heatmap of missing data in a dataset for a quick visual check.

##### Intelligent Explorations

- [Intelligent Explorations](https://docs.coreweave.com/monolith-ai/1814204050/Intelligent+Explorations.md): Visualise correlations between parameters with Intelligent Correlations and Parallel Coordinates
- [Intelligent Correlations](https://docs.coreweave.com/monolith-ai/1814202868/Intelligent+Correlations.md): Summarise one-to-one relationships between pairs of variables as a heat map of correlation strength
- [Parallel Coordinates](https://docs.coreweave.com/monolith-ai/1814203840/Parallel+Coordinates.md): Interactive plot showing each table row as a line across multiple parallel axes.

#### Transform

- [Transform](https://docs.coreweave.com/monolith-ai/1814202764/Transform.md): Manipulators for transforming datasets: edit, filter, restructure, split, and time series.

##### Edit and Create

- [Edit and Create](https://docs.coreweave.com/monolith-ai/1814203774/Edit+and+Create.md): Manipulators for creating new data.
- [Custom SQL Code](https://docs.coreweave.com/monolith-ai/1814202174/Custom+SQL+Code.md): Write and run your own SQL code to transform a tabular dataset in the Monolith platform.
- [Quick Columns](https://docs.coreweave.com/monolith-ai/1814202325/Quick+Columns.md): Add new columns to a dataset or update existing ones with calculations based on existing columns.
- [Rename Columns](https://docs.coreweave.com/monolith-ai/1814204231/Rename+Columns.md): Rename the columns of imported data; changes apply to all steps downstream of this step.
- [Sample Design Space](https://docs.coreweave.com/monolith-ai/1814202949/Sample+Design+Space.md): Create a sampling of the design space from dataset ranges or user-defined parameters.
- [Specify Data Types](https://docs.coreweave.com/monolith-ai/1814202889/Specify+Data+Types.md): Change the data type of columns in your dataset

##### Filter and Clean

- [Filter and Clean](https://docs.coreweave.com/monolith-ai/1814203945/Filter+and+Clean.md): Manipulators for removing unwanted data from datasets.
- [Custom Filter](https://docs.coreweave.com/monolith-ai/1814203812/Custom+Filter.md): Filter a dataset based on column values, with operations for numerical and categorical columns.
- [Filter Category](https://docs.coreweave.com/monolith-ai/1814202607/Filter+Category.md): Filter a dataset based on a single categorical column, keeping only the selected values
- [Find and Replace](https://docs.coreweave.com/monolith-ai/1814203781/Find+and+Replace.md): Replace cells in a dataset within a specified filter, such as filling missing data with a fixed value
- [Random Subset](https://docs.coreweave.com/monolith-ai/1814202020/Random+Subset.md): Create a randomly selected subset of a dataset and control its size as a percentage of the original.
- [Remove Duplicates](https://docs.coreweave.com/monolith-ai/1814201926/Remove+Duplicates.md): Remove duplicate rows from a dataset by checking a single column or a combination of columns.
- [Remove Missing](https://docs.coreweave.com/monolith-ai/1814202894/Remove+Missing.md): Remove rows with missing data from a dataset using Any or All matching across selected columns.

##### Restructure

- [Restructure](https://docs.coreweave.com/monolith-ai/1814203788/Restructure.md): Rearrange tables of data with append, group by, join, and select columns steps
- [Append](https://docs.coreweave.com/monolith-ai/1814203338/Append.md): Combine two datasets that share common columns into a single dataset by stacking them.
- [Group By](https://docs.coreweave.com/monolith-ai/1814202077/Group+By.md): Group a dataset by one or more columns and apply operations like min, max, mean, sum, or count.
- [Join](https://docs.coreweave.com/monolith-ai/1814202884/Join.md): Join two tables into one based on shared columns, using left, right, inner, outer, and exclusive methods
- [Select Columns](https://docs.coreweave.com/monolith-ai/1814202657/Select+Columns.md): Remove selected columns from a dataset or keep only the selected columns.

##### Time Series

- [Time Series](https://docs.coreweave.com/monolith-ai/1814203892/Time+Series.md): Manipulators for transforming time series data.
- [Resample Time Series](https://docs.coreweave.com/monolith-ai/1814203343/Resample+Time+Series.md): Resample time series values along regular time intervals with several interpolation methods.
- [Time Series Feature Extraction](https://docs.coreweave.com/monolith-ai/1814202786/Time+Series+Feature+Extraction.md): Create new feature columns from time series data using derivative, moving average, or low pass filter.

##### Split

- [Split](https://docs.coreweave.com/monolith-ai/1814203795/Split.md): Split manipulators, including Train Test Split.
- [Train Test Split](https://docs.coreweave.com/monolith-ai/1814203239/Train+Test+Split.md): Split a dataset into training and test subsets for model training and evaluation.

#### Model

- [Model](https://docs.coreweave.com/monolith-ai/1814203005/Model.md): Landing page for model steps: anomaly detection, evaluations, processing, and tabular models

##### Anomaly Detection

- [Anomaly Detection](https://docs.coreweave.com/monolith-ai/1814204146/Anomaly+Detection.md): Model steps for anomaly detection, including the Anomaly Detection Model and Finetuner.
- [Anomaly Detection Model & Finetuner](https://docs.coreweave.com/monolith-ai/1814202082/Anomaly+Detection+Model+Finetuner.md): Trains an anomaly detection model to learn expected behaviours from a nominal data set.

##### Models for Tabular Data

- [Models for Tabular Data](https://docs.coreweave.com/monolith-ai/1814204159/Models+for+Tabular+Data.md): Model types available for tabular data, from regression methods to neural networks.
- [Gaussian Process Regression](https://docs.coreweave.com/monolith-ai/1814202848/Gaussian+Process+Regression.md): Train a Gaussian Process Regression model on tabular data and choose or combine kernels.
- [Neural Network](https://docs.coreweave.com/monolith-ai/1814202478/Neural+Network.md): Neural Network model step for predicting numeric values, with advanced parameter options.
- [Linear/Polynomial Regression](https://docs.coreweave.com/monolith-ai/1814202505/Linear+Polynomial+Regression.md): Fit relationships between output and input variables with linear or polynomial regression models.
- [Random Forest Regression](https://docs.coreweave.com/monolith-ai/1814202910/Random+Forest+Regression.md): Train an ensemble of decision trees to predict outputs, with advanced options and memory guidance.
- [Series Model](https://docs.coreweave.com/monolith-ai/1814202843/Series+Model.md): Train a model on series-based data, combining curves and scalar values as inputs and outputs.
- [How Categorical Data is Handled in Tabular Models](https://docs.coreweave.com/monolith-ai/1814205438/How+Categorical+Data+is+Handled+in+Tabular+Models.md): How tabular models automatically encode categorical columns using one-hot encoding.

##### Model Processing

- [Model Processing](https://docs.coreweave.com/monolith-ai/1814204172/Model+Processing.md): Model processing steps: Model Stack & Model Chain and Post Processor.
- [Model Stack & Model Chain](https://docs.coreweave.com/monolith-ai/1814202238/Model+Stack+Model+Chain.md): Wrapper models that combine multiple models into a single model for prediction or optimisation
- [Post Processor](https://docs.coreweave.com/monolith-ai/1814202483/Post+Processor.md): Apply a calculation on a model's inputs or outputs to create a new output as a wrapper around the model

##### Model Evaluations

- [Model Evaluations](https://docs.coreweave.com/monolith-ai/1814204190/Model+Evaluations.md): Index of model evaluation steps such as Error Distribution, Learning Curve, and Predicted Vs Actual.
- [Compare Against Criteria](https://docs.coreweave.com/monolith-ai/1814203022/Compare+Against+Criteria.md): Compare one or multiple models against each other and user-defined accuracy criteria.
- [Compare Performance Metrics](https://docs.coreweave.com/monolith-ai/1814201931/Compare+Performance+Metrics.md): Compare trained model performance quantitatively using metrics like MAE, MSE, R-squared, and Pearson correlation
- [Error Distribution](https://docs.coreweave.com/monolith-ai/1814202559/Error+Distribution.md): Create a histogram of the prediction error for one or multiple models
- [Learning Curve](https://docs.coreweave.com/monolith-ai/1814202564/Learning+Curve.md): Create a learning curve from a trained model to check for over- or underfitting and data needs.
- [Model Evaluation](https://docs.coreweave.com/monolith-ai/1814203199/Model+Evaluation.md): Evaluate cross validation results of a trained model across tabs for hyperparameters, plots, and scores
- [Predicted Vs Actual](https://docs.coreweave.com/monolith-ai/1814203321/Predicted+Vs+Actual.md): Visually compare model predictions on a test set against the true values to assess model quality
- [Validation Plot](https://docs.coreweave.com/monolith-ai/1814203182/Validation+Plot.md): Compare predicted and actual values as line plots against another variable in your dataset.

##### Next Test Recommendations

- [Next Test Recommendations](https://docs.coreweave.com/monolith-ai/1814204012/Next+Test+Recommendations.md): Overview of Next Test Recommendations pages, covering Next Test Recommender Evaluation.
- [Next Test Recommender Evaluation](https://docs.coreweave.com/monolith-ai/1814203062/Next+Test+Recommender+Evaluation.md): Evaluate recommenders on existing data to identify which maximises learning efficiency.

#### Apply

- [Apply](https://docs.coreweave.com/monolith-ai/1814203160/Apply.md): Steps for applying models: predictions, explanations, optimisation, and next test recommendations.

##### Apply - Next Test Recommendations

- [Apply - Next Test Recommendations](https://docs.coreweave.com/monolith-ai/1814204117/Apply+-+Next+Test+Recommendations.md): Steps for applying next test recommendations with a trained model.
- [Next Test Recommender](https://docs.coreweave.com/monolith-ai/1814202418/Next+Test+Recommender.md): Train models, get recommendations for the next tests to perform, and optimise test campaigns.
- [NTR Optimisation](https://docs.coreweave.com/monolith-ai/1814205334/NTR+Optimisation.md): Recommend the next tests to run to reach one or more optimisation targets.

##### Prediction

- [Prediction](https://docs.coreweave.com/monolith-ai/1814204208/Prediction.md): Steps for applying trained models to make predictions.
- [Series Prediction](https://docs.coreweave.com/monolith-ai/1814203067/Series+Prediction.md): Get predictions from a Series Model for an entire dataset in a single step.
- [Anomaly Detector & Visualiser](https://docs.coreweave.com/monolith-ai/1814201943/Anomaly+Detector+Visualiser.md): Apply an anomaly detection model to a dataset and inspect raw and aggregated anomaly scores.
- [Curve Prediction](https://docs.coreweave.com/monolith-ai/1814203027/Curve+Prediction.md): Use a trained model to predict and plot the response curve of an output against one of the inputs.
- [Dataset Prediction](https://docs.coreweave.com/monolith-ai/1814202673/Dataset+Prediction.md): Add model predictions and optional error columns to an entire dataset in one step.
- [Evaluate Anomaly Detection Models](https://docs.coreweave.com/monolith-ai/1814203807/Evaluate+Anomaly+Detection+Models.md): Compare multiple Anomaly Detection models quantitatively and identify optimal thresholds using RoC curves
- [Surface Prediction](https://docs.coreweave.com/monolith-ai/1814202733/Surface+Prediction.md): Use a trained model to predict and plot the response surface of an output against two inputs.

##### Explanation

- [Explanation](https://docs.coreweave.com/monolith-ai/1814204043/Explanation.md): Analyses that explain model behaviour: Explain Predictions and Sensitivity Analysis
- [Explain Predictions](https://docs.coreweave.com/monolith-ai/1814202413/Explain+Predictions.md): Use SHAP analysis to understand the relationships between the inputs and outputs of your models
- [Sensitivity Analysis](https://docs.coreweave.com/monolith-ai/1814201972/Sensitivity+Analysis.md): Visualise how much impact each model input has on outputs using Sobol, Morris, or FAST methods.

##### Optimisation

- [Optimisation](https://docs.coreweave.com/monolith-ai/1814204097/Optimisation.md): Landing page for Min/Max Optimisation and Targeted Optimisation steps
- [Min/Max Optimisation](https://docs.coreweave.com/monolith-ai/1814203145/Min+Max+Optimisation.md): Find the inputs that maximise or minimise a selected model output.
- [Targeted Optimisation](https://docs.coreweave.com/monolith-ai/1814202254/Targeted+Optimisation.md): Find model inputs that produce outputs as close as possible to user-defined target values.

### Platform

- [Platform](https://docs.coreweave.com/monolith-ai/1814201999/Platform.md): Guides to the Monolith platform: getting started, navigation, file and data management, plots, and teams
- [Apps](https://docs.coreweave.com/monolith-ai/1814204842/Apps.md): Create an App from a notebook to automate scheduled workflow runs and track run history.

#### Getting Started

- [Getting Started](https://docs.coreweave.com/monolith-ai/1814202623/Getting+Started.md): Guides for getting started with the Monolith platform.
- [Platform Registration](https://docs.coreweave.com/monolith-ai/1814202718/Platform+Registration.md): Steps to register a user account on your company's Monolith platform URL, with email verification.
- [Platform Sections](https://docs.coreweave.com/monolith-ai/1814203077/Platform+Sections.md): The main sections of the Monolith platform: My Content, File Manager, Teams, and the user menu.
- [Getting started with your own data](https://docs.coreweave.com/monolith-ai/1814202978/Getting+started+with+your+own+data.md): Upload data in the File Manager and organise notebooks and folders from the My Content page.
- [How to structure data before uploading it to the platform](https://docs.coreweave.com/monolith-ai/1814201989/How+to+structure+data+before+uploading+it+to+the+platform.md): Data structure requirements for files uploaded to the Monolith platform, with examples of common issues.
- [Tutorials](https://docs.coreweave.com/monolith-ai/1814202749/Tutorials.md): Overview of the tutorials available on the Getting Started tab of the Monolith platform.

#### Teams

- [Teams](https://docs.coreweave.com/monolith-ai/1814202092/Teams.md): Overview of Teams pages, covering creating and deleting teams and what teams are.
- [What are Teams?](https://docs.coreweave.com/monolith-ai/1814202275/What+are+Teams.md): Teams isolate data, models, notebooks and dashboards; how to access, create, and manage a team.
- [Creating and deleting teams](https://docs.coreweave.com/monolith-ai/1814202586/Creating+and+deleting+teams.md): Only Monolith team members can create and delete teams; request changes through a support ticket.

#### Managing files, data & models

- [Managing files, data & models](https://docs.coreweave.com/monolith-ai/1814203326/Managing+files+data+models.md): Pages on managing files, data, and models: management, CSV export, and the File Manager.
- [File Manager](https://docs.coreweave.com/monolith-ai/1814202754/File+Manager.md): Upload raw files to team folders through the File Manager tab for import into notebooks.
- [Data & Model Management](https://docs.coreweave.com/monolith-ai/1814202920/Data+Model+Management.md): Export and reuse data and models across notebooks and manage them with the Data Manager.
- [Export data to CSV](https://docs.coreweave.com/monolith-ai/1814202532/Export+data+to+CSV.md): Export a tabular dataset from Monolith as CSV files and uncompress the downloaded .gz files.

#### Plots

- [Plots](https://docs.coreweave.com/monolith-ai/1814202630/Plots.md): Plot behaviour on the platform: data point limits and static images replacing dynamic plots.
- [Number of data points limited to 10,000](https://docs.coreweave.com/monolith-ai/1814202488/Number+of+data+points+limited+to+10+000.md): Plots are limited to 10,000 randomly sampled data points by default to keep notebooks responsive.
- [Static images replace dynamic plots](https://docs.coreweave.com/monolith-ai/1814202537/Static+images+replace+dynamic+plots.md): Dynamic plots are replaced by static images to save browser resources; click the image to re-enable them.

#### Best Machine Learning Practices

- [Best Machine Learning Practices](https://docs.coreweave.com/monolith-ai/1814202515/Best+Machine+Learning+Practices.md): Guides on machine learning best practices in the Monolith platform.
- [How to read the training curve of a Neural Network?](https://docs.coreweave.com/monolith-ai/1814202801/How+to+read+the+training+curve+of+a+Neural+Network.md): Interpret typical Neural Network error curves and how to adjust the model in each case.
- [How to use Hyperparameter Optimisation?](https://docs.coreweave.com/monolith-ai/1814201994/How+to+use+Hyperparameter+Optimisation.md): How to tune model hyperparameters manually or with automated exhaustive or randomised search.
- [Neural Network uncertainty compared with model error](https://docs.coreweave.com/monolith-ai/1814202104/Neural+Network+uncertainty+vs.+model+error.md): How neural network uncertainty from dropout differs from model error, and how the two relate.
- [What does the 'moving average' calculate?](https://docs.coreweave.com/monolith-ai/1814202284/What+does+the+moving+average+calculate.md): How the moving average option in Time Series Feature Extraction computes a centred average.
- [How to read a learning curve?](https://docs.coreweave.com/monolith-ai/1814203251/How+to+read+a+learning+curve.md): What a learning curve shows and how to spot underfitting, suitable, and overfitting models.
- [Why does Explain prediction return different results?](https://docs.coreweave.com/monolith-ai/1814203279/Why+does+Explain+prediction+return+different+results.md): Why the Explain Predictions step is non-deterministic and how to reduce variation in its results.
- [Cross Validation](https://docs.coreweave.com/monolith-ai/1814203155/Cross+Validation.md): How K-fold and Group K-fold cross validation work and how to interpret their scores.
- [Why do I sometimes see long decimal numbers?](https://docs.coreweave.com/monolith-ai/1814205300/Why+do+I+sometimes+see+long+decimal+numbers.md): Why results like 3.8999999999 appear instead of 3.9, and how floating-point storage causes it.

#### Navigating the platform

- [Navigating the platform](https://docs.coreweave.com/monolith-ai/1814202423/Navigating+the+platform.md): Guides for navigating the platform, including keyboard shortcuts
- [Keyboard Shortcuts](https://docs.coreweave.com/monolith-ai/1814202806/Keyboard+Shortcuts.md): Keyboard shortcuts for navigating, selecting and editing in Monolith on macOS and Windows.

### Release Notes

- [Release Notes](https://docs.coreweave.com/monolith-ai/1814202925/Release+Notes.md): Release notes for Monolith platform versions.
- [Release Notes for v1.1](https://docs.coreweave.com/monolith-ai/1814202723/Release+Notes+for+v1.1.md): Release notes for Monolith platform v1.1, released 8 May 2024.
- [Release Notes for v1.2](https://docs.coreweave.com/monolith-ai/1814201955/Release+Notes+for+v1.2.md): Release notes for Monolith platform version 1.2, released 6 June 2024.
- [Release Notes for v1.3](https://docs.coreweave.com/monolith-ai/1814203219/Release+Notes+for+v1.3.md): Release notes for v1.3: Tutorials in Getting Started and new Explore, Transform, Model and Apply manipulators.
- [Release Notes for v1.4](https://docs.coreweave.com/monolith-ai/1814202289/Release+Notes+for+v1.4.md): Release notes for Monolith platform v1.4, released 10 September 2024.
- [Release Notes for v1.5](https://docs.coreweave.com/monolith-ai/1814202863/Release+Notes+for+v1.5.md): Release notes for v1.5: Next Test Recommender, step numbering and URLs, and help text links.
- [Release Notes for v1.7](https://docs.coreweave.com/monolith-ai/1814202811/Release+Notes+for+v1.7.md): Release notes for v1.7, including Azure support and new model manipulators.
- [Release Notes for v1.8](https://docs.coreweave.com/monolith-ai/1814202366/Release+Notes+for+v1.8.md): Release notes for Monolith platform v1.8, released 13 February 2025.
- [Release Notes for V1.9](https://docs.coreweave.com/monolith-ai/1814203742/Release+Notes+for+V1.9.md): Release notes for Monolith platform V1.9: Custom Filter, Evaluate Anomaly Detection Models, Parallel Coordinates.
- [Release Notes for v1.10](https://docs.coreweave.com/monolith-ai/1814204858/Release+Notes+for+v1.10.md): Release notes for v1.10: Apps scheduled runs, NTR Evaluation test plan output, and plot limit increase.
- [Release Notes for v1.11](https://docs.coreweave.com/monolith-ai/1814200327/Release+Notes+for+v1.11.md): Model sharing across teams, HighCharts beta plotting, bug fixes, and known issues in v1.11.
- [Release Notes for v1.13](https://docs.coreweave.com/monolith-ai/1814200326/Release+Notes+for+v1.13.md): Release notes for v1.13 covering new features and bug fixes.
- [Release Notes for v1.14](https://docs.coreweave.com/monolith-ai/1814205232/Release+Notes+for+v1.14.md): New features, improvements, and bug fixes in Monolith platform v1.14.
- [Release Notes for v1.15](https://docs.coreweave.com/monolith-ai/1814200330/Release+Notes+for+v1.15.md): Release notes for Monolith platform v1.15: new features, improvements, and bug fixes.
- [Release Notes for v1.16](https://docs.coreweave.com/monolith-ai/1814200329/Release+Notes+for+v1.16.md): Bug fixes shipped in Monolith platform release v1.16.
- [Release Notes - V1S - 1.23.26 - Dec 10 14:19](https://docs.coreweave.com/monolith-ai/1814205383/Release+Notes+-+V1S+-+1.23.26+-+Dec+10+14+19.md): Release notes working page for V1S version 1.23.26, listing the Jira issues in the release.