> ## Documentation Index
> Fetch the complete documentation index at: https://docs.coreweave.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Release Notes for v1.1

> Release notes for Monolith platform v1.1, released 8 May 2024.

*Release date: 8 May 2024*

**Step change in platform stability and performance**

Over the past few months, our development team has undergone a major effort to improve the stability and performance of the platform. New software infrastructure now enables Monolith to handle an order of magnitude larger datasets (tens of millions of rows) with greater reliability.

**New manipulators**

Over the next months, we are excited to progressively migrate functionality to use this new infrastructure. For this first version, the following manipulators are available:

* **Import & Export:** [Tabular](/monolith-ai/1814203229/Tabular+-+File+selection), [Import Global Model](/monolith-ai/1814202833/Import+Global+Model), [Import Global Tabular](/monolith-ai/1814202211/Import+Global+Tabular), [Export Model](/monolith-ai/1814202689/Export+Model), [Export Tabular](/monolith-ai/1814203296/Export+Tabular)
* **Explore:** [2D Point Plot](/monolith-ai/1814202647/2D+Point+Plot), [Distribution](/monolith-ai/1814202121/Distribution), Line Plot, [Note](/monolith-ai/1814203017/Note), [Plot Missing Data](/monolith-ai/1814202063/Plot+Missing+Data)
* **Transform:** [Append](/monolith-ai/1814203338/Append), [Custom SQL Code](/monolith-ai/1814202174/Custom+SQL+Code), [Join](/monolith-ai/1814202884/Join), [Random Subset](/monolith-ai/1814202020/Random+Subset), [Resample Time Series](/monolith-ai/1814203343/Resample+Time+Series), [Train Test Split](/monolith-ai/1814203239/Train+Test+Split)
* **Model:** [Anomaly Detection Model & Finetuner](/monolith-ai/1814202082/Anomaly+Detection+Model+Finetuner)
* **Apply:** [Anomaly Detector and Visualiser](/monolith-ai/1814201943/Anomaly+Detector+Visualiser)
* ***Other:*** Tabular Data Explorer, [Export CSV](/monolith-ai/1814202532/Export+data+to+CSV)

**Use case for Anomaly Detection**

With our Anomaly Detection, you can auto-detect erroneous sensor measuremnts, to trust data quality and avoid wasting time collecting erroneous data. Find out more here: [https://www.monolithai.com/use-case/test-data-validation](https://www.monolithai.com/use-case/test-data-validation)