Uptrain

To provide an effective and user-friendly solution for model observability, we are excited to announce the following as a part of UpTrain's Latest Feature Release:
* Restructures classes and improves naming conventions for better usability
* Adds histogram views for easy statistical analysis
* Includes Umap visualizations for clear and interactive representation of embeddings
* Adds a practical example of recommendation bias in shopping cart recommendations
* Ensures proper functioning through the addition of thorough tests

With this latest release, UpTrain continues to make strides in streamlining the tool and providing users with better functionality and insights. The histogram views and Umap visualizations offer new ways to analyze and understand data, while the added recommendation bias example provides practical guidance on addressing common challenges in recommender systems.

This UpTrain feature release makes the tool even more user-friendly and has the following features:

- Reduced package requirements for UpTrain installation: Easier for users to get up and running with the tool.
- Improved streamlit dashboard design: The new design offers a more intuitive and user-friendly interface, making it easier for users to navigate and access the features they need.
- Convergence monitors of embeddings: Allows users to track the evolution of embeddings for a given object over time. This can help users understand how their models are evolving and make informed decisions about when to retrain their models.
- Distribution statistics of embeddings: Provides users with statistical properties of embeddings to give them a deeper understanding of their data, models and the convergence rate. This helps them understand the stability of their models and identify potential areas for improvement.
- Measurable Caching: Introduction of this feature into the framework makes it even more efficient and fast.

This is the very first release of the UpTrain package with the following features.

- **Model performance monitoring**: UpTrain tracks the performance of a machine learning model over time, including metrics such as accuracy, precision, recall, and F1 score. It provides visualizations and alerts to help users understand how well the model is performing 📊
- **Data drift detection**: UpTrain uses advanced techniques such as statistical tests and change point detection algorithms to detect any changes in the distribution of data over time. This allows users to identify when their model's performance might be negatively impacted by data drift and take appropriate action 🕵️‍♂️
- **Edge-case checking**: UpTrain uses user-defined signals as well as statistical techniques such as outlier detection to identify data points that fall outside of the typical range of values. These edge cases can be challenging for a machine learning model to predict accurately, and UpTrain allows users to flag and handle these cases separately 🛑
- **Data integrity verification**: UpTrain checks for missing or inconsistent data, duplicate records, and other potential issues that could affect the accuracy of a machine learning model. It also checks for data quality issues such as outliers, missing values, and incorrect data types 🔍
- **Customizable metrics**: UpTrain provides users with the ability to add their own custom metrics to monitor, such as business-specific KPIs. These metrics can be easily added to the dashboard and used alongside other performance metrics to gain a more complete understanding of the model's performance 📈
- **Smart data-point collection**: UpTrain automatically collects data points that fall outside of the typical range of values or that cause data drift, for use in automated retraining of the model. This allows users to constantly improve the performance of their model by retraining it with new, relevant data 🤖