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Mosaic AI — Answer to full Machine Learning Lifecycle

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Summary

This article discusses the use of LTI Mosaic AI to build an end-to-end machine learning (ML) use case for fraud detection. It covers the entire ML lifecycle, including data collection, wrangling, model development, model deployment, model monitoring, and Explainable AI (XAI). It explains the data collection process, assisted wrangling, notebook templates, model development, model deployment, model monitoring, and XAI. Finally, it mentions that the platform is constantly being improved and provides a link to a nice read on Explainable AI.

Q&As

What is Mosaic AI's role in the machine learning lifecycle?
Mosaic AI provides a rich source of resources to help data scientists, machine learning engineers, and developers to prepare, build, train, and deploy ML models rapidly and with ease.

How is Mosaic AI used to prepare, build, train, and deploy ML models?
Mosaic AI provides a list of connectors to various enterprise storage devices to scan and publish metadata to catalog layer. It also offers assisted wrangling with more than 300 functions, notebook templates, model development, model deployment, model monitoring, and Explainable AI.

What is the data collection process used by Mosaic AI?
The data from different sources is published to the catalog, which helps in identifying and locating data useful for the current project. The dataset published from catalog gets added in project and is displayed in the Dataset tab.

What features does Mosaic AI provide for model monitoring?
Mosaic AI provides model monitoring for any kind of drift it observes due to data or change in the usage of data. It also provides logging capabilities at multiple places to debug the failures at pod level as well as easy tracking of CPU and Memory utilization.

What is Explainable AI and how is it used with Mosaic AI?
Explainable AI (XAI) is used to understand the model in terms of performance matrices, feature importance, PDPs, data drift, and risk profiles. Mosaic AI provides XAI in three sections: Overview, Know Your Data, and What If.

AI Comments

👍 This article provides a great overview of the Mosaic AI platform and how it can be used to quickly and easily build and deploy machine learning models. The author does a great job of breaking down each step of the machine learning lifecycle and explaining it in an easy to understand way.

👎 This article lacks sufficient detail on how to use the Mosaic AI platform. While it provides a brief overview of the platform, it does not offer any real information on how to set up a project or use the platform to build and deploy models.

AI Discussion

Me: It's about Mosaic AI and how it can help with the entire machine learning lifecycle, from data collection to model deployment to model monitoring and explainable AI. It provides resources to help data scientists, machine learning engineers, and developers to prepare, build, train, and deploy ML models quickly and easily.

Friend: That's really interesting. What are the implications of this article?

Me: Well, with the help of Mosaic AI, the machine learning lifecycle can be streamlined and made more efficient. It can help reduce the time and effort required for data collection, wrangling, model development, deployment, and monitoring. Additionally, it can help provide explainable AI to help understand the model better and identify any potential issues. This can help reduce the risk of errors in the machine learning process.

Action items

Research other datasets that could be used for fraud detection and explore how Mosaic AI could be used to analyze them.
Experiment with the different deployment strategies offered by Mosaic AI to determine which is most effective for fraud detection.
Read up on Explainable AI and explore how it could be used to improve the accuracy of fraud detection models.

Technical terms

Machine Learning (ML): A type of artificial intelligence that uses algorithms to learn from data and make predictions.
Lifecycle: The process of developing, deploying, and managing a machine learning model.
Data Collection: The process of gathering data from various sources.
Intelligent Assisted Wrangling: The process of preprocessing and feature engineering data to make it more useful for machine learning models.
Notebook Offerings: A type of development environment that allows users to create and run code in an interactive way.
Model Development: The process of creating a machine learning model.
Model Deployment: The process of making a machine learning model available for use.
Model Monitoring: The process of tracking the performance of a machine learning model over time.
Explainable AI (XAI): A type of artificial intelligence that provides explanations for its decisions and predictions.