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This is an email from Open Data Science Newsletter, a newsletter by ODSCJournal.

Evaluating ML Models with Azure, Preventing AI Failure, and Interactive Pipelines

Evaluate ML Models with Azure Machine Learning’s Responsible AI Insights

The Responsible AI scorecard is a reporting feature that can also be generated in Azure Machine Learning to create and share reports surfacing key data characteristics, and model performance, and fairness insights.

How To Prevent AI Project Failure

While advances in artificial intelligence have broadened its applications, project complexity has also led to higher rates of AI failure. Here’s why.

Reuse Features with Feature Views on Hopsworks

Learn to develop machine learning models without spending extra resources on computation and development with the new Hopsworks API “feature view.”

Interactive Pipeline and Composite Estimators for Your End-to-End ML Model

In this article, we will discuss how to use scikit-learn pipelines to structure your code using chaining estimators and column transformers while developing an end-to-end machine learning model.

Introduction to Data Version Control

Here’s a step-by-step guide to implementing your own data version control in Python using Hangar.

How to Choose the Right Data Visualization Tools for Your Business

How do you decide what data visualization tools your business needs, and how do you go about figuring out which ones will benefit your business the most?

Can AI-Generated Recipes Shine For Thanksgiving?

Are Thanksgiving feasts going to be made by AI in the future? One food writer decided to test out some AI-generated Thanksgiving recipes.

AI-Powered Algorithms are Helping Manage Washington D.C.

According to a new report from EPIC, Washington D.C. is using AI algorithms to assist with a number of challenges.

University of Iowa Team Looks To Empower Interventional Radiology With AI

Empowered by artificial intelligence, doctors and staff who utilize interventional radiology can get a helping hand thanks to researchers at the University of Iowa.

ODSC East 2023 Call for Speakers & Save the Date

Do you have some interesting data science use cases, research, or projects you want to share with the world? Learn more about how you can speak at ODSC East 2022 this May.

Video of the Week: Re-Trainable MLOps Systems

The original MLOps process is to build a machine learning pipeline that can be retrained after data management → artificial intelligence development → deployment. But most libraries and paid tools are only capable of machine learning pipelines that cannot be retrained. So, we introduce the MLOps service architecture that can automatically add the data, auto annotate, and retrain in a way that the accuracy is improved with AutoML technology.

Upcoming Webinars:

CI/CD for Machine Learning

Wed, Nov 30, 2022, 12:00 PM — 1:00 PM EST

In this talk, you’ll learn how to automatically allocate cloud instances (AWS, Azure, GCP) to train ML models, automatically shut the instance down when training is over, automatically generate reports with graphs and tables in pull/merge requests to summarize your model’s performance using any visualization library, and more.

Form Recognizer 3.0: Document Understanding

Tue, Dec 6, 2022,12:00 PM — 1:00 PM EST

Form Recognizer 3.0 is now Generally available! Join this session to learn more about the new features and services available through Form Recognizer to make document processing and automation even easier and more streamlined.

Using Open Source for Failure Prediction

Thu, Dec 8, 2022, 12:00 PM — 1:00 PM EST

Failure prediction in real time on time series data can be realized with the use of Open Source tools. We will deliver an overall view of how to start with the generation of new raw sensor data (typically captured by an Edge device), and end up with a real-time graph that shows alerts warning that a failure is imminent. There are a number of processes that must be put into place before the stated goal can be fully realized.

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