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Bringing an AI Product to Market

O'Reilly on Data

In this article, we turn our attention to the process itself: how do you bring a product to market? Without clarity in metrics, it’s impossible to do meaningful experimentation. Experimentation should show you how your customers use your site, and whether a recommendation engine would help the business. Identifying the problem.

Marketing 362
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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. It analyzes historical data and news articles, confirming a possible market correction.

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How Do Super Rookies Start Learning Data Analysis?

FineReport

When it comes to data analysis, from database operations, data cleaning, data visualization , to machine learning, batch processing, script writing, model optimization, and deep learning, all these functions can be implemented with Python, and different libraries are provided for you to choose. From Google.

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Of Muffins and Machine Learning Models

Cloudera

In this article, we explore model governance, a function of ML Operations (MLOps). We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI. The complete list is shown below: Model Lineage .

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The New Improved and Open GraphDB

Ontotext

By taking the open source approach, the Workbench can address a wider spectrum of use-cases, creating a higher value for clients and increasing the likelihood that specific non-generic features exist and have been developed to address the real-world problems facing the optimization of semantic data processing and management. The Plugins.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. Not only is data larger, but models—deep learning models in particular—are much larger than before. However, the concept is quite abstract.

IT 346
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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.