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Machine learning for Java developers: Machine learning data pipelines

InfoWorld

The article, Machine learning for Java developers: Algorithms for machine learning , introduced setting up a machine learning algorithm and developing a prediction function in Java. You'll get a quick introduction to Weka, a machine learning framework for Java.

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AWS adds machine learning capabilities to Amazon Connect

CIO

In a bid to help enterprises offer better customer service and experience , Amazon Web Services (AWS) on Tuesday, at its annual re:Invent conference, said that it was adding new machine learning capabilities to its cloud-based contact center service, Amazon Connect. Cloud Computing, Enterprise Applications, Machine Learning

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Machine learning for Java developers: Algorithms for machine learning

InfoWorld

Large language models like ChatGPT and Bard have raised machine learning to the status of a phenomenon. Tech companies are investing heavily in machine learning, so knowing how to train and work with models is becoming essential for developers.

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Arthur.ai machine learning monitoring gathers steam with $42M investment

TechCrunch

It’s widely understood that after machine learning models are deployed in production, the accuracy of the results can deteriorate over time. Company co-founder Adam Wenchel told TechCrunch it’s the largest round ever given to a machine learning monitoring startup. Gow will join the board under the terms of the funding.

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How Banks Are Winning with AI and Automated Machine Learning

By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

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Enhancing customer care through deep machine learning at Travelers

CIO

And we recognized as a company that we needed to start thinking about how we leverage advancements in technology and tremendous amounts of data across our ecosystem, and tie it with machine learning technology and other things advancing the field of analytics. Here are some edited excerpts of that conversation.

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Real-time Data, Machine Learning, and Results: The Evidence Mounts

CIO

From delightful consumer experiences to attacking fuel costs and carbon emissions in the global supply chain, real-time data and machine learning (ML) work together to power apps that change industries. more machine learning use casesacross the company. By Bryan Kirschner, Vice President, Strategy at DataStax.

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How Banks Are Winning with AI and Automated Machine Learning

By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the whitepaper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

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Resilient Machine Learning with MLOps

Today’s economy is under pressure from inflation, rising interest rates, and disruptions in the global supply chain. As a result, many organizations are seeking new ways to overcome challenges — to be agile and rapidly respond to constant change. We do not know what the future holds.

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Intelligent Process Automation: Boosting Bots with AI and Machine Learning

In Data Robot's new ebook, Intelligent Process Automation: Boosting Bots with AI and Machine Learning, we cover important issues related to IPA, including: What is RPA? But in order to reap the rewards of Intelligent Process Automation, organizations must first educate themselves and prepare for the adoption of IPA. What is AI?

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MLOps 101: The Foundation for Your AI Strategy

Many organizations are dipping their toes into machine learning and artificial intelligence (AI). Machine Learning Operations (MLOps) allows organizations to alleviate many of the issues on the path to AI with ROI by providing a technological backbone for managing the machine learning lifecycle through automation and scalability.

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5 Things a Data Scientist Can Do to Stay Current

And more is being asked of data scientists as companies look to implement artificial intelligence (AI) and machine learning technologies into key operations. Fostering collaboration between DevOps and machine learning operations (MLOps) teams. Sharing data with trusted partners and suppliers to ensure top value.

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The Business Value of MLOps

As machine learning models are put into production and used to make critical business decisions, the primary challenge becomes operation and management of multiple models.

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Build Trustworthy AI With MLOps

In our eBook, Building Trustworthy AI with MLOps, we look at how machine learning operations (MLOps) helps companies deliver machine learning applications in production at scale. For businesses that are AI-driven, this trust hinges on the confidence that their AI solution can help them make their most critical decisions.

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Trusted AI 102: A Guide to Building Fair and Unbiased AI Systems

How to choose the appropriate fairness and bias metrics to prioritize for your machine learning models. Download this guide to find out: How to build an end-to-end process of identifying, investigating, and mitigating bias in AI. How to successfully navigate the bias versus accuracy trade-off for final model selection and much more.