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Machine learning model serving architectures

Xebia

After months of crunching data, plotting distributions, and testing out various machine learning algorithms you have finally proven to your stakeholders that your model can deliver business value. For the sake of argumentation, we will assume the machine learning model is periodically trained on a finite set of historical data.

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Enhancing customer care through deep machine learning at Travelers

CIO

s unique about the [chief data officer] role is it sits at the cross-section of data, technology, and analytics,â?? s unique about the role is it sits at the cross-section of data, technology, and analytics. Here are some edited excerpts of that conversation. s a unique role and itâ??s s been a great journey.

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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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Getting Machine Learning Projects from Idea to Execution

Harvard Business Review

Machine learning might be the world’s most important general-purpose technology, but it’s notoriously difficult to launch. Outside of Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing?

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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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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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Hugging Face reaches $2 billion valuation to build the GitHub of machine learning

TechCrunch

That consumer bet hasn’t paid off, but the company kept iterating on its natural language processing technology. Due to the success of this libary, Hugging Face quickly became the main repository for all things related to machine learning models — not just natural language processing.

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

Numerous high-profile examples demonstrate the reality that AI is not a default “neutral” technology and can come to reflect or exacerbate bias encoded in human data. How to choose the appropriate fairness and bias metrics to prioritize for your machine learning models.

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Data Science Fails: Building AI You Can Trust

The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology.

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Realizing the Benefits of Automated Machine Learning

While everyone is talking about machine learning and artificial intelligence (AI), how are organizations actually using this technology to derive business value? This white paper covers: What’s new in machine learning and AI. Real-world examples of companies using the DataRobot automated machine learning platform.

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The New Tech Experience: Innovation, Optimization, and Collaboration

Speaker: Paul Weald, Contact Center Innovator

Learn how to streamline productivity and efficiency across your organization with machine learning and artificial intelligence! Embrace automation, collaborate with new technology, and watch how you thrive!

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How AI and ML Can Accelerate and Optimize Software Development and Testing

Speaker: Eran Kinsbruner, Best-Selling Author, TechBeacon Top 30 Test Automation Leader & the Chief Evangelist and Senior Director at Perforce Software

In this session, Eran Kinsbruner will cover recommended areas where artificial intelligence and machine learning can be leveraged. This includes how to: Obtain an overview of existing AI/ML technologies throughout the DevOps pipeline across categories. Realize the value of each of these technologies across DevOps categories.