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Article: Agile Development Applied to Machine Learning Projects

InfoQ Culture Methods

Machine learning is a powerful new tool, but how does it fit in your agile development? Developing ML with agile has a few challenges that new teams coming up in the space need to be prepared for - from new roles like Data Scientists to concerns in reproducibility and dependency management. By Jay Palat.

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Transform the modern data center: From today to the future

CIO

A seismic shift is underway in the evolution of the data center, driven by a variety of converging factors. These include the accelerating transition to cloud platforms, the growth of hybrid and remote work models, and the rapid adoption of artificial intelligence (AI) and machine learning (ML) technologies across every industry.

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The Power of Machine Learning in the Cloud: Transforming Business Operations

Mentormate

In recent years, the fusion of machine learning (ML) and cloud computing has ushered in a new era of innovation and efficiency for businesses across industries. But what exactly is machine learning, and how does it intersect with the vast capabilities of cloud computing? What is Machine Learning?

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Data replication holds the key to hybrid cloud effectiveness

CIO

At the heart of every business decision, regardless of industry or sector, is data. A hybrid cloud approach offers a huge swath of benefits for organizations, from a boost in agility and resiliency to eliminating data siloes and optimizing workloads. There’s more to data than just adopting hybrid cloud.

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Resilient Machine Learning with MLOps

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

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Unlocking the Power of AI with a Real-Time Data Strategy

CIO

By George Trujillo, Principal Data Strategist, DataStax Increased operational efficiencies at airports. To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making.

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MVP versus EVP: Is it time to introduce ethics into the agile startup model?

TechCrunch

However, today’s startups need to reconsider the MVP model as artificial intelligence (AI) and machine learning (ML) become ubiquitous in tech products and the market grows increasingly conscious of the ethical implications of AI augmenting or replacing humans in the decision-making process.