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Artificial Intelligence in practice

CIO

The world has known the term artificial intelligence for decades. Developing AI When most people think about artificial intelligence, they likely imagine a coder hunched over their workstation developing AI models. Today, integrating AI into your workflow isn’t hypothetical, it’s MANDATORY.

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Taktile makes it easier to leverage machine learning in the financial industry

TechCrunch

Meet Taktile , a new startup that is working on a machine learning platform for financial services companies. This isn’t the first company that wants to leverage machine learning for financial products. They could use that data to train new models and roll out machine learning applications.

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CatalyzeX grabs $1.64M seed to help developers find right machine learning model

TechCrunch

Machine learning is exploding, and so are the number of models out there for developers to choose from. While Google can help, it’s not really designed as a model search engine. working on machine learning projects, where they saw the kinds of research challenges they are attempting to fix with CatalyzeX.

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Leveraging AMPs for machine learning

CIO

Such a large-scale reliance on third-party AI solutions creates risk for modern enterprises. It’s hard for any one person or a small team to thoroughly evaluate every tool or model. However, the road to AI victory can be bumpy. Yet, today’s data scientists and AI engineers are expected to move quickly and create 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. It is based on interviews with MLOps user companies and several MLOps experts. Which organizational challenges affect MLOps implementations.

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The key to operational AI: Modern data architecture

CIO

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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Dulling the impact of AI-fueled cyber threats with AI

CIO

Take for instance large language models (LLMs) for GenAI. While LLMs are trained on large amounts of information, they have expanded the attack surface for businesses. Artificial Intelligence: A turning point in cybersecurity The cyber risks introduced by AI, however, are more than just GenAI-based.

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Machine Learning for Builders: Tools, Trends, and Truths

Speaker: Rob De Feo, Startup Advocate at Amazon Web Services

Machine learning techniques are being applied to every industry, leveraging an increasing amount of data and ever faster compute. But that doesn’t mean machine learning techniques are a perfect fit for every situation (yet). Where machine learning is a perfect fit to drive your business, and where it has further to go.

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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). Download this comprehensive guide to learn: What is MLOps? How can MLOps tools deliver trusted, scalable, and secure infrastructure for machine learning projects?

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Future Focus: Constructing Unshakeable Stability in Your Manufacturing Supply Chain

Speaker: Jay Black, Senior Account Executive

We’ve all heard the buzzwords to describe new supply chain trends: resiliency, sustainability, AI, machine learning. But what do these really mean today? Over the past few years, manufacturing has had to adapt to and overcome a wide variety of supply chain trends and disruptions to stay as stable as possible.