Artificial Inteligence Machine Learning Strategy 
TechCrunch
MARCH 31, 2021
We’re living in a phenomenal moment for machine learning (ML), what Sonali Sambhus , head of developer and ML platform at Square, describes as “the democratization of ML.” ML recruiting strategy. Snehal Kundalkar is the chief technology officer at Valence. Recruiting for ML comes with several challenges.
AWS Machine Learning - AI
APRIL 9, 2025
Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements.
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CIO
JANUARY 9, 2025
Generative artificial intelligence ( genAI ) and in particular large language models ( LLMs ) are changing the way companies develop and deliver software. These autoregressive models can ultimately process anything that can be easily broken down into tokens: image, video, sound and even proteins.
CIO
NOVEMBER 4, 2024
Jeff Schumacher, CEO of artificial intelligence (AI) software company NAX Group, told the World Economic Forum : “To truly realize the promise of AI, businesses must not only adopt it, but also operationalize it.” Most AI hype has focused on large language models (LLMs).
Advertiser: Data Robot
Capitalizing on the incredible potential of AI means having a coherent AI strategy that you can operationalize within your existing processes. The importance of governance in ensuring consistency in the modeling process. How MLOps streamlines machine learning from data to value.
CIO
NOVEMBER 18, 2024
In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. It’s impossible,” says Shadi Shahin, Vice President of Product Strategy at SAS. If the data quality is poor, the generated outcomes will be useless.
CIO
NOVEMBER 27, 2024
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.
Advertiser: Data Robot
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? Why do AI-driven organizations need it?
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.
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