CIO
NOVEMBER 14, 2024
Data scientists and AI engineers have so many variables to consider across the machine learning (ML) lifecycle to prevent models from degrading over time. Explainability is also still a serious issue in AI, and companies are overwhelmed by the volume and variety of data they must manage.
TechCrunch
NOVEMBER 4, 2022
Called OpenBioML , the endeavor’s first projects will focus on machine learning-based approaches to DNA sequencing, protein folding and computational biochemistry. Stability AI’s ethically questionable decisions to date aside, machine learning in medicine is a minefield. Image Credits: OpenBioML.
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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.
Cloudera
DECEMBER 9, 2024
Were thrilled to announce the release of a new Cloudera Accelerator for Machine Learning (ML) Projects (AMP): Summarization with Gemini from Vertex AI . The post Introducing Accelerator for Machine Learning (ML) Projects: Summarization with Gemini from Vertex AI appeared first on Cloudera Blog.
Advertiser: Data Robot
Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations.
CIO
NOVEMBER 5, 2024
Leveraging machine learning and AI, the system can accurately predict, in many cases, customer issues and effectively routes cases to the right support agent, eliminating costly, time-consuming manual routing and reducing resolution time to one day, on average. One example is toil.
CIO
JANUARY 9, 2025
A striking example of this can already be seen in tools such as Adobe Photoshop. Before LLMs and diffusion models, organizations had to invest a significant amount of time, effort, and resources into developing custom machine-learning models to solve difficult problems. Lets look at some specific examples.
Advertiser: Data Robot
Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations.
Advertiser: Data Robot
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.
Advertiser: Data Robot
While everyone is talking about machine learning and artificial intelligence (AI), how are organizations actually using this technology to derive business value? Renowned author and professor Tom Davenport conducted an in-depth study (sponsored by DataRobot) on how organizations have become AI-driven using automated machine learning.
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