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Machine learning modeling News
July 15, 2020
An assisted living and transitional care provider uses DataRobot to automate the process of building and deploying machine learning models, enabling it to deploy models quickly.
November 07, 2019
Booz Allen Hamilton introduced an AI platform and marketplace made for uploading, deploying and managing AI models across a scalable environment.
October 30, 2019
With the new TensorFlow for enterprises, organizations running previous versions of TensorFlow can get long-term support, including security updates and select bug fixes.
August 09, 2019
IBM's open source AI Explainability 360 toolkit packages algorithms and training examples to help humans better understand the decision-making process of machine learning models.
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Predictive modeling, also called predictive analytics, is a mathematical process that seeks to predict future events or outcomes by analyzing patterns that are likely to forecast future results. Continue Reading
GPT-3 is the latest natural language generation model, but its acquisition by Microsoft leaves developers wondering when, and how, they'll be able to use the model. Continue Reading
Automated machine learning is the process of applying machine learning models to real-world problems using automation. Continue Reading
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Data science, machine learning and AI are central to analytics and other enterprise uses. Here's what each involves and how combining them benefits organizations. Continue Reading
The demand for data scientists continues to grow, but the job requires a combination of technical and soft skills. Here are 14 key skills for effective data scientists. Continue Reading
Data scientists use a variety of statistical and analytical techniques to analyze data sets. Here are 15 popular classification, regression and clustering methods. Continue Reading
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The data science process includes a set of steps that data scientists take to gather, prepare and analyze data and present the analytics results to business users. Continue Reading
Data scientists are forever vigilant in their desire to identify and eliminate the many forms of bias that can compromise the credibility of machine learning models. Continue Reading
Encompassing ethics, transparency and human centricity, responsible AI is an effective approach to deploying machine learning models and achieving actionable insights. Continue Reading
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Autoencoders' ability for automated feature extraction, data preparation, and denoising are complicated by their common problems and limitations in usage. Continue Reading
Machine learning expert Ben Cox of H2O.ai discusses the problem of bias in predictive models that confronts data scientists daily and his techniques to identify and neutralize it. Continue Reading
Bias in AI is a systematic issue that derails many projects. Dismantling the black box of deep learning algorithms is crucial to the advancement and deployment of the technology. Continue Reading