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Building vs. buying AI News
March 31, 2021
A new OSDU standard aims to bring information from oil, gas, wind, solar and other sources into a single data platform to help energy companies address future demand.
May 18, 2018
A new developer kit from Intel seeks to lower the bar for doing deep learning on CPUs and other types of chips to extract more intelligence from video.
July 31, 2017
Think before you act on artificial intelligence technologies to ensure that your efforts to become a "cognitive business" lead you in the right direction.
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Making machine learning more accessible and helping developers with AWS SageMaker training is at the core of Julien Simon's book, 'Learn Amazon SageMaker.' Continue Reading
Deep learning is powering the development of AI. To build your own neural network, start by understanding the basics: how neural networks learn, correlate and stack with data. Continue Reading
AI encompasses a wide range of disciplines, from advanced math to application development, and building a strong AI team starts with incredibly skilled data scientists. Continue Reading
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Enterprises are adopting machine learning technologies at rapid rates. In this machine learning guide, we break down what you need to know about this transformative technology. Continue Reading
In the past year, AI hyperscalers got serious about their machine learning platforms, expectations were reset and transformer networks empowered the GPT-3 language model. Continue Reading
GPUs are often presented as the vehicle of choice for running AI workloads, but the push is on to expand the number and types of algorithms that can run efficiently on CPUs. Continue Reading
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As standardized NLP framework evaluations become popular, experts urge users to focus on individualized metrics for enterprise success. Continue Reading
A shortage of data for machine learning training sets can halt a company's AI development in its tracks. Turning to external sources and hidden data can solve the problem. Continue Reading
Problem Solve Building vs. buying AI Issues
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Most data science projects end up facing similar problems, such as lack of robustness and data quality issues. In this feature, experts offer tips on how to overcome these challenges. Continue Reading