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Building vs. buying AI News
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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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
Before AI can revolutionize business processes or decision-making, companies need a strong foundation. These tools, platforms and applications help enterprises get started with AI. Continue Reading
Evaluate Building vs. buying AI Vendors & Products
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Before autoML can improve model building and deployment, enterprises need to choose a platform. Here, we evaluate autoML platforms by category, key features and accessibility. Continue Reading
Once a machine learning model is trained, developers need to operationalize it. This turns out to be a significant challenge for many enterprises. Continue Reading
At Wayfair, using computer vision and NLP to understand the meaning behind images and searches is the key to customer recommendation, satisfaction and easy substitutability. Continue Reading
Manage Building vs. buying AI
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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