Chapter 1: Introducing AutoML
AI is everywhere. From recommending products on your favorite websites to optimizing the supply chains of Fortune 500 companies to forecasting demand for shops of all sizes, AI has emerged as a dominant force. Yet, as AI becomes more and more prevalent in the workplace, a worrisome trend has emerged: most AI projects fail.
Failure occurs for a variety of technical and non-technical reasons. Sometimes, it's because the AI model performs poorly. Other times, it's due to data issues. Machine learning algorithms require reliable, accurate, timely data, and sometimes your data fails to meet those standards. When data isn't the issue and your model performs well, failure usually occurs because end users simply do not ...
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