Forging ahead
When deployed with the right strategy, artificial intelligence (AI) and machine learning (ML) can increase agility, streamline processes, boost revenue by creating new products and improving existing ones, and enable better, faster decision-making.
There’s no doubt that AI and ML can help companies achieve more—by 2025, global spending on AI will reach $204 billion.1 It’s also clear that adopters continue to have confidence in AI’s ability to drive value and competitive advantage.
While ML has been around for decades, its accessibility as a tool to transform businesses is relatively new. Additionally, the lack of a singular proven path to ML success is keeping some businesses waiting on the sidelines, unsure of how to take the next (or even the first) step on the journey.
It’s time for organizations to overcome ML barriers, stop playing catch-up, and forge ahead with confidence. This eBook outlines a proven path—from the first step to measuring results—with insights from Amazon ML best practices and its experience helping thousands of customers realize their own initiatives.
What are artificial intelligence and machine learning?
You’ve probably heard AI and ML described in a number of ways, so let’s take a step back and review what each term means. AI is a way to describe any system that can replicate tasks that previously required human intelligence.
Most use cases for AI are looking for a probabilistic outcome making predictions, classifications, or decisions with a high degree of certainty and in ways that are similar to human judgment.
Almost all AI systems today are created using ML, which uses large amounts of data to create and validate decision logic. This is known as a model. The AI system feeds input data into that model, and then the model outputs human-like predictions or classifications. Essentially, ML is the underlying technology that powers intelligent systems.
This article is posted at aws.amazon.com
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