EMEA19 - Assets (new)

How to get from ML concept to production - Q&A

Amazon Web Services Resources EMEA

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I N T R O D U C T I O N Machine learning (ML) is frequently the catalyst that turns business data into accurate predictions and actionable information. For many companies, however, the barriers to entry seem daunting: • Data needs to be discovered, organized, and standardized • Compute resources need to be sourced to handle ML workloads • Models need to be built and trained by data scientists • Outputs need to be integrated into business processes When faced with requirements that are hard to scope out and goals that require imagination, companies need expert help. Larry Pizette, senior manager and leader of the Machine Learning (ML) Solution Lab at Amazon Web Services, knows a lot about overcoming ML barriers. He meets with customers from diverse industries, helping them plan and implement their ML initiatives across a wide range of use cases. We had the opportunity to sit down with Larry and pose 10 questions, eliciting his insights for business leaders who want to understand ML trends, how to get a project started, and how to move from proof-of-concept to production. How to get started with Machine Learning: Expert advice from the front lines Smart companies know that artificial intelligence and machine learning have the potential to transform business, but many aren't sure where to start. Here's an expert guide based on 10 of the most burning questions asked about what it takes to launch a machine learning initiative. A P P R OAC H I N G M L What's driving interest in machine learning today? We're at an inflection point, where machine learning has hit the common consciousness. There are references to artificial intelligence and machine learning on television, magazines, websites, and product announcements – it's everywhere. Customers are looking at ML for a variety of different reasons, but it tends to be around the same goals: better experiences and outcomes for their customers, improved operations, and, for commercial customers, better financial results. Business owners want to unlock the potential of their data. It's hard not to pay attention to machine learning. Are there specific industries or functions that are strong candidates for ML? We have customers from many different business areas: oil and gas, healthcare, life science, insurance, financial services, restaurants, professional sports, transportation, government, and educational institutions. Customers are coming to the ML Solutions Lab because they want to get started with ML capabilities in their organizations, and they'd like to understand the art of the possible. Amazon Web Services + Intel | How to get started with Machine Learning: Expert advice from the front lines | February 2019 T E C H B R I E F Larry Pizette Senior Manager, Machine Learning Solutions Lab Amazon Web Services Q1 | Q2 | Plug-and-Play ML Many leaders think ML is complex and labor-intensive, but it need not be. AWS can get you started today, with no ML expertise required, through a range of ML services that can be easily added to your applications: • Analyze your images and videos with Amazon Rekognition. • Build voice and text chat bots for your business with Amazon Lex, which is powered by the same technology as Amazon Alexa. • Create applications that talk by turning text into lifelike speech with Amazon Polly. • Discover relationships and insights in text, such as your customer service emails or transcripts, with Amazon Comprehend. • Localize your content, such as websites and applications, quickly and cost- effectively with Amazon Translate. A W S T E C H

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