Machine Learning - eBook (EN)

measurable return on investment. You also need to have the data required to support the building, training, and testing of your machine learning model. And it certainly helps to have senior- level business buy-in for the project so that it is not simply a science experiment but something that solves a real business problem and is incorporated into the fiber of the business. Q: What kinds of ML specialists do companies need today? Lee: Particular roles that are necessary include data scientists, data engineers, software developers, and technical program managers. A variety of skills are needed, and the key for a company is to do a skills analysis to identify the gaps up front. Data analytics and machine learning, at least in their current forms, are relatively new disciplines, so there is a shortage of people with these skills. This means that a company probably isn't going to be able to hire all of them, so perhaps it ought to focus on training its current workforce. At Amazon, we took an approach to both hire new talent and develop existing talent. Amazon developed a machine learning university that we have used for over six years to train our engineers. Last year, we made a lot of this content available for free to our customers — and, actually, to the public too. We have seen well over 100,000 developers start their machine learning journeys using this content. Q: What are some common challenges that companies may face in adopting ML? Lee: We've learned four key challenges that leaders need to address for successful adoption of machine learning: data strategy, getting started, the ML skills gap, and spending time on undifferentiated heavy lifting. That last challenge refers to activities such as building their own infrastructure and tools for data aggregation, access, and cleanup and modeling, rather than taking advantage of existing services such as Amazon's data lake offering, SageMaker for helping with ML model building and deployment, Rekognition for computer vision, Translate for language translation, or Comprehend for natural language processing. Data is often cited as the No. 1 challenge in adopting machine learning. To be successful in machine learning, a company needs to have a data strategy that identifies the data it has, where it's located, who controls it, and where it needs to be to support its full and optimal use by the company. A company also needs to ask, "What data don't I have today that I want to have in the future?" and then begin developing a plan to gather such data. Without a data strategy, the ML scientists a company hires will spend an inordinate amount of time dealing with data-management access and cleanup or, worse, get bogged down and frustrated because they lack what they need to solve the larger problem. So companies need to enable the IT team to break down any data silos and to collect the right data in a safe and compliant way. A second challenge is, how do I get started? Although every business has a machine learning opportunity, not every business problem is solvable by machine learning. So identifying that high- value use case whose results are measurable is key. But that's not always easy. There is a lot of hype around what machine learning can do. That's why AWS created the Machine Learning Solutions Lab, which allows us to work side by side with our customers, to listen to their business problems, to identify their highest- value ML use cases, and to help guide them to implementation. To each of our engagements, we bring tremendous depth and breadth of experience and expertise based on our engagements across a wide range of industries and use cases. The third challenge is the skills gap. Again, the growth in artificial intelligence has led to a shortage of data scientists and machine learning experts. You may not be able to hire all the data scientists you need, so you should probably focus your energy on upskilling the level of your current workforce and/or leveraging outside resources. And a fourth challenge is the tendency to think you have to develop everything on your own from scratch, when a cloud platform like AWS can provide many of the necessary tools and infrastructure needed for data access and machine learning model development, testing, and deployment. By taking advantage of these existing tools and services, you can focus on bringing your differentiated, value-added contributions, such as your domain and industry expertise and any special insights that SPONSOR'S VIEWPOINT • 23 "You do need data scientists, either on your team or as consultants. But, equally important, you need to identify and tackle the right machine learning use case for your company — one that solves a real and significant problem that has measurable return on investment."

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