Unlocking Machine Learning for All: Insights from Felipe Chies at Amazon Web Services
Amazon Web Services (AWS) stands at the forefront of public cloud infrastructure, offering over 200 fully-featured services. These encompass various domains, including computing, storage, databases, networking, analytics, robotics, the Internet of Things (IoT), security, and, notably, machine learning and artificial intelligence (AI). AWS aims to make machine learning accessible to a broader audience.
Among its offerings, Amazon SageMaker is a standout platform. This fully managed service simplifies the complexities typically associated with machine learning, enabling developers and data scientists to effectively leverage machine learning technologies. Since its launch in 2017, SageMaker has expanded to include over 150 new features. At the re:Invent conference in December 2020, AWS emphasized its goal: to ensure machine learning becomes foundational in most applications within the decade, making it usable for more than just experts.
During the AI & Big Data Expo in Amsterdam, AI News spoke with Felipe Chies, AWS’s senior business development manager for AI and ML in Benelux. Chies, who co-founded semiconductor startup Axelera AI, discussed the acceleration of innovation through no-code and low-code machine learning and shared insights into key use cases and AWS product offerings. Chies explained, “We are very proud to have the most robust and complete set of machine learning capabilities. Our approach focuses on our customers, and we organize our offerings into three layers.”
The first layer is intended for machine learning practitioners who are comfortable with deep learning frameworks and building complex models. The second layer offers tools like Amazon SageMaker that simplify model training and deployment for developers and data scientists. Finally, the third layer consists of Application Services that allow developers to seamlessly integrate pre-built AI functionalities into their applications, often without needing to understand the underlying machine learning models. Examples include Amazon Kendra, Amazon CodeGuru, Contact Lens for Amazon Connect, and Amazon HealthLake, which empower users to harness AI effortlessly.
Chies noted, “To truly democratize machine learning, we need to make it accessible to those who are not experts. We designed Amazon SageMaker to eliminate the heavy lifting and complexity from the entire machine learning process. It empowers everyday developers and scientists to build machine learning models successfully.” In line with this vision, AWS introduced Amazon SageMaker Canvas. This tool allows business users and analysts to create highly accurate machine-learning predictions using a simple visual interface, all without coding.
When asked about the level of expertise required to utilize AWS’s AI and machine learning tools, Chies emphasized that AWS aims to make technology accessible to a wider audience, ensuring that even those without extensive backgrounds in machine learning can benefit from its capabilities.
A few years ago, advanced technologies were limited to well-funded organizations, but our goal has always been to democratize access. We’ve successfully broadened the availability of storage, computing, analytics, databases, and data warehousing, applying the same strategy to machine learning. Our aim is to ensure that machine learning is accessible to as many users as possible.
AI: What are the common use cases and industries that you see, and how can you help?
FC: Currently, AWS Machine Learning is utilized by more than 100,000 customers. One notable sector leveraging this technology is manufacturing and supply chain management. Recent global challenges in the supply chain have highlighted the importance of accurate demand forecasting. Companies often inquire about how we can help them predict changes in demand, minimize costs, enhance customer satisfaction, and ensure timely delivery. Predictive maintenance and quality control are straightforward applications of machine learning in manufacturing. For instance, computer vision can be employed for quality inspections. In marketing and sales, forecasting remains a crucial use case where the benefits to businesses are easily recognized.
AI: What would you identify as the primary obstacles to machine learning adoption?
FC: Many organizations I engage with already possess a machine learning mindset, which is not a barrier. However, a significant challenge is the backlog of human resources; there simply isn’t enough bandwidth for development teams. While hiring more specialists—such as data scientists and engineers—could be a solution, these experts are in short supply. This situation underscores the need for democratizing machine learning further. Why not empower a wider variety of employees, including business analysts, finance professionals, and marketers, to engage with machine learning? Tools like Amazon SageMaker Canvas illustrate this approach, enabling non-technical users to generate precise machine-learning predictions through a user-friendly, code-free interface.
AI: What insights would you like attendees to take away from your keynote presentation?
FC: Some individuals may believe that machine learning is beyond their reach, prompting them to defer their requests to the data science team and wait for weeks. However, this is not the reality; they can start utilizing machine learning within minutes. It’s essential for people to realize that they no longer need deep knowledge of model-building to harness the power of machine learning.
Are you interested in delving deeper into AI and big data innovations from leading industry figures? Consider attending the AI & Big Data Expo in Amsterdam, California, and London.
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