As more European organizations move from experimentation to production for AI projects, the importance of running these projects on a scalable, secure, and cost-efficient platform becomes clear. Building AI solutions from scratch is often beyond the capabilities of many organizations, especially because it requires in-house AI expertise, which is in short supply. According to analyst Annie Bailey of European-based analyst firm KuppingerCole, AI Service Clouds (such as Amazon SageMaker) speed up the time to value for AI projects and allow a larger group of company roles to contribute to the success of the project. AI Service Clouds put the vast capabilities of AI into a wide variety of company roles and personas, including line of business staff and software developers, not just data scientists.
According to the KuppingerCole study, a key factor for the growth of AI usage in an enterprise is reduction of uncertainty (and complexity), especially around how to develop, staff, and implement a successful AI project. The existing knowledge gap for AI expertise (globally and in the EU) can act as a barrier to organizations fully implementing technologies such as robotics, computer vision, and natural language processing (NLP). AI Service Clouds mitigate these barriers by automating or streamlining ML processes into the workflow, such as data labeling, data preparation, bias detection, AutoML, training, hosting, explainability, and monitoring.
Trust and transparency are another potential risk for AI projects, and AI Service Clouds are well-positioned to reduce uncertainty here. According to KuppingerCole, “a model can only succeed in operation if it is trusted to behave fairly, ethically, and logically.” Fully managed cloud services such as SageMaker offer a wide variety of services to ensure bias reduction, accurate data, and understandable algorithms and outcomes. For example, Amazon SageMaker Clarify provides machine learning (ML) developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions. European customers such as Zopa use Clarify improve their fraud detection capabilities.
The KuppingerCole Market Compass for AI Service Clouds focuses on the “key areas of the AI/ML development process including lifecycle management, explainability, and bias mitigation.” AWS was named a leader, earning the highest ranking in four of five review categories (Security, Interoperability, Deployment, and Market Standing).
AWS was also named Outstanding in a Modular Approach: “AWS has broken down the AI/ML development process into modular steps, pathways for users with different areas of expertise, and pre-built horizontal and vertical solutions. The AWS AI services include solutions for healthcare, industrial and manufacturing, and more. Horizontal pre-built solutions include vision, speech, text, coding, forecasting, fraud, and more. Moving on to the model development and implementation modules, AWS offers Amazon SageMaker, the service most focused on in this report, which includes data preparation, monitoring, work flows, debugging, and explainability.”
To read this report, see Market Compass AI Service Clouds – AWS Excerpt.
About the Author
Mark Kitchell is a Senior Analyst Relations Manager at AWS, based in Luxembourg. Mark works with influential industry analysts from firms such as Gartner, Forrester, and IDC, to ensure they have a complete understanding of AWS, and how we can help their customers using ML technologies. He enjoys showcasing how customers are solving critical business challenges using Machine Learning. In his spare time, Mark loves to ride motorcycles, rescue cats, and spend time with his family.