In its 12th year, latest report also shows moving data ownership to the edges providing greater privacy and personalization of information on devices
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As computational power grows on devices of all sizes and types and open-source tools become more prevalent and easier to use, ML is becoming accessible to even the smallest organizations. In particular, with more stringent requirements and awareness around privacy and personalized information, organizations are finding techniques, such as federated machine learning, provide greater privacy for sensitive information in
IoT and mobile use cases. As ML is highly dependent on the quality of training data, the standard cautions remain on vulnerabilities and inherent bias in data sets. Yet a preponderance of open source tools is helping to build transparency in how the algorithms are interpreting and acting on the data
“Once limited to the most advanced users and highly resourced IT organizations, more publicly available and easier to use ML-models and components is helping to lower barriers to entry and make ML experiences and solutions accessible to even more organizations,” said Dr.
Highlighted themes in Technology Radar Vol. 27 include:
- The mainstreaming of ML: In little more than a decade, machine learning has moved from a highly specialized technique to something that almost anyone with data and computational power can do. That is to be welcomed — yet it remains essential that the industry can navigate both the proliferation of tools and frameworks in the space and the ethical issues that are becoming increasingly visible and urgent.
- The power of platforms as a product: A platform can be a powerful thing, particularly when it comes to empowering developers. However, we often see disappointing results when they’re not treated properly as products — it’s important that platforms are built and maintained in a way that responds to and mediates the needs of both technical teams and the wider organization.
- Moving data ownership to the edges: When it comes to data, centralization can be constrictive. New techniques and tools, however, are making it easier to overcome the challenges of centralization, offering advantages from both a technical and privacy standpoint.
- Mobile should be modular, too: The benefits of modularity are well-known, but, for a number of reasons, they haven’t been leveraged as much in mobile development. This is now starting to change; we believe adopting a modular approach to mobile will improve not only the quality of mobile applications but also the experience of building them.
Visit www.thoughtworks.com/radar to explore the interactive version of the Radar or download the PDF version.
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