Thoughtworks Technology Radar Foresees ML Propelling IoT and Pragmatic Use Cases Alike

Rhea-AI Summary

Thoughtworks (NASDAQ: TWKS) has released Volume 27 of its Technology Radar, highlighting advancements in machine learning (ML) accessibility for smaller organizations. The report emphasizes the mainstreaming of ML, the role of platforms, the importance of data ownership decentralization, and the benefits of modular mobile development. With better computational power and open-source tools, ML is now more achievable for many. However, the report warns about vulnerabilities and biases in training data. The Technology Radar aims to guide organizations in effectively leveraging these technologies.

Positive

• Increased accessibility of machine learning for smaller organizations.
• Growth in the availability of open-source tools for ML.
• Emphasis on privacy through techniques like federated machine learning.
• Encouragement to explore pragmatic ML use cases for efficiency.

Negative

• Potential vulnerabilities and biases remain in training data sets.
• Ethical issues in ML proliferation require careful navigation.

In its 12th year, latest report also shows moving data ownership to the edges providing greater privacy and personalization of information on devices

CHICAGO--(BUSINESS WIRE)-- Thoughtworks (NASDAQ: TWKS), a global technology consultancy that integrates strategy, design and engineering to drive digital innovation, today released Volume 27 of the Technology Radar, a biannual report informed by Thoughtworks’ observations, conversations and frontline experiences solving its clients’ toughest business challenges globally. While machine learning (ML) once required large data sets and access to immense compute power to tackle complex business issues, the report observes that the growth and breadth of tools, applications and techniques is enabling IT organizations to do more with ML and in more places.

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. Rebecca Parsons, chief technology officer at Thoughtworks. “Organizations are advised to also be open to more pragmatic use cases where ML can be applied to operations, products and services for greater efficiency, not only the more game-changing applications.”

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.

Supporting resources:

• Keep up with Thoughtworks news by visiting the company’s website
• Follow us on Twitter, LinkedIn and YouTube.

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About Thoughtworks

Thoughtworks is a global technology consultancy that integrates strategy, design and engineering to drive digital innovation. We are 12,000+ people strong across 49 offices in 18 countries. Over the last 25+ years, we’ve delivered extraordinary impact together with our clients by helping them solve complex business problems with technology as the differentiator.

View source version on businesswire.com: https://www.businesswire.com/news/home/20221026005016/en/

Media contact:

Linda Horiuchi, global head of public relations

Email: linda.horiuchi@thoughtworks.com

Phone: +1 (646) 581-2568

Source: Thoughtworks

FAQ

What does the latest Thoughtworks Technology Radar report discuss for TWKS?

The report discusses advancements in machine learning accessibility and the importance of data privacy.

How does Thoughtworks (TWKS) view machine learning in its latest report?

The report highlights the mainstreaming of machine learning and its growing accessibility for smaller organizations.

What key themes are highlighted in Thoughtworks' Technology Radar Volume 27?

The themes include mainstreaming of ML, data ownership decentralization, platform development, and modular mobile development.

What are the privacy concerns mentioned by Thoughtworks (TWKS) regarding machine learning?

The report emphasizes vulnerabilities and biases in data sets that could affect privacy.