Introduction
IT Organisations are doing more with Machine Learning, observes Thoughtworks’ latest report “Technology Radar”. Organizations are using ML more frequently and in more areas as a result of the expansion and diversity of tools and applications.
The potential to harness AI and ML is an exciting prospect in enterprise IT today. ThoughtWorks’ report outlines how these technologies can be used for propelling IoT use cases.
According to the report, this process could be a boon for many industries as it enables companies to integrate new data sources into their existing workflows. This could lead to improved efficiency. Or even create entirely new business models.
With the growth and breadth of tools and applications, organisations are doing more with ML and in more places
ML Propelling IoT Use Cases
The Internet of Things (IoT) is currently in the third wave of development. The first wave was about connecting isolated devices such as smart homes and cars, but now we’re seeing a massive surge in connectivity.
According to Gartner research, by 2025, there will be more connected gadgets than humans on Earth.
IoT and machine learning are a match made in heaven. We can now gather data at a scale never before possible thanks to the IoT. However, it might still be challenging to discern any useful trends.
Machine learning helps us identify patterns by analysing data and making predictions based on past experiences. The result is that both technologies will only continue to improve and grow as they work together.
Organizations are finding techniques, such as federated machine learning, to provide greater privacy for sensitive information in IoT and mobile use cases, notes the report.
Once limited to the most advanced users and highly resourced IT organisations, ML is becoming accessible to even more organisations, notes Dr. Rebecca Parsons, CTO at ThoughtWorks.
The ThoughtWorks Radar on IoT highlights the mainstreaming of Machine Learning. 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.
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