Computer Science & IT
Coding & Algorithms Development
- Shrewd Object Visualization Mechanism
- Robotic Process Automation
- Role of AI in Healthcare
- Natural Language Processing
- Edge Computing
- AI For Cybersecurity and Knowledge Breach
- Reinforcement Learning
- Machine Learning in Hyperautomation
- The Intersection of ML and IoT
- Consistent Integration with Other Languages
The Intersection of ML and IoT
The intersection of machine learning and IoT is creating a need for new ways of thinking about — and understanding — data, sensors, citizen data scientists, and a host of other issues. In an increasingly turbulent technology environment, new ideas are often to be found at the intersection of things. In such cases, the contradictions between trends and possibilities appear in high relief. Within the data center, one such intersection is that between machine learning (ML) and the internet of things (IoT). ML is becoming an essential player in a growing array of process areas involving image recognition, natural language processing, forecasting, prediction, and process optimization.
At the same time, the IoT is creating an explosion in structured and unstructured data from a growing army of sensors capable of registering location, voices, faces, audio, temperature, sentiment, health and the like. ML is evolving to the point of being able to draw interesting patterns and inferences from these real time data streams, and make those results available to analysts as well as to embed them directly in business processes.
Fig.1. Strategy and Business Innovation in M2M, IoT & IoM Markets
There are many industries gearing up to take advantage of new opportunities in ML, but this does not disguise the fact that this revolution is different from the recent evolution in big data and analytics. Both the production of data and the need for data are huge, and often in real time. Data must be stored, secured, and prepared for use. IoT provides vast rivers of structured, semi-structured, and unstructured data; and ML brings a range of new problems such as algorithm selection and modeling to the fore. At the same time, corporate needs for security and data governance cannot be ignored.
The ML/IoT intersection is already well underway and highly visible in areas such as autonomous vehicles and robotics. Demands from IoT will help shape data strategy for years to come, as well as influencing how AI and ML technologies are integrated into the workplace. Gaining an early understanding of these influences will help ensure that your company can cope with the next wave of innovation in this sector.