TinyML is a machine learning technique powered by small and low-powered devices like microcontrollers. It is a subfield of ML that enables applications to cheap devices and resource and power-constrained devices. TinyML intends to bring machine learning to the edge, reducing power consumption and allowing fast processing and storage of data where it is needed. ML also improves security.
Automated machine learning (AutoML) entails using automation to identify ML models for real-world problems. It automates the selection, automation and composition as well as parametrization of ML models. It is used to minimize human interaction and to process tasks automatically to solve real-world problems. This includes a whole process, from raw data to the final machine learning model. AutoML offers an extensive learning technique even for non-experts in ML. Since it is automated, it does not require human interaction.
- Data fabric
This is an architecture and data service providing consistent capabilities across various endpoints in hybrid multi-cloud environments. It standardizes data management practices and practicalities in cloud, on-premise and edge devices. According to Gartner, data fabric is one of the best analytical tools. It contains data management technologies that help in data governance, data pipelining, and data integration, among others which are crucial in big data analytics. It reduces the time for fetching out business insights making it useful in business decision-making.
- Cloud migration
The world and businesses are migrating their applications and services to the cloud. This is a key trend that is expected to change operations due to various benefits not only for businesses but also for individuals who rely on the cloud for storage. Cloud migration helps the organization by offering storage of big data from different sources at a lower cost and with improved speed, performance and scalability, especially when there is heavy traffic.
- Data regulation
Although big data has made its way into the company and corporate world and has helped in decision-making across the board, it has yet to impact the legal landscape as it ought to be. Although some have started adopting big data structures, it is still a long way to go. The responsibility of handling data at a large scale in industries such as healthcare needs laws and regulations because such data needs to be secured and cannot be left with AI alone. Going into the end of the year and even 2023, companies and relevant stakeholders are getting concerned about the existing data regulations and the need for new, better regulatory frameworks.
The growing pace of technology means that we are becoming dependent on it. The Internet of Things (IoT) plays an excellent role in data technologies and architecture. The growing demand for big data has seen the adoption of sensors to gather data for decision-making. IoT will play a larger role in collecting, storing and processing data in real-time to solve organizational problems in industries such as manufacturing, healthcare and supply chain.