Top 5 machine learning trends

Market news | September 30, 2021

Software engineering company MobiDev has listed what it believes to be the latest innovations in machine learning (ML) to benefit businesses in 2021-2022.阅读更多

By Rich Pell

Based on analysis of the company’s AI team experience, the company says the following are the top five machine learning trends in 2021-2022:

TinyML– It can take time for a web request to send data to a large server for it to be processed by a machine learning algorithm and then sent back. Instead, a more desirable approach might be to use ML programs on edge devices – we can achieve lower latency, lower power consumption, lower required bandwidth, and ensure user privacy.
AutoML– Auto-ML brings improved data labeling tools to the table and enables the possibility of automatic tuning of neural network architectures. Evgeniy Krasnokutsky PhD, AI/ML Solution Architect at MobiDev, explains: “Traditionally, data labeling has been done manually by outsourced labor. This brings in a great deal of risk due to human error. Since AutoML aptly automates much of the labeling process, the risk of human error is much lower.”
Machine Learning Operationalization Management (MLOps)– MLOps provides a new formula that combines ML systems development and ML systems deployment into a single consistent method. Dealing with more and more data on larger scales requires greater degrees of automation – MLOps can easily address systems of scale.
Full-stack Deep Learning– A large demand for “full-stack deep learning” results in the creation of libraries and frameworks that help engineers to automate some shipment tasks and education courses that help engineers to quickly adapt to new business needs.
一般Adversarial Networks (GANs)– GANs produce samples that must be checked by discriminative networks which toss out unwanted generated content. Similar to branches of government, General Adversarial Networks offer checks and balances to the process and increase accuracy and reliability.

“Like many other revolutionary technologies of the modern day, machine learning was once science fiction,” says Evgeniy Krasnokutsky, an AI/ML Solution Architect at MobiDev. “However, its applications in real world industries are only limited by our imagination. In 2021, recent innovations in machine learning have made a great deal of tasks more feasible, efficient, and precise than ever before.”

“Understanding the possibilities and recent innovations of ML technology is important for businesses so that they can plot a course for the most efficient ways of conducting their business,” he says. “It is also important to stay up to date to maintain competitiveness in the industry.”

For more ML trends, see Krasnokutsky’s blog post: “Machine Learning Trends To Impact Business in 2021-2022.”

MobiDev