Top 5 Machine Learning Trends To Watch Out For in 2021
As we enter the new year, it’s time to take a closer look at the machine learning trends that are set to dominate the industry. In 2020, the global pandemic brought about significant changes in how businesses operate, and machine learning played a significant role in helping organizations adapt to the new normal. In this article, we’ll look at the top five machine learning trends to watch out for in 2021.
1. Automated Machine Learning
Automated machine learning (AutoML) is set to become one of the most significant machine learning trends in 2021. AutoML allows businesses to use machine learning algorithms without requiring in-depth knowledge of data science. It automates the process of selecting algorithms, tuning hyperparameters, and feature engineering.
AutoML enables companies to streamline the process of developing machine learning models, which is crucial in an industry where speed is a competitive edge. The technology is beneficial for small to medium-sized businesses that lack the funds and resources to hire data scientists, making data science accessible to more organizations.
2. Federated Learning
Federated learning is a machine learning technique that enables machine learning models to be trained on decentralized data sources. Instead of pooling data in a centralized database, federated learning allows models to be trained on devices that are distributed across a network. This enables organizations to preserve the privacy and security of their data while still benefiting from the insights gained from machine learning.
Federated learning is set to become more popular in 2021 as data privacy concerns grow, and businesses realize the potential of decentralized machine learning.
3. Explainable AI
Explainable AI (XAI) is an emerging trend in machine learning that emphasizes the ability of machine learning models to explain their decision-making process. With the increasing complexity of machine learning models, it’s becoming more crucial for businesses to understand how models arrive at their decisions.
XAI enables businesses to gain insights into the reasoning behind predictions made by machine learning models, which is essential in fields such as healthcare and finance. In 2021, expect more focus on developing explainable AI systems, especially in areas where decision making is critical.
4. Edge Computing and Machine Learning
Edge computing involves processing data closer to where it is created, such as on mobile devices and Internet of Things (IoT) devices. By performing machine learning tasks on the edge, businesses can reduce the amount of data that needs to be transferred to centralized servers, minimizing latency and bandwidth issues.
Edge computing and machine learning are set to work together more seamlessly in 2021, with more focus on developing machine learning models that can be deployed on resource-constrained devices.
5. Reinforcement Learning
Reinforcement learning is a machine learning technique that allows machines to learn through trial and error. It’s commonly used in game development and robotics, but it’s also gaining popularity in fields such as finance and marketing. Reinforcement learning is set to become more prevalent in 2021, with more businesses seeing its potential in areas such as dynamic pricing and personalized advertising.
In conclusion, the machine learning trends listed above are set to dominate the industry in 2021. AutoML will make machine learning more accessible to businesses, federated learning will enable organizations to preserve the privacy and security of their data, XAI will enable businesses to gain insights into the reasoning behind machine learning models, edge computing and machine learning will reduce latency and bandwidth issues, and reinforcement learning will help businesses make more informed decisions. As we continue to navigate the challenges posed by the pandemic, machine learning will become even more critical in helping businesses adapt to the new normal.
