Machine Learning in the cloud can automatically detect anomalies in user behavior.

Machine learning promises capabilities to study algorithms and learn and access data by training computers. Businesses use machine learning algorithms in a diverse range of applications, such as email filtering and computer vision, where it is challenging to build conventional algorithms to perform the needed tasks. However, implementing ML algorithms to huge amounts of data raised can be quite challenging and require new and innovative approaches as traditional machine learning libraries do not support the processing of massive datasets. Enter, the cloud infrastructure.

Cloud delivers computing services for big data sets, including servers, storage, databases, networking, software, analytics and intelligence. Most companies capitalize on cloud solutions to reap digital business opportunities, eliminating the expense of buying the hardware and the software and setting up on-site data centers.

Why Do We Need Machine Learning in Cloud?

Machine learning is actively being utilized in almost all industries. Most cloud providers even started offering machine learning capabilities, including AWS, Google, and Microsoft, among others. They typically provide support for three types of predictions:

Binary prediction – This type of ML prediction addresses “yes” or “no” responses, and is primarily used for practices like fraud detection, recommendation engines, and order processing, to name a few.

Category prediction – In this type of prediction, a dataset is observed and based on the information collected, and then placed under a specific category. The insurance industry, for instance, use category prediction to categorize distinct types of claims.

Value prediction – This type of prediction explores patterns within the accrued data by using learning models to demonstrate the quantitative measure of all the likely outcomes. Many companies use value prediction to envisage an uneven number of how many units of a product will sell in the near future, permitting them to shape their manufacturing plans accordingly.

Cloud-Based Machine Learning for Cybersecurity

The threat landscape is relentlessly growing, and the rapid pace of technological adoption, including the cloud, is paving opportunities for malicious actors to breach data. However, to ensure security, the scale of services across cloud and on-premises environments requires a new approach to cyber defense. Most foresighted companies are shifting from manual security strategies to intelligent security operations centers (SOCs) that can foresee, spot, thwart and respond to threats automatically.

In an Oracle report on “Machine learning-based adaptive intelligence: The future of cybersecurity”, the company CTO and Chairman Larry Ellison noted that, “The way to secure our data, the way to prevent data theft, is more automation. And we need a cyber defense system that automatically detects vulnerabilities and attacks. Fix the vulnerability before an attack. And then, if there is an attack, detect the attack and shut it down.”

Organizations are increasingly capitalizing on cybersecurity technologies that rely on AI and ML algorithms to manage configurations, monitor who has access to what resources, and encrypt sensitive data to safeguard IT assets. By the way, cloud infrastructures remain a largely unexploited resource when it comes to training and applying ML models for cybersecurity applications and for industries handling sensitive, regulated data alike. According to a Forbes article, there are a few considerable reasons for this lag, including confidentiality concerns, a lack of technological maturity, misunderstandings about how cloud-based ML actually works, and others.

While developments in technologies, including cybersecurity products, will instigate tackling the challenges of cloud-based machine learning, businesses will turn to this tech and invest in it as it provides scalability, cost-effective advantages of the intelligence, detection, and automation across all on-premises and cloud environments, instant implementation and continuous improvement of new models and algorithms and security at scale.