The theoretical bases for Machine Learning have existed for decades yet it wasn’t until the early 2000’s that the last AI winter came to an end. Since then, interest in and use of machine learning has exploded and its development has been largely democratized. Perhaps not so coincidentally, the same period saw the rise of Big Data, carrying with it increased distributed data storage and distributed computing capabilities made popular by the Hadoop ecosystem.

The introduction of AI into products and services – across all sectors – is creating new capabilities at a scale that software developers could never have dreamed of just a decade ago. But this development is not just about the tech.

In today’s business world, marketing segmentation is a must have for every organisation. It helps you process and aim different targets in a market into multiple customer or prospect segments to enhance your marketing actions. Through this discipline, you can hold a crucial competitive advantage over your competitors because you can adapt your offer and your communication according to the identified groups of personas you want to address.

Artificial intelligence works on the principle of human intelligence. The machines are programmed in such a way that they think like humans and can imitate our actions. They can be designed to execute all types of tasks from complex to simple ones. The primary task machines can perform are learning, reasoning and perception.

We are excited to release Deep Learning for Anomaly Detection, the latest applied machine learning research report from Cloudera Fast Forward Labs. Anomalies, often referred to as outliers, are data points or patterns in data that do not conform to a notion of normal behavior. Anomaly detection, then, is the task of finding those patterns in data that do not adhere to expected norms. The capability to recognize or detect anomalous behavior can provide highly useful insights across industries.

Software Testing has changed a lot! Earlier, manual testing ruled the world of testing, however, test automation increasingly became a reality in most organizations developing software. Testing continued to evolve, and it took advantage of technology innovations. Artificial Intelligence (AI) is one such technology that has made a substantial contribution to automation in general. But, can AI-driven test automation significantly enhance test automation?

Readers of this blog have probably read about allegro.ai’s partnership with NetApp. Earlier this year the two companies showcased an integrated AI solution at the NVIDIA GPU Technology Conference (GTC) which took place in San Jose, California.

Machine learning is a branch of Artificial Intelligence that makes it possible for systems to learn without being programmed. Applying this technology to real world Marketing is often misunderstood and is within your reach.‍

Great to hangout with Noah Learner and Jordan Choo and talk about automation in agency life, with some thoughts around Machine Learning thrown in there too.‍