Machine learning is a technique that helps to teach computers how to do what comes naturally to humans and animals, like learning from experience. Machine learning uses computational methods to “learn” information from data directly without relying on a predetermined equation as a model. These algorithms improve their performance as the number of samples become available from learning increases.
Why Does Machine Learning Matter?
Machine learning has become a key technique for solving problems in areas with the rise in big data. Some essential techniques include:
Computational Finance – used for credit scoring and algorithmic trading
Image Processing and Computer Vision – used for face recognition, motion detection, and object detection
Computational Biology – used for tumor detection, drug discovery, and DNA sequencing
Energy Production – used for price and load forecasting
Automotive, Aerospace, and Manufacturing – used for predictive maintenance
Natural Language Processing – used for voice recognition applications
When and Why Should You Use Machine Learning?
A good time to use machine learning would be when you have a complex task or problem involving a large amount of data and lots of variables, but you do not have an existing formula or equation. Machine learning is used in a large number of applications today. Facebook’s news feed is one of the most well-known, and it uses machine learning to personalize each member’s feed. It recognizes when you scroll past certain things but stop to read certain articles or posts, and it will start to show more activity similar to what you were reading earlier in your news feed. It uses statistical analysis and predictive analytics to identify patterns in your data and uses those patterns to populate news feeds. When you no longer stop to look at certain things, it also collects that data and will adjust your newsfeed accordingly.
Machine learning is opening up a new frontier in cloud computing. The cloud is becoming smarter every day. The cloud we use today is intelligent. Machine learning is now a primary focus for the cloud and a necessity for many enterprises in order to stay on top of business intelligence and to optimize their business models by incorporating the best of the IoT, bots, and personal assistants. We explore in greater depth five ways that machine learning is influencing the cloud.
The Internet of Things
The Internet of Things has been evolving over the last several decades existing in several different forms. Today cloud computing is redefining the IoT with its data-driven platforms. These platforms can capture enormous amounts of data from a variety of data types in order to query, process, and analyze the data to identify significant trends and make the IoT more intelligent. One of the best case studies to demonstrate the value of machine learning on the IoT is that of predictive maintenance. Machine learning algorithms help to create the right model that is best matched for understanding the patterns found in datasets generated by industrial devices. The models can search for anomalies in the datasets that vary from the predicted normal patterns that can result in the failure or breakdown of equipment. The neat thing about machine learning is that these anomalies or faults can be identified and detected before a user ever notices them. This form of predictive maintenance is powerful and opens the door for industrial IoT to evolve to the next level.
Business Intelligence
Big Data and Apache Hadoop caused great disruption to the traditional data warehouse. The move is to bring Machine Learning closer to the enterprise data warehouse so that decision-makers can glean intelligent insights from their existing data. By bringing machine learning closer, it will enable more accurate trend forecasting. Every part of the enterprise from SCM, CRM, ERP, MRP, HR, Sales, and Finance will benefit from the more accurate insights driven by Machine Learning
All the major cloud players, including Amazon, Google, IBM, and Microsoft, are building bridges between traditional business intelligence platforms and Machine Learning-based tools. Developers and architects can connect the dots easily with the bridges connecting business intelligence platforms with Machine-learning tools to build the next generation of business intelligence tools.
Machine learning is instrumental in today’s society in order to determine and analyze patterns in data. It also helps to teach computers things that humans and animals are already familiar with. Machine learning is very important and something that will continue to grow.
