Machine learning uses sophisticated algorithms to "learn" from massive volumes of Big Data. The more data the algorithms can access, the more they can learn. Real-world machine learning examples are everywhere. Think of personalized product recommendations on Amazon, facial recognition on Facebook, or fastest route suggestions in Google Maps.
Manage your business with Machine Learning
Machine learning algorithms can prioritize and automate decision making. They can also flag opportunities and smart actions that should be taken immediately so you can achieve the best results.
Artificial intelligence doesn't just look at historical data. It can process real-time inputs so you can adjust on the fly. Think of cars that can automatically stop before rear-ending another vehicle.
An "algorithmic business" uses advanced machine learning algorithms to achieve a high level of automation. Making the shift can pave the way for innovative new business models, products and services.
Machine learning can analyse big, complex, and streaming data, and find insights including predictive insights that are beyond human capabilities. It can then trigger actions based on those insights.
With smart, machine learning-supported business processes, you can dramatically improve efficiency. Plan and forecast accurately, automate tasks, reduce costs and even eliminate human error.
class="text"From triggering smart actions based on new opportunities and risks, to accurately predicting the results of a decision before it is made machine learning can help you drive better business outcomes.
Many different industries and lines of business are ripe for machine learning particularly the ones that amass large volumes of data.
Manufacturers collect a huge amount of data from plant sensors and the Internet of Things which is perfect for machine learning. Computer vision and anomaly detection algorithms are used for quality control and others are used for everything from predictive maintenance and demand forecasting to powering new services.
Few industries are better suited for machine learning than finance given its high data volumes and historical records. Algorithms are used for trading stocks, approving loans, detecting fraud, assessing risks and underwriting insurance. They're even used for "robo advising" customers and aligning portfolios to user goals.
Machine learning algorithms can process more data and spot more patterns than any team of researchers or doctors, no matter how many hours they put in. From medical image analysis and early cancer detection, to drug development and robot-assisted surgery the machine learning possibilities in healthcare are endless.