SAPVoice: How to Solve IoT’s Big Data Challenge with Machine Learning
Machine learning will come of age this year, moving from the research labs and proof-of-concept implementations to cutting-edge business solutions. Along the way, it will help power innovations, such as autonomous vehicles, precision farming, therapeutic drug discovery and advanced fraud detection for financial institutions.
Machine learning intersects with statistics, computer science and artificial intelligence, focusing on the development of fast and efficient algorithms to enable real-time data processing. Rather than just follow explicitly programmed instructions, these machine learning algorithms learn from experience, making them a key component of artificial intelligence platforms.
Machine Learning Helps Tackle IoT Data Flows
Machine learning may also help us with a challenge from one of last year’s most buzzed about technology developments: the Internet of Things. The first generation of big data analytics grew up around the flow of information generated by social media, online shopping, online videos, Web surfing and other user-generated online behaviors, according to Vin Sharma, the director of machine learning solutions in Intel’s Data Center Group.
Analyzing these massive datasets required new technologies, flexible cloud computing and virtualization, software such as Apache Hadoop and Spark. It also needed more powerful, high-performance processors that provided the tools to uncover the insights in big data.
And today’s IoT connected networks dwarf the data volume from this first era of big data. As devices and sensors continue proliferating, so will the volume of data they create.
For example, a single autonomous car will generate 4,000 GB of data per day. The new Airbus A380-1000 is equipped with 10,000 sensors in each wing. Legacy big data technology won’t be able to handle the data created by connected appliances in smart homes, traffic sensors in smart cities and robotic systems in smart factories.