Whether on-premises or in the cloud, your data provides a link to the past and a glimpse into the future. Why did you lose past customers? Which current customers should you pay more attention to? Where are your new customers going to come from? In this blog, I'll talk about the capabilities of Teradata Vantage and its Machine Learning Engine, and how it can help you turn 100% of your data into answers that your business can use to pave a path to the future.

Whether on-premise or in the cloud, your data provides a link to the past and a glimpse into the future. Why did you lose past customers? Which current customers should you pay more attention to? Where are your new customers going to come from? In this blog, I'll talk about the capabilities of Teradata Vantage and its Machine Learning Engine, and how it can help you turn 100% of your data into answers that your business can use to pave a path to the future.

The future is looking good for machine learning. As data becomes cheaper and processing power gets even better, feats of data science become possible for everyone. However, hiring machine learning experts is something of an issue, as the demand continues to outstrip the supply. What's more, hiring a ML expert isn't cheap, as they regularly command some of the highest salaries in tech. Enter machine learning as a service (MLaaS).

Last November, when Google announced that machine learning research luminary Fei-Fei Li, Ph.D. would join Google's Cloud Group Platform group, a lot was known about her academic work. But Google revealed little about why she was joining the company except she would lead machine learning for the Google Cloud business. After five months of suspense, yesterday Li revealed the focus of her new role during her keynote address at Google's cloud developer conference, Cloud Next 2017. She will apply her experience to democratize machine learning to the enterprise. Her task: Study the problems that machine learning could solve in a wide variety of industries and enable enterprises to adopt machine learning. It sounds more like a job for an enterprise salesman, not a Stanford research professor with over a hundred papers published in the field, but that would be the wrong conclusion.

Our simple and extensible framework lets users easily point and collect data from multiple data sources across similar application and infrastructure elements. In addition to providing rich, out-of-the-box data source plug-ins, Perspica enables fast, easy creation of custom data source integrations through our data collection API.

Machine learning (ML) is still growing as a field in big data and has of late made some significant advances. In fact, practical applications have become quite commonplace and most of us have benefited from it already. Email providers use it to determine whether an email is spam or legitimate. Credit card companies use it to flag potentially fraudulent charges. Hospitals are using it to improve outcomes for patients.