In this series of posts, we'll cover how Gretel's synthetic data platform helps you overcome challenges across the data-centric machine learning life cycle to help you successfully build, deploy, maintain, and realize value from your AI projects. The life cycle outlined below is a common framework or workflow process for building machine learning and AI solutions. It's focused on streamlining the stages necessary to develop machine learning models, deploy them to production, and maintain and monitor them. These steps are a collaborative process, often involving data scientists and DevOps engineers. The process below was inspired by the value chains created by The Sequence, Databricks, Google Cloud, and Microsoft.

A great way to keep your wits about you when working with machine learning (ML) and artificial intelligence (AI) is to think like a teacher. After all, the point of ML/AI is that you're getting your (machine) student to learn a task by giving examples instead of explicit instructions. As any teacher will remind you: if you want to teach with examples, the examples must be good. The more complicated the task, the more examples you'll need. If you want to be able to trust that your student has learned the task, the test must be good.

For example, those processes where RPA could not be implemented because the underlying data was unstructured and human judgment was involved can now be automated by introducing AI, which would convert the unstructured data to structured data and make decisions as a human would. In addition, AI robots would self-learn over time and thus would further free up resources that are needed to work on the'exceptions', which RPA could not handle. Integrating RPA and AI would also allow the automation process to become significantly faster and end-to-end. Similarly, the lone implementation of AI may not make business sense because of the implementation costs involved, but when combined with RPA, would be viable to implement, as combined ROI of the two technologies would be positive.

BEIJING, May 25, 2017 (GLOBE NEWSWIRE) -- Gridsum Holding Inc. ("Gridsum" or the "Company") (NASDAQ:GSUM), a leading provider of cloud-based big-data analytics, machine learning and AI solutions in China, today announced that, as a part of its strategic evolution, it has consolidated all of its artificial intelligence ("AI") activities strategically, technically and organizationally into a new division called the Gridsum Prophet. Gridsum is a first mover in China in big data intelligence. Since 2005, the Company has utilized a distributed big-data computing architecture, developed and implemented sophisticated natural language processing ("NLP"), and leveraged machine learning directed toward large enterprise clients. During that time, from serving large enterprise customers, the Company has accumulated deep domain knowledge and expertise as well as a massive amount of data that fuels its machine learning algorithms. Since this early inception, the Company has continued to stay at the forefront through focus and investment, hiring and training extraordinary engineers and architects and, importantly, playing an active and leading role in the AI academic and developer communities.