The Future State of Machine Learning Needs Improved Frameworks
Reposted from TechTarget – Ryohei Fujimaki, Ph.D., founder and CEO of dotData, comments on how ML (Machine Learning) has significant potential for solving business problems.
Read the full article here – The Future State of Machine Learning Needs Improved Frameworks #datascience #AutoML
Machine learning is becoming a crucial part of business intelligence, but it’s still difficult to access. Programmers and analysts must create machine learning models without assistance, and this is not yet possible. In the current state of machine learning, automated machine learning refers to frameworks that automate pieces of the machine learning process. Machine learning systems need to know how to work with data and find features in the data to provide accurate responses. Data analysts have to create initial data sets for machine learning applications using artificial intelligence to assist analysts in finding features in the data.
Machine learning is more complex than RDBMS, especially in vision and language, and it’s easy to neglect machine learning features. Programmers and analysts generally do not get dirty with data collection, but simplifying that task is essential. Machine learning can help business and technology people better communicate. Companies are making frameworks to advance automated machine learning, which will help with the earlier steps in the development flow. Machine learning companies are starting to use SQL on relational databases to help with machine learning and new technologies. Companies like PowerSoft and Gupta helped move the programming model forward. Companies like dotData want to help the framework model advance as well. Read more about it at TechTarget.