Automated machine learning (AutoML) hasn’t completely replaced handcrafted algorithms, but it has made an impact. Datanami brought this interesting information to our attention in their article, “AutoML Tools Emerge as Data Science Difference Makers.“
The data science field is changing how companies look to utilize intelligence into their products and services, AutoML tools have the potential to open more doors into data science and data-driven automation on vast scales.
While automated machine learning features may be found in a range of tools, the AutoML category has a fairly defined set of features, including: acquiring and prepping data, engineering features from the data, selecting the best algorithm, tuning the algorithm, and deployment and monitoring of production models.
The most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.