Because data scientists are forced to down-sample, move data, and settle for sluggish computation, the promise of predictive analytics at scale with complete accuracy has only been a dream. Until now. Watch this video to learn how Vertica’s in-database machine learning empowers teams to put machine learning into operation – whether your data scientists want to build models in Python, or data analysts and engineers prefer a SQL-based approach.
Vertica’s in-database machine learning supports the entire predictive analytics process with massively parallel processing and a familiar SQL interface, allowing data scientists and analysts to embrace the power of Big Data and accelerate business outcomes with no limits and no compromises.
Want to know the latest in machine learning model development, data preparation, regression algorithms, clustering algorithms and more?
Check out the newly launched Digital Learning course: Predictive Analytics Using Machine Learning.
This 4-hour course, consisting of six self-paced modules, is designed with both new and experienced users. Improve your machine learning know-how, and put those algorithms to the test!
The VerticaPy library gives Python scale and performance
Python is the most popular language for machine learning workflows, but it can only work within a computer’s memory. To get the most accurate models, you need to prepare and train on data sets far beyond that limitation.
With VerticaPy, you can use full data sets without downsampling, do data preparation in a fraction of the time, and use every feature that contributes to accuracy with Vertica’s blazing-fast performance.
Are you interested in a technical deep-dive on Vertica’s built-in machine learning? Here is a paper, written by Vertica’s Chief Architect and Engineering leaders, that describes our distributed machine learning subsystem within the Vertica database. It includes Vertica’s current SQL machine learning functionalities that cover a complete data science workflow as well as model management.