In my October 2022 article, "How to choose a cloud machine learning platform," my first guideline for choosing a platform was, "Be close to your data." Keeping the code near the data is necessary to keep the latency low, since the speed of light limits transmission speeds. After all, machine learning -- especially deep learning -- tends to go through all your data multiple times (each time through is called an epoch). The ideal case for very large data sets is to build the model where the data already resides, so that no mass data transmission is needed. Several databases support that to a limited extent.

The Vertica SQL database and in-database machine learning solutions support the entire predictive analytics process with massively parallel processing and a familiar SQL interface. After a brief dip due to the impact of the pandemic, business analytics services resumed double-digit growth in 2021 and 2022, according to IDC. There is a great need to improve business outcomes using insights from analytics and other techniques, including machine learning. What most businesses find when undertaking new analytics and machine learning (ML) projects is that their current infrastructure is not up to the task. The projects typically need compute and storage capabilities that are not typically available in most businesses.

Another of the new machine learning algorithms in Oracle 21c Database is called XGBoost. Most people will have come across this algorithm due to its recent popularity with winners of Kaggle competitions and other similar events. XGBoost is an open source software library providing a gradient boosting framework in most of the commonly used data science, machine learning and software development languages. It has it's origins back in 2014, but the first official academic publication on the algorithm was published in 2016 by Tianqi Chen and Carlos Guestrin, from the University of Washington. The algorithm builds upon the previous work on Decision Trees, Bagging, Random Forest, Boosting and Gradient Boosting.

Does anyone write code to access data in a database anymore, and by code I mean SQL? The answer to this question is'It Depends', just like everything in IT. Using REST APIs is very common for accessing processing data with a Database. From using an API to retrieve data, to using a slightly different API to insert data, and using other typical REST functions to perform your typical CRUD operations. Using REST APIs allows developers to focus on write efficient applications in a particular application, instead of having to swap between their programming language and SQL.

In my August 2020 article, "How to choose a cloud machine learning platform," my first guideline for choosing a platform was, "Be close to your data." Keeping the code near the data is necessary to keep the latency low, since the speed of light limits transmission speeds. After all, machine learning -- especially deep learning -- tends to go through all your data multiple times (each time through is called an epoch). I said at the time that the ideal case for very large data sets is to build the model where the data already resides, so that no mass data transmission is needed. Several databases support that to a limited extent.

Will the new Vertica 9 platform from Micro Focus lead the way for other analytics platform providers in enhancing machine learning capabilities? Micro Focus has announced the release of Vertica 9, a new analytics platform offering enhanced machine learning capabilities. The new in-database machine learning developments are intended to simplify the creation and deployment of machine learning models, bringing on board new algorithms and data preparation functions. In addition to this, continuous end-to-end workflows and model replication are also new features included in Vertica 9 to achieve this goal. New support for the Google Cloud Platform will enhance the user's freedom, while scalability and performance have also been targeted.