Artificial intelligence and machine learning frequently make headlines today, and enterprises are spinning up projects leveraging these technologies to enhance business operations and data-driven decision making. A key part of such solutions involves producing machine learning models. But, deploying and scaling those models in production applications – as well as broader Python and R-based solutions – can be more challenging. Oracle Machine Learning (OML) simplifies embedding AI and ML in applications.
In this Oracle DevLive | Level Up 2023 session, we explore why you use machine learning models, where models come from, and how you can embed AI/ML in your applications. Of course, it’s important to see how to get started with OML on Oracle Autonomous Database, so we’ll cover that before looking at a few demos.
As part of the introduction, we’ll look at different machine learning techniques and some typical enterprise business use cases.
We’ll look at ways to create models using OML from PL/SQL, Python, and R with in-database algorithms, as well as code/no-code AutoML. For example, Oracle Machine Learning for Python, provides functions to automate the machine learning modeling process so you can avoid repetitive and time consuming modeling tasks and the need to understand complex ML algorithm hyperparameters.
Join me to learn about machine learning models and how to embed them in your applications and dashboards through multiple code examples and demonstrations of the OML AutoML UI, OML Services REST API, and the new OML Notebooks features. Oracle Machine Learning provides multiple convenient way for developers and data scientists to produce enterprise-grade solutions. Try OML through Oracle LiveLabs workshops, such as OML Fundamentals for a closer look.