Presented at RECONNECT Dive Deep 2021

Session ID: 101930

Join Pratap Madgula, Director of Innovation and Implementation at Kaiser Permanente, to hear about emerging technologies like RPA, chatbots, machine learning, AI, and Oracle Analytics Cloud Services and how Kaiser Permanente utilizes them.

Presented at Database & Technology Week 2021

Using data to gain insights, make predictions, and identify patterns has become a priority for most enterprises. Machine learning is a key technology, but it often requires specific skill sets to take advantage of. Even for experts, the machine learning process can be highly iterative and involve significant trial and error. To help make machine learning more accessible to a broader set of users and enhance data scientist productivity, Oracle introduced automated machine learning, or AutoML, both from a Python API and a no-code user interface. In this session, we introduce the machine learning process and how AutoML plays a key role in the modeling process. We’ll include a demonstration of the no-code OML AutoML UI and the OML4Py AutoML features.

Presented at Database & Technology Week 2021

This presentation will focus on what specific algorithms inside Oracle can be applied to business problems. Robots may be the first to truly learn machine learning (ML) and expand into artificial intelligence (AI). Python is one of the keys to machine learning as we program the invention of the mind to further replace humans’ most basic tasks. This session will focus on ML 101 and building the future ahead. Some business issues are seasonal, some relate to customers with certain attributes, and some relate to customers we don’t know exist, but all can be solved by using the correct algorithm to quickly prescribe a better corporate future.

Presented at INSYNC 21

Session ID: 101380

Oracle Machine Learning’s new AutoML UI makes ML easier than ever before.  OML’s AutoML automates many of the time-intensive and repetitive aspects of a data scientist’s job including algorithm selection, feature selection, and hyperparameter tuning.  AutoML UI enables “citizen data scientists” to leverage AutoML in ADW with a simple, intuitive, easy to use UI.   Using AutoML UI, data scientists and citizen data scientists can focus on the important phases of ML: Defining the problem, assembling the “right data”, disseminating the model’s insights and predictions, and deploying the model enterprise-wide.  Come see the new AutoML UI and learn how easy it is to get started.

Presented at INSYNC 21

Session ID: 101970

In this Hands-on Lab, join us to experience Oracle Machine Learning for Python (OML4Py) on Oracle Autonomous Database. OML4Py supports scalable, in-database data exploration and preparation using native Python syntax, invocation of in-database algorithms for model building and scoring, and embedded execution of user-defined Python functions from Python or REST APIs. OML4Py also includes the AutoML interface for automated algorithms, feature selection, and hyperparameter tuning. In this six-part lab, explore the range of Python-enabled functionality and see how Oracle technology can support your data science projects.

Presented at INSYNC 21

Session ID: 100100

Oracle Machine Learning comes free in every Autonomous Database. Oracle Machine Learning Notebooks provide easy to understand, reuse and repurpose ML methodologies that apply OML’s 30+ machine learning algorithms for in-database, parallelized implementations of classification, regression, clustering, anomaly detection, text mining, associations machine learning functions that can be used to tackle a wide variety of data-driven problems and to build “predictive” applications. This session will step through five (5) OML notebooks that can serve as the core of many data science projects:  classification, attribute importance, time-series forecasting, text mining and anomaly detection.

Presented at INSYNC 21

Session ID: 100970

Many Machine Learning tasks need to access a lot of data set, which in many cases are stored in a database such as Oracle Database.  It makes a more scalable solution to do the machine learning task in the database, which is called in-database machine learning.  Oracle Autonomous Data Warehouse (ADW)  comes a library of Oracle machine learning algorithms and a set of building tools such as SQL notebooks  for machine learning.  This allows Data scientists to run Machine Learning projects in Oracle Database without moving data. This session will examine Oracle Machine Learning as part of Oracle database  as well as ADW . We will discuss process of machine learning:  analyze and prepare data set;  build and evaluate and apply machine learning model. We also will discuss the  Oracle machine learning features in Oracle 21c such as  AutoML for In-Database Machine Learning and newly added in-database machine learning algorithms for anomaly detection, regression, and deep learning analysis.

Presented at Quest Experience Week (QXW) – Database & Technology Day

Solving today’s business problems increasingly relies on machine learning techniques to leverage the volumes of data enterprises both amass and have access to. Oracle Machine Learning empowers data scientists, data analysts, DBAs, application developers, and executives to realize machine learning-based solutions faster and more easily. Automation of the model building process, referred to as AutoML, along with the ability to scale to enterprise data volumes while maintaining data security in Oracle Database, all come together to meet enterprise needs. In this session, learn about the in-database machine learning features and services, including those related to Oracle Autonomous Database. This session will include demonstrations and previews of Oracle Machine Learning functionality.

Quest Forum Digital Event 2020

The current buzz in the IT world around Robotic Process Automation (RPA) and machine learning has PeopleSoft professionals thinking about how to take advantage of these new technologies.  This session will explain the key features of RPA and machine learning tools and discuss how they can interact with PeopleSoft and compliment PeopleTools.  We will discuss use cases and the pros and cons of when to use these tools versus a native PeopleSoft feature.