Tag: Machine Learning

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.

Building on its COLLABORATE legacy, Quest is excited to introduce a BRAND NEW virtual conference, exclusively for Oracle Database and Technology users - INSYNC! The conference will take place on March 30 - April 1, 2021. We can't wait for you to join us, and INSYNC education is now live! Begin browsing the great education and networking opportunities we have in store for you!

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.

Attend our webinar: LTI, through its deep understanding of domains, together with its extensive, acclaimed experience in Oracle ERP, has devised an approach and execution strategy to realize the vision of TERP. Join us in a webinar aimed at delivering a positive outlook towards its possibilities, role as an ‘outcome enabler’ and an elaborate explanation on the approach for practical execution.

Jul 22 @  11:00am

The concept of Touchless ERP (TERP) is realized through the implementation of connected devices, touchless digitization, embedded Machine Learning, Digital Analytics and conversational applications to deliver a “new way of working” for users. Their collective outcome delivers automation of the injection, reception, and processing of data, and delivery of output. This is further extended to…

Quest Forum Digital Event 2020

Oracle Machine Learning “moves the algorithms to the data”, processing data where it resides—in the Oracle Database (On-premises, Cloud, and Autonomous).   This next-generation, hybrid, combined Data Management, and Machine Learning platform enables companies to harvest more insights from their data, eliminates data movement, delivers scalability, preserves security, and accelerates model deployment. Oracle Machine Learning’s 30+ in-database algorithms can address many business problems e.g. predicting behavior, finding anomalies and detecting potential fraud, recommending “next best offers”, sentiment analysis, anticipating churn and discovering key attributes.   OML supports the key roles—data scientists, business and data analysts, database and application developers, and executives with ML algorithms, SQL, R and Python APIs, AutoML, notebooks, UIs, and tight integration with open-source R and Python.  Come hear the latest developments, how to get started and what’s coming next.