Ambili T

Machine Learning scares you? Are you naive when it comes to making predictions using ML models? Oracle Analytics with its robust ML capabilities is here for your rescue. Multiple case studies covering the end-to-end journey will teach you to do this the Oracle Analytics Cloud way – with ‘No Code’! Without the need to write a single line of code, do you know want to harness the power of Machine Learning, through Oracle Analytics Cloud? Well, this is the session wherein you will experience the end-to-end development journey of the same. Let us build a machine learning workflow and witness the ease with which OAC enables you to do – loading, cleansing, data transformation, machine learning model creation and eventually the model evaluation. This session is intended to inspire and empower you with the ML capabilities of OAC which helps even a beginner to sail through with ease!

Mark Hornick

Oracle Machine Learning (OML) enables data exploration and preparation, and model building, evaluation, and deployment at scale. For Oracle Autonomous Database in early 2021, OML expanded to include an integrated Python API (OML4Py), REST services for model management and deployment (OML Services), and a no-code user interface supporting automated machine learning (OML AutoML UI). Since then, we’ve introduced multiple enhancements and new features to the OML product suite. Join us for this session to learn about the new Oracle Machine Learning capabilities along with demonstrations. 

Žiga Vaupot

Traditionally, the use of machine learning is in the domain of the data scientist. The latter has in-depth knowledge of machine learning methods and algorithms and strives for the most optimal preparation of machine learning models, which are then used for various analyses such as predictions, customer segmentation, anomaly detection, finding patterns and the like. Because the machine learning process usually takes a long time, even a few weeks, the data scientist is a rather “rare species”. Business users usually need results quickly and often don’t have time to wait their turn. They are faced with the dilemma to choose between lower accuracy over the speed of development of machine learning models.

There is no doubt that there are business problems which require machine learning models that provide accuracy as high as possible, but on the other hand there are situations where speed of finding an advanced analytics solution is even more important and when “good enough” model is acceptable to the business.

Regardless of the dilemma described above, one thing is in common to both approaches. It is the machine learning process which must be implemented in any case. It is now commonly expected that this is supported by any analytics tool. Oracle Analytics certainly fulfills this requirement.

The workshop Business Users and Machine Learning in Oracle Analytics will look at not only a few examples where a business user can use machine learning algorithms, but also what options are available for the whole machine learning process and how they can use them independently, without the involvement of data scientists and/or other IT professionals. Of course, we will also look at scenarios when data scientists are available and how the results of their work can also be used in business analysis.