Analytics and Data Summit 2020

Get an overview of Oracle's platform ML solutions including new approaches using machine learning and data science. This presentation will cover Oracle’s strategy and solutions for ML across all of Oracle's cloud platform and how they support both data scientists and the extended data science team of data engineers, business analysts and application developers. Learn how to take advantage of machine learning capabilities embedded in Oracle Database, Hadoop and Oracle Analytics, and how Oracle's Data Science service improves teamwork and shortens time to value.

You've finally decided to migrate your legacy Monolithic applications for modern Microservices based architecture. What are the challenges, trade-offs and best practices? This sessions introduces the motivation to adopt MicroServices applications and a quick summary of Cloud Native Platform, Kubernetes and Microservices communication (Event Sourcing, CQRS). It then discusses Microservices transaction management (local/flat transactions, SAGAs, compensation functions, techniques for persisting Events and States) and the best practices for splitting your backend layer which runs your business logic (e.g., split along bounded contexts) and tools (automating compensations functions i.e., no to little coding). The session proceeds with MicroServices data management (polyglot persistence, data models and trade-offs), and best practices for migrating your monolithic databases (e.g., split along bounded contexts) and tools.

Oracle Machine Learning supports data science and machine learning with Oracle Database, Oracle Autonomous Database, and Oracle Big Data-related products and technologies. Join this session to learn about the family of products that make up Oracle Machine Learning and our product roadmap. Oracle Machine Learning consists of SQL, R, Python, and REST APIs, as well as Apache Zeppelin-based notebooks, Big Data support on Spark, and rich microservices supporting model management and cognitive text and image services.

In this introductory hands-on-lab, try out the new Oracle Machine Learning Zeppelin-based notebooks that come with Oracle Autonomous Database. Oracle Machine Learning extends Oracle’s offerings in the cloud with its collaborative notebook environment that helps data scientist teams build, share, document, and automate data analysis methodologies that run 100% in Oracle Autonomous Database. Interactively work with your data, and build, evaluate, and apply machine learning models. Import, export, edit, run, and share Oracle Machine Learning notebooks with other data scientists and colleagues. Share and further explore your insights and predictions using the Oracle Analytics Cloud.

Putting Machine Learning Models into product can be a challenge. Real value comes when machine learning models can be shared throughout the organisation. This presentation will demonstrate how models create in the Oracle Database and Autonomous Databases can be REST enabled, thereby allowing developers to easily use the models for scoring new data. This presentation will present a demonstration of creating the REST APIs for the models, and then uses call using a web browser and from Python. This allows real-time scoring of data from any of languages used for your production applications.

Every want to have an “algorithm in the Cloud” help you pick a good wine for a fair price? This detailed demo and presentation walks you through the entire process; launching ADW, uploading data, creating an Oracle Machine Learning notebook, exploring the data, building machine learning models, applying the models and most importantly, picking a good bottle of wine! We’ll even review our machine learning insights and predictions using interactive graphing in Oracle Analytics Cloud and possibly show how to deploy our model into Apps using Oracle REST Data Services (ORDS). This presentation demonstrates how developers and DBAs can quickly become machine learning experts. Examples illustrate the different machine learning options in Oracle Autonomous Data Warehouse and Oracle Autonomous Transaction Processing, showing what can be achieved very quickly and with a few lines of code. So, sit back, grab a glass of fine wine and learn some practical and fun Oracle machine learning Tech Tips.

The low cost and simplicity of Autonomous Database make Oracle Database as accessible to individuals, departments and small and medium businesses (SMBs) as to Oracle's traditional corporate customers. The next step is to extend the benefits of autonomy beyond the DBA to other players in the data management space. This audience needs a comprehensive, integrated suite of tools that breaks down silos and promotes collaboration.

DBAs spend time dealing with maintenance, security and outage issues with little time for innovation. Self-driving, self-securing and self-repairing Autonomous Databases free data professionals to leverage domain knowledge and data skills to extract more value from their data. Oracle Machine Learning, part of Autonomous Database, “moves the algorithms to the data” for in-database processing of machine learning algorithms that eliminate data movement. Now, the entire machine learning process: problem statement, data understanding and preparation, model building and evaluation, model apply and model deployment runs inside the Oracle Database. Database developers perform many supporting tasks today, typically 80% of the work, but refer to them as ETL, data wrangling, data transformations and productionizing scripts. Come learn how ADW, ATP and Oracle Machine Learning makes it easy for you to easily expand your valuable data skills and become “data scientists” in just 6 weeks!