Prepare your data management architecture for machine learning at THINK
With 82 percent of organizations at least considering artificial intelligence (AI) adoption, it’s safe to say that business leaders are realizing it is the key to deeper insights and competitive advantage. Yet one thing often overlooked is the data, or more specifically, the data management and architecture that fuels AI.
Machine learning (ML) and AI rely upon a corpus of usable data. Whether the goal is to answer a specific query or train a model based on an abundance of data points, the ability to reliably access a wide range of information is crucial. This has become more difficult recently due to the ever-increasing volume of data being created at incredible speed, which varies in both type and location. Some legacy architectures aren’t able to keep up with these changes in the data landscape, meaning their AI practice will suffer because of an inability to access the full breadth of available data that could be informing models and insights.
Fortunately, modern architectures are taking the ML and AI future into account, providing more integrated environments capable of handling the volume, variety, and velocity of today’s data. Think 2019, taking place in San Francisco from 12 through 15 February, presents the perfect opportunity to learn more about these solutions. There will be a wide variety of sessions dedicated to machine learning, including general overviews, discussions with customers who are putting machine learning solutions in place, and technical sessions with a deep dive on how to build a foundation for ML.
Don’t miss these top AI and ML sessions
With the rise in the volume and speed at which data is created, thanks to advancements such as the Internet of Things, one of the hottest sessions is sure to be “Fast Data for Real-Time Analytics and Action.” Those who attend will discover how to uncover insights that would have previously passed them by with the help of the machine learning and open source tools found in IBM Db2 Event Store. They will also discover how Lightbend helps build an end-to-end fast data platform for app development, which uses Event Store’s speedy data ingestion and real-time analytics.
Another top-tier session, “Same Data, New Game: Learn How to Extend Your BI Stack with Machine Learning”, will elaborate on how to ensure you’re getting the most out of your data. Many organizations have implemented business intelligence (BI) with tools such as IBM Cognos or Tableau, but machine learning provides the opportunity to use the information in your data warehouse to much greater effect. Join this session and learn how IBM Watson Studio was engineered to provide data scientists with the ability to train powerful machine learning models on the data that’s already sitting in your warehouse.
Learn how your peers are achieving AI and ML success
One of the best parts of Think is hearing details of successful implementations of hybrid data management solutions and machine learning directly from peers across a variety of industries. For instance, you’ll hear how IBM Integrated Analytics System was used as part of an advanced logistics platform to help meet customer demand for faster deliveries at lower cost. Advancements from the financial sector will also be shared, including the recent loan rating application built using IBM Hosted Analytics with Hortonworks to house its customer data. The convenient access to data helped the developers create and train a robust machine-learning model, with the goal of minimizing the inherent risk of providing loans.
Go hands on for deep technical knowledge
If you want to go even deeper into machine learning solutions, Think 2019 offers a variety of technical sessions. In the AI Think Tank session, “Developers: Use Your On-Premises Data for Machine Learning in the Cloud”, Principal Offering Manager for Db2 Roger Sanders will demonstrate how to connect a Db2 Developer-C database to Watson Studio, use the connection to build a prediction and deploy it as an API endpoint. Attendees can see firsthand the benefits of using cloud resources on a more complete set of data for machine learning.
“Predictive System Behavior and Degradation Compensation with IBM Machine Learning for z/OS”, a use case from IT service provider Fiducia GAD will also be presented. The session will demonstrate how IBM Machine Learning for z/OS can assist in the management of different workload behaviors as well as identifying system degradation and bottlenecks.
No matter which session you choose to attend at Think 2019, you’ll walk away with a better sense of how to build your data foundation for machine learning and AI, and the success that other businesses have found. With the increased interest in machine learning and questionable ability to deliver on it with current data foundations, these sessions will help put you a step ahead in building your foundation for AI.