Join the conference exhibitors as they discuss innovations and best practices in the field. Professional Development Units (PDUs) are available to those who attend these sessions.
Descriptions and times below:
Monday, April 17
Time:
9:10-10am
Location:
TBD
Machine Learning in Text Analytics: Do We Really Need Deep Learning?
Presented by: Normand Peladeau
The renewed enthusiasm for artificial intelligence (A.I.) and, more particularly, for techniques based on deep learning and other forms of neural networks, means that we are trying to apply these latest techniques to all problems requiring a supervised or unsupervised form of learning. But this unprecedented wave of interest often makes us forget there are other forms of machine learning that have proven themselves over time. During this presentation we will compare certain forms of machine learning with and without the contribution of neural network techniques in order to assess the importance and the nature of a possible contribution (if any). To do this, we will examine different tasks in the field of automatic language processing, namely topic modeling, automatic word disambiguation, and the development of semantic lexicons. We will also try to identify in which context an approach based on neural networks or deep learning deserves consideration.
Time:
11:30am-12:20pm
Location:
TBD
Quickly deploy your optimization models to the Cloud with DBOS!
DecisionBrain Optimization Server (DBOS) is designed to help build and deploy fully scalable optimization-based applications. It enables optimization developers to focus on their models, benchmark them and allows them to effortlessly deploy those models in production in a context that will support multiple parallel runs on dedicated resources.
To achieve this, DBOS lets you encapsulate any computational module (optimization solvers, analytics modules, etc.) into so-called “Workers.” Workers can be deployed on dedicated resources (local, private, or public cloud) to ensure the best execution time. When deployed on Kubernetes, Workers may be activated on-demand to reduce cloud costs.
DBOS can be used in a stand-alone mode to run computations, or it can also be integrated with existing applications to let them provide scalable and on-demand optimization capabilities and powerful monitoring capabilities.
DBOS also has a benchmarking functionality that allows you to benchmark your optimization engine across versions, different datasets, or models.
In this presentation, we will demonstrate how this technology can be used to:
- Encapsulate an optimization model in a Worker
- Deploy this Worker on a Kubernetes cluster using resources only on-demand
- Monitor Real-time Executions
- Benchmark models and datasets
Time:
3:40-4:30pm
Location:
TBD
DECIDE BETTER: the Decision Science lifecycle
Presented by: Matt Brady
What is the current state of the art in Decision Science? Attend this interactive workshop to see the full lifecycle in action, from pre-mortem to robust decision to post-mortem. Watch as actual audience scenarios go through a decision architecture process, and benefit from the collaborative decision optimization (TM) that the Volley platform enables.
Tuesday, April 18
Time:
11:30am-12:20pm
Location:
TBD
Making Dynamic Decisions with Simulation Analysis
Today’s decision makers are tackling increasingly complex problems, but they often have less time for analysis. Simulation can be a bridge between simple static analysis and large, expensive solutions. Learn how simulation modeling transforms your data into accurate predictions, and how it conquers the three enemies of static analysis: variability, time, and interdependency.
