{"id":220,"date":"2022-11-28T17:11:29","date_gmt":"2022-11-28T17:11:29","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/?page_id=220"},"modified":"2023-04-10T17:21:05","modified_gmt":"2023-04-10T17:21:05","slug":"technology-tutorials","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/tracks\/technology-tutorials\/","title":{"rendered":"Technology Tutorials"},"content":{"rendered":"\n
\n
\n
\n
\n \n
\n
\n

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.<\/span><\/o:p><\/span><\/p>\n

Descriptions and times below:<\/b><\/p> <\/div>\n<\/div>\n <\/div>\n

\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n
\n

Monday, April 17<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n
\n

Time:
9:10-10am<\/b><\/p>\n

Location:
Cottonwood 11<\/b><\/p> <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n \"nextmv-logo-horizontal-color\" \n <\/div>\n \n \n <\/div>\n\n
\n
\n

Decision Model, Meet Production: A Collaborative Workflow for Optimizing More Operations\u00a0<\/h2>\n
Presented by:\u00a0Ryan O’Neil<\/div>\n
\u00a0<\/div>\n
When optimization technology works well, it feels magical. But it is not magic. Good decision optimization is both art and science. But the path to useful solutions that impact business operations is often fraught with roadblocks and dead ends — from model definition to solver setup to testing to deployment. So what’s the answer? The next era of optimization isn’t about building a better solver. It’s about collaborative, opinionated tooling that empowers teams to move faster with less confusion and more access to the decision technology ecosystem. The result: a decision optimization workflow that makes it possible to take a locally developed decision model and run it in a managed remote endpoint in minutes. This improves OR ops by reducing the time teams need to spend on infrastructure and maximizing the time they can spend on model development, testing, and iteration and ultimately derive more value out of their existing optimization stack.<\/div>\n
\u00a0<\/div> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n <\/div>\n\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n
\n

Time:
9:10-10am<\/b><\/p>\n

Location:
Cottonwood 10<\/b><\/p> <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n \"Provalis \n <\/div>\n \n \n <\/div>\n\n
\n
\n

Machine Learning in Text Analytics: Do We Really Need Deep Learning?<\/h2>\n

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.<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n <\/div>\n\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n
\n
\n \n
\n
\n

Time:
10:30-11:20am<\/b><\/p>\n

Location:
Cottonwood 10<\/b><\/p> <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n \"Gurobi_new_black_logo\" \n <\/div>\n \n \n <\/div>\n\n
\n
\n

Gurobi’s Newest Educational Resources: Where Data Meets Decisions – An Overview of Our Free Jupyter Notebook Data Science Example Library<\/h2>\n

Presented by: Rahul Swamy and Jerry Yurchisin<\/p>\n

How can you use different prediction models for avocado price optimization? How can you identify plagiarism with text similarity? How can you effectively plan for airline disruption in time of continual flight delays and cancelations? How can you discover lesser-known artists in your daily music playlists? How can you build the perfect fantasy basketball team?<\/p>\n

By combining data science tools and mathematical optimization.<\/p>\n

In this session, Gurobi will introduce several of our newest (and free) educational examples that students and instructors can use to learn and teach real-world applications of combined data science and optimization problem solving. We will review our new data science library of Python Notebook Examples that combine predictive and prescriptive analytics and offer new data science learners an entry point into problem-solving with optimization.\n<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n \n

\n <\/div>\n\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n
\n
\n \n
\n
\n

Time:
11:30am-12:20pm<\/b><\/p>\n

Location:
Cottonwood 10<\/b><\/p> <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n \"DecisionBrain\" \n <\/div>\n \n \n <\/div>\n\n
\n
\n

Quickly Deploy your Optimization Models to the Cloud with DBOS!<\/h2>\n

Presented by:\u00a0Giulia Burchi and Filippo Focacci

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.<\/span> To achieve this, DBOS lets you encapsulate any computational module (optimization solvers, analytics modules, etc.) into so-called \u201cWorkers.\u201d 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: \u00a0<\/p>\n