Data science is a community-driven effort, defined by the effort of many individuals and organizations looking to push the nature of open-source data. As such, we want to showcase a variety of talks that represent the best in our community. Below is a list of free ODSC East 2020 Virtual Conference talks and workshops that you can view today, Thursday 4/16.
All times in EST
10:40am: Raising Your Analytics from Infancy to Maturity: Dr. Brett Wujek | SAS
In this session, learn about the challenges companies face during their digital transformation, and how issues throughout the entire analytics lifecycle must be accommodated to transform data into action to drive value and make better, faster decisions.
10:40am: Deep Dive in Wenju–A Solution Platform for Enterprise AI: Changfeng Charles Wang, PhD | Futurewei
Learn more about Wenju, a platform that helps enterprises of any size to quickly and cost effectively build end-to-end AI solutions in hybrid and multi-cloud environments.
10:40am: End to End Modeling and Machine Learning: Jordan Bakerman, PhD and Ari Zitin | SAS
In this workshop, you will load data into memory, prepare input variables for modeling and build complex analytics pipelines to demonstrate powerful machine learning models.
11:30am: Training and Operationalizing Interpretable Machine Learning Models: Francesca Lazzeri, PhD | Microsoft
In this talk, we will introduce some common challenges of machine learning model deployment and we will discuss various points in order to enable you to tackle some of those challenges, such as how to select the right tools to succeed with model deployment.
12:45pm: Accelerate ML Lifecycle with Kubernetes and Containerized Data Science Tools: Abhinav Joshi and Tushar Katarki | Redhat
The session will provide an overview of containers and Kubernetes, and how these technologies can help solve the challenges faced by data scientists, ML engineers, and application developers.
12:45pm: In the Defense of Data: Delivering Value During a Global Crisis: Alexander Dean | Snowplow Analytics
Alexander will present some strategies for deepening a business’ value, for example by: moving from backward to forward data deployments; breaking down functional data silos; piloting the killer app that everybody says couldn’t be done, and more.
2:15pm: Best Practices in Deep Learning and the Art of Research Management: Moses Guttmann | ALLEGRO AI
In this workshop, we share our experience from numerous deep learning projects. We will discuss topics such as hyperparameter optimization search, data biases, diminishing returns of annotated data and productive use of an experiment management platform.
2:25pm: Deep Learning for Tabular Data: A Bag of Tricks: Jason McGhee | DataRobot
By taking a disciplined approach to tuning hyperparameters, leveraging some recent techniques, and building some intuition, Deep Learning can be a useful approach to learning heterogenous tabular data.
2:25pm: How Retailers Can Automate AI/ML in Minutes: Hiroaki Shioi | dotData
How can retailers empower BI professionals to implement AI, Machine Learning, or Predictive Analytics? Join dotData’s Senior Data Scientist, Hiroaki Shioi, as he discusses AutoML 2.0: The latest advancement in Automated Machine Learning.
3:15pm: DevOps for Machine Learning and other Half-Truths: Processes and Tools for the ML Life Cycle: Kenny Daniel | Algorithmia
This talk will cover: Key differences between ML and traditional software development, where the SDLC works with ML, and where it breaks down, an overview of the new ML stack, from training to deployment to production, and more.
3:15pm: Workflow Design for Natural Language Annotation Teresa O’Neill: PhD | iMerit
This talk draws on iMerit’s experiences as a natural language annotation partner, laying out our approach to five key processes in the annotation pipeline: (i) exchange of expertise, (ii) annotator training, (iii) workflow design, (iv) feedback cycle, and (v) quality evaluation.
Want more? You can still obtain an on-demand pass to see all of the talks that you missed during the week. Learn more here.