OnDemand Filterable List

DCLA 2021 OnDemand

wdt_ID Full Name Track Company Org Title Title Description
1 Nishtha Chouhan AI/ ML/ Data Science Denodo Technologies Senior Product Marketing Manager Advanced Data analytics and ML using Logical Data Fabric Why should Logical Data Fabric be at the backbone of an organization's data architecture? When the data landscape is fragmented and businesses require advanced analytics for decision making, Logical data fabric provides an agile and governed approach to b
2 Rustem Feyzkhanov AI/ ML/ Data Science Instrumental Senior Machine Learning Engineer Building scalable end-to-end deep learning pipelines in the cloud My presentation will show how to utilize services like AWS Batch, AWS Fargate, Amazon SageMaker, AWS Lambda, and AWS Step Functions to organize scalable deep learning pipelines.
3 Cupid Chan AI/ ML/ Data Science 4C Decision Managing Partner Catch me if you can - How to fight Fraud, Waste and Abuse using Machine Learning AND Machine Teaching In 2020, I led a committee in Linux Foundation to research a topic around Human-Centered AI. One point is about machine TEACHING, in addition to machine learning. This presentation is to see how this can help in fighting Fraud, Waste and Abuse along with
4 Bunmi Akinremi AI/ ML/ Data Science Zindi Data Science Mentor Deploying your model to mobile app: Offline or Online? I'll be talking about various ways companies and individuals deploy models to mobile apps, benefits and disadvantages of deploying offline, benefits and disadvantages of deploying online, and when you should deploy online or offline.
5 Debu Sinha AI/ ML/ Data Science Databricks Sr Solutions Architect Detecting fake reviews at scale using Apache Spark and John Snow Labs In this session I will walk through and end to end NLP pipeline using John Snow Labs on Databricks to detect fake reviews at scale while utilizing MLFlow to manage model lifecycle.
6 Paige Roberts AI/ ML/ Data Science Vertica Open Source Relations Manager In-Database Machine Learning with Jupyter Jupyter with Python code is a productive way to prepare models, but putting machine learning models into production at scale may require re-building the entire workflow. Using the same interactive tools, but letting a distributed database do the work coul
7 Sayantika Banik AI/ ML/ Data Science Twilio Data Engineer Lie factor and it's impact on visualisation With the increasing complexity of graphs, we tend to forget the basic integrity principles. Lie factor is one such principle that gets subconsciously incorporated in the graphs, resulting in false interpretation.
8 Ginni Malik, Ying Wang AI/ ML/ Data Science Amazon Web Services/ Amazon Web Services Data & ML Engineer/ Senior Big Data Consultant Talk to Your Data: Query Your Data Lake with Amazon QuickSight Q Ask questions on Amazon QuickSight Q and get prompt answers by using ML-powered, natural language query (NLQ) capabilities that empower users to ask questions about data using everyday business language.
9 Elijah Ben Izzy AI/ ML/ Data Science Stitch Fix Data Platform Engineer The new ML Platform at Stitch Fix In this talk, we will discuss the system we built for Data Scientists to easily get their models into production. We will focus on the abstraction we used to write a model, and how that enables us to simplify their workflow with self-service, configuratio
10 Christian Bourdeau BI/ Reporting/ Business Use Cases Sony PlayStation Sr. Business Intelligence Engineer How to Break into Business Intelligence & Data Analytics How to Break into Data Analytics & Business Intelligence
Full Name Track Company Org Title Title Description