WEBINAR: Extract Data from PDFs at Scale - 4 August 2022

Auto-extraction of unstructured and image PDF data is here.       Live Webinar   Extract Data from PDFs at Scale     Much of the valuable data locked in your PDFs is unstructured, contained in images, or both. Until now, analyzing that data took manual entry and transcription — time-consuming and expensive.   But now, it’s finally possible to extract that data automatically, without sacrificing efficiency for accuracy. In this live interactive conversation, our own VP of Data Science, Adam Blacke, reveals:   How much valuable data is hidden in your organization’s PDFs — data you couldn’t previously access   How new automated OCR breakthroughs can parse that data in a blink, with drag-and-drop ease   How these new efficiencies are transforming company after company — and how yours can be next     Join the webinar   Date   Thursday, Aug. 4, 2022   Time   9 a.m. Pacific   Save My Spot   Speakers     Adam Blacke VP of Data Science Alteryx     Chris deMontmollin Product Marketing Manager Alteryx

WEBINAR: AI vs unstructured data: Best practices for scaling video AI - 15 September 2021

 

AI vs unstructured data: Best practices for scaling video AI What: A free online webinar exclusively for IT Pros When: Wednesday, September 15th, 9 AM PST / 12 PM EST Where: From the convenience of your personal computer  Register Now In this latest Data Science Central webinar, Vincent Koops, Senior Data Scientist at RTL Netherlands, will walk through their Video AI platform at RTL and how they’ve addressed the challenge of scaling and automating their ML lifecycle when working with large unstructured datasets. Their platform is built on top of Pachyderm and Kubernetes to enable a wide range of ML applications such as automatic thumbnail picking and mid-roll marking. Register to learn: - How to take a modular approach to creating a scalable and automated ML platform - The challenges and best practices when working with unstructured data like video clips - Considerations your teams need to make to prevent human error while getting the most out of AI and ML Hope to see you there! Sean Welch Data Science Central

Understanding Feature Engineering - 4 part article by Dipanjan Sarkar via @TDataScience

Great 4 part series that you really need to set some time aside so you can sit and read these:

1 - Strategies for working with continuous, numerical data

2 - Strategies for working with discrete, categorical data

3 - Traditional strategies for taming unstructured, textual data

4 - Newer, advanced strategies for taming unstructured, textual data

Unstructured content: An untapped fuel source for AI and machine learning by Alex Welsh via @sdtimes

Advancements in AI (Artificial Intelligence) and machine learning now make it possible and affordable to sift through and find meaning in vast amounts of unstructured data obtained from video and audio files, emails, logs, social media posts and even notifications from Internet of Things (IoT) devices.

Nice to know that there is hope and something useful for unstructured data.

Dirty Data Is OK, How You Cleanse It Matters by Chirag Shivalker via @DZone

It has been an unsolved mystery for companies if they should get their data cleansed first to opt for data analytics or if they should opt for data analytics to conclude whether their data is dirty.

There are some really good points in this article.  I cannot emphasise enough the single source of truth point.  We must all have worked for organisations where department A's figures don't match department B's.  You cannot run an organisation if the numbers in your reporting don't match, and even worse you have no idea why they don't match. You need data management, agreed definitions for data, and just the one source of the truth across the entire company.