Webinars & Live Streams

Join us for live or on-demand discussions, tutorials, or demos on web scraping, knowledge graph creation, and structured public web data.

[E3] 4 Ways Technical Leaders Are Using Natural Language Processing To Drive Data Transformations

Join us June 10th as we explore ways in which Natural Language Processing is promoting data transformations in ecommerce, human resources, market intelligence, and news monitoring. Register for this on-demand webinar here.

[E2] How To Wrangle The Entire Web Into A Valuable Media Monitoring Dashboard

In this webinar we work through the three “building blocks” of structured public web data that enables world-class media monitoring dashboards. Register for this on-demand webinar here.

[E1] Are Rules Meant To Be Broken? Rule-based Versus Rule-Less Web Data Extraction

Humans don’t follow a rigid set of rules when we read the web. So why should web data extraction bots? Register for this on-demand webinar here.

Other Events

DataBytes Netcast Episode 4 With Ganes Kesari

In this DataBytes we talk with co-founder and chief decision scientist at Gramener. Ganes steps viewers through a key issue he sees between the relationship between data and decision-making teams: that they don’t decide on a business problem to solve before deciding on a data problem.

DataBytes Netcast Episode 3 With Doug Laney

In this DataBytes we talk with bestselling author, thought leader, and professor Doug Laney on why he often sees data teams fail to treat their data as an asset.

DataBytes Netcast Episode 2 With Praveen Balaji

In this DataBytes netcast senior architect at Diffbot Praveen Balaji talks about how we can take the hype out of machine learning. This leads the way to a much wider array of software engineers integrating ML tooling and techniques into their workflows.

DataBytes Episode 1 With Julia Wiedmann

In our inaugural DataBytes netcast we talk with knowledge graph researcher at Diffbot, Julia Wiedmann. She details how important a focus on stepping back and assessing bias in ML training data can be. Additionally, Julia talks through the importance of end-to-end machine learning.