Labelbox•July 16, 2021
Our second Labelbox Academy event took place on June 30th, 2021, and covered some of the more complex capabilities available with our training data platform, along with best practices and the philosophy behind these features. Read on to learn three important takeaways from the event.
Since Labelbox released its Model Assisted Labeling (MAL) feature, many of our customers have used it to reduce human effort and costs over time. The Accelerate your pipeline with Model Assisted Labeling session included a detailed demonstration of how an ML team can use the feature to bring a model’s output back into Labelbox, correct its labels, and retrain it to achieve higher accuracy.
The session also featured ImageBiopsy Labs’ Senior ML & AI Engineer Caroline Magg, whose team used MAL to sort large datasets of medical imagery. They labeled a batch of data and trained their first model, which generated predictions on a second batch of data that the team also needed to annotate. The output was then corrected using MAL in Labelbox, creating a larger, more enriched dataset for the team.
“Once you finish the first iteration, the wheel is spinning,” said Magg. “We were able to double the amount of data we could handle in a given time with Model Assisted Labeling.”
The event also included a session about best practices in labeling operations. Led by Labelbox COO and Cofounder Brian Rieger and Labelbox Director of Labeling Operations Audrey Smith, the session included a discussion of common misconceptions surrounding the subject.
In software development, there are plenty of tools and services that allow teams to monitor their product’s performance by providing traces, logs, metrics, and dashboarding and other investigation tools. ML teams, however, currently lack these resources. In the Monitoring the performance of deployed models session, Labelbox Product Manager Gareth Jones and ML Engineer Matt Sokoloff discussed three primary challenges facing ML teams that want to monitor their models:
If you’d like to watch any of the sessions from Labelbox Academy: Mastering the platform, please contact us at firstname.lastname@example.org or send a request through the chatbot on this site. You can also learn more about MAL, labeling operations, and Model Diagnostics here on labelbox.com.
Labelbox•September 29, 2021
Labelbox lands in London: The key to a performant ML model lies within your training data
In a data-centric approach to machine learning, no element is more essential in your ML endeavors than creating and maintaining high-quality training data. Many ML teams fail due to two common but critical mistakes.