Webinars

V7 Labs’ Alberto Rizzoli and CloudFactory’s Keith McCormick discuss overcoming data annotation challenges like scaling teams, labeling complex data, and handling edge cases.

In this webinar, we explore the definition, benefits, and use cases for human in the loop (HITL) machine learning from the perspective of seasoned data science practitioners.

Keith McCormick and Jonathan Reichental discuss smart cities, the data they collect, and the impact of AI technology like autonomous vehicles.

Keith McCormick and Usama Fayyad discuss autonomous vehicles, deep learning, NLP, and the future of data science in this on-demand webinar.

Interested in humans in the loop machine learning? Watch a replay of Keith McCormick and Robert Monarch’s discussion about HITL, workforce options, ethics, and active learning.

Now is the time to implement robotic process automation (RPA). We discuss maximizing ROI, workforce decisions, and exception processing with Ian Barkin of SYKES.

How should you combine your predictive models with existing business rules? Watch this discussion with James Taylor, CEO of Decision Management Solutions to learn how to optimize ML outcomes.

How can humans in the loop help optimize machine learning outcomes? Hear from ML experts CloudFactory's Keith McCormick and Dean Abbott, Chief Data Scientist at SmarterHQ.

Data annotation is seeing advances such as using AI and automation that seemingly promise to eliminate the need for human-powered labeling. This panel explores the future of humans-in-the-loop data annotation and what role your labeling workforce will play in the years to come.

Gathering an initial data set for your machine learning project is the first hurdle on the path to a successful ML algorithm. CloudFactory and Keymakr discuss the attributes of an ideal data set, the pros and cons of using a pre-created data set, and best practices for building your own.

CloudFactory’s Paul Christianson and Infinia ML data scientist Ben Schneller discuss what data scientists wish you knew about preparing your data for AI projects, data readiness strategies, and high-quality training data annotation at scale.

The end goal for every data labeling project is quality data - but how do you get there? There are several QA workflow types but each has pros and cons when it comes to the quality and speed of data outputs. In this panel discussion, we will explore 5 quality assurance workflows for data labeling including tooling, staffing, and how each workflow affects throughput and data quality.

It takes a mountain of data to train, test, and build machine learning algorithms and AI projects. You may be considering hiring a data labeling service to take the burden off your in-house data scientists and machine learning engineers. But what does that entail? Watch the webinar to learn what you need to know before hiring a data labeling service.

Developing high-performance deep learning models for computer vision requires a strategic combination of people, tools, and processes in pre-production. Watch the webinar to learn how to streamline your data labeling and experimentation process to accelerate your ML training and your time to market.

Data-science tech developer Hivemind designed a quantitative experiment to determine which type of workforce completed a series of increasingly complex tasks to deliver the highest-quality structured datasets. Watch the webinar to learn which workforce performed better.