Presented by Perceptilabs
Did you ever wonder what goes on inside the brain of a data scientist?
A few years ago, PerceptiLabs, a deep tech startup, took on an ambitious goal — to visualize what data scientists see when they are building a machine learning model. In doing so, they reinvented the process of model building, making it simpler and faster for experts and beginners alike, to build, train, and analyze their models, so companies could speed up their innovation process.
It’s not news that AI is transforming the world in which we live. Banks are using AI to identify potential fraud, healthcare providers use AI to assist with diagnosis, grocery stores build algorithms to predict consumer behavior, and much more. Today, as businesses rush to accelerate their digital transformations due to COVID-19, AI is becoming more crucial, penetrating more business-critical functions.
To enable AI to do all these great things, the field has generally relied on experts (highly trained data scientists) to build and train complex mathematical models also called machine learning models. This is a complex time-consuming process, involving thousands of lines of code. To see what the models were doing, the experts have to use their imagination to visualize the models in their heads.
As AI and ML took hold and the experience levels of AI practitioners diversified, efforts to democratize ML materialized into a rich set of open source frameworks like TensorFlow and datasets. Advanced knowledge is still required for many of these offerings, and experts are still relied upon to code end-to-end ML solutions. This can have some advantages when building customized solutions, but can require a large investment in resources, infrastructure, and maintenance.
More recently a variety of AutoML tools have launched, promising end-to-end capabilities, where data is input, parameters are adjusted, and a fully-trained, deployable ML model is generated. The simplicity of this sounds inviting — indeed it’s appropriate in certain scenarios — however, ML models created through AutoML often lack transparency into their performance and can be difficult to interpret (i.e., explain why they produce certain results). As well, AutoML solutions often restrict users to only a few ML techniques.
The next generation of ML modeling
PerceptiLabs has developed a next-generation ML tool with our visual modeler that took the best of all worlds: the flexibility of code, some of the automation in connecting components, generating model architectures as well as tuning settings and hyperparameters, combined with the ease of a drag and drop UI.
This makes model building easier, faster, and accessible to a wider spectrum of users, whether you are an expert or beginner. There is also the ability to create custom models like simple linear regression, or something more complex like a GAN.
We designed our tool as a visual API on top of TensorFlow, which has grown to become the most popular ML framework. This gives developers full access to the low-level TensorFlow API and the freedom to pull in other Python modules.
Most importantly, users have full transparency into how their model is architected and a view into how their model performs. The result is a new visual approach that’s almost as good as seeing inside a data scientist’s brain!
ML modeling approaches at a glance
There are a lot of choices when it comes to building machine learning models, and each approach needs to be carefully evaluated against the resources you have available to see it through.
That’s why here at PerceptiLabs, we think that our new visual way to build machine learning models, strikes just the right balance across a wide spectrum of ML users while offering better explainability, sophistication, and usability. It’s a flexible but comprehensive approach, that lets you choose the way you want to work, depending on your experience and project needs.
To learn more visit us at www.perceptilabs.com.
Martin Isaksson is Co-Founder and CEO of Perceptilabs.
Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. Content produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact email@example.com.