The field of Digital image processing is increasingly shifting from deterministic mathematical calculations and statistical methods, to machine learning (ML)-based approaches which can provide better and more accurate results. This in turn is also helping to drive new methods and use cases for computer vision in the pursuit of extracting information from images.
The use of ML for image processing and computer vision is a growing part of ML research. While it’s a hot topic in and of itself, it’s driving some of the hottest topics in the tech industry including robotics, self-driving cars, and facial recognition.
In this blog…
In A New Visual Approach to Machine Learning Modeling, we talked about how TensorFlow is one of the most popular machine learning (ML) framework today, but it’s not necessarily an easy one for beginners to start building ML models.
That’s why we decided to create a GUI on top of TensorFlow. With PerceptiLabs, beginners can get started building a model more quickly, and those with more experience can still dive into the code. …
Machine learning (ML) tools are exploding and specializing, giving users the option to build and manage their ML models in different ways ranging from writing code, relying on frameworks to using automated solutions, each with their pros and cons. The good news is, PerceptiLabs has developed a next-generation ML tool with our visual modeler that makes model building easier, faster, and accessible to a wider spectrum of users, whether you are an expert or beginner.
To understand the significance of this tool, let’s take a step back and review how ML has evolved.
We’re excited to announce we’ll be presenting a session at the upcoming Red Hat webinar series AI/ML: Smart apps, easy delivery, fast platform on Wednesday, June 17. This webinar series will provide a variety of perspectives on driving innovation by simplifying the deployment and lifecycle management of Artificial Intelligence and Machine Learning applications.
Our session, Build a Machine Learning Model with Transparency, will be lead by Martin Isaksson, PerceptiLabs’ Co-founder + CEO, and In this session you will learn:
PerceptiLabs Enterprise is now available through Red Hat Marketplace as an OpenShift Operator. The recently introduced Red Hat Marketplace is a simpler way to buy and manage enterprise software, with automated deployment to any cloud. Built in partnership by Red Hat and IBM, Red Hat Marketplace is designed to meet the unique needs of developers, procurement teams, and IT leaders through simplified and streamlined access to popular enterprise software.
By running PerceptiLabs on Red Hat OpenShift, enterprise users gain three primary benefits:
Those who are developing machine learning (ML) models or just getting into ML for the first time have it good, because never before have so many open source datasets been freely-available to get you started.
Access to open source datasets brings a number of benefits. For starters, it allows you to focus on the development of your model, not data management in which you would first gather up large collections of data. Using existing datasets makes it easy to see what goes into a dataset, as these have already been wrangled and labeled for classification. …
Creating a machine learning (ML) model involves a lot of variables such as deciding what data to analyze, which approach to employ (e.g., a neural network), and what type of result to generate (e.g., probabilities, classifications, etc.). The key parts of an ML model that define its structure and behavior are its hyperparameters.
You can think of these hyperparameters as the model’s configuration settings, which specify how the model is laid out and how it will work. Hyperparameters are often referred to as the model’s external parameters, because they cannot be estimated from data while the training process is running…
PerceptiLabs is proud to announce our partnership with Red Hat, for our “Enterprise” version of our visual machine learning modeling tool.Through this partnership, our enterprise customers can install PerceptiLabs to run on either their on-premise or cloud-based deployments of the Red Hat OpenShift Container Platform, which hosts an ecosystem of container apps for building solutions.
Both PerceptiLabs and Red Hat share a philosophy of providing flexible solutions through interoperability with other ecosystem players, and the goal of compressing the time to value for machine learning (ML) projects…
During our initial development of PerceptiLabs Beta, we generated our visual modeling tool as a native, platform-specific executable for Windows, Mac, and Linux.
We recently switched to a new “browser-based” version that runs a local copy of our kernel on your machine. So, we thought we’d take a few minutes to discuss how this works and why we chose this architecture over traditional, platform-specific executables.
A Two-Part Architecture
Rather than provide developers with a monolithic executable, we have chosen to break our implementation into two components:
In our blog The Importance of Transparency in Machine Learning Models, we talked about how transparency in machine learning (ML) models helps us to build an understanding of the model, provide insight into why it’s generating certain results, and ultimately increase our trust that the model will perform as expected in the real world. However, achieving such transparency in a typical real-world ML workflow requires the right processes and tools to be in place.
At PerceptiLabs, we believe the best approach for achieving transparency is being able to “visualize” your model and how it performs, which is why our modeling…
Machine learning at Warp Speed with the PerceptiLabs Visual Modeling tool.