Modeling Tool - PerceptiLabs

Integrations: FastAPI for Fast SaaS Application Deployments

This blog shows why PerceptiLabs added the FastAPI deployment target, and to inspire how you might build on that output.

Machine Learning

Apply Datasets and Pre-Trained Models Faster and Easier with PerceptiLabs

Our mission is evolving to help you apply your DL models to real-world problems, easier, and quicker. How could we possibly do that? Read on to find out!

Machine Learning

Tips to Steer Your Developer Career Towards Machine Learning

Excited by the hype around machine learning (ML)? Perhaps you’d like to transition into the field but you’re not sure where to start. Learn more here.

Machine Learning

Inference for the Real World: What to do with your ML Model Once You've Trained it

The end result of the ML modeling process is a file containing the model's trained weights ready for inference. But once you have this file, how do you actually use it for inference?

Machine Learning

The Power of Abstraction: How ML Frameworks and PerceptiLabs Provide a more Human-Readable Language for Describing ML Models

Everyone wants to enhance their understanding of machine learning or ML these days. But what does it really mean to do ML?

Machine Learning

Don’t Start your Model from Scratch - Use Transfer Learning

Did you ever wish you could use one of your existing models as a starting point to solve a similar problem? Then you might want to try Transfer Learning. Transfer learning means to use or adapt an existing machine learning (ML) model for a new purpose and helps get you

Transparency

An Overview of TensorFlow and how PerceptiLabs Makes it Easier

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

Machine Learning

Today's Top Four Machine Learning Modeling Challenges and how PerceptiLabs Solves Them

Machine learning (ML) modeling is challenging – we know from experience! From wrangling data to choosing an appropriate ML algorithm, and then debugging and iterating on it, it can be a daunting task to create or update your model. Solving these types of issues is what fueled us to create PerceptiLabs,

Transparency

A New Visual Approach to Machine Learning Modeling

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

Machine Learning

Venture Beat: Getting inside the head of a machine learning scientist

We were excited to be featured in Venture Beat, in their Article titled: Getting inside the head of a machine learning scientist. Check it out to see how PerceptiLabs allows you to visualize what data scientists see when they are building a machine learning model. https://venturebeat.com/2020/10/

Transparency

Join us at Red Hat's AI/ML: Smart apps, easy delivery, fast platform webinar series

https://www.brighttalk.com/webinar/build-a-machine-learning-model-with-transparencyWe’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

Machine Learning

PerceptiLabs Releases Free Source Code for Our Machine Learning Handbook

Here at PerceptiLabs we love exploring all sorts of machine learning (ML) approaches. And if you've poked around our site in the last little while, you may have come across our Machine Learning Handbook. It's a free resource that you can download and use to become more familiar with approaches

Machine Learning

Four Common Types of Neural Network Layers (and When to use Them)

Neural networks (NN) are the backbone of many of today's machine learning (ML) models, loosely mimicking the neurons of the human brain to recognize patterns from input data. As a result, numerous types of neural network topologies have been designed over the years, built using different types of neural network

News

PerceptiLabs Enterprise now available through Red Hat Marketplace

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

Machine Learning

PerceptiLabs' Top 5 Open Source Datasets for Machine Learning

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

Machine Learning

Five Common Hyperparameters (and how to set them in PerceptiLabs)

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

Machine Learning

A Look at PerceptiLabs’ Browser-Based Architecture

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