You can easily try out different backbones by selecting them from the Component's Backbone setting. And other settings like Activation, Output Activation, Pooling, and Unpooling methods, can just as easily be experimented with in a similar manner. From there, it's just a matter of viewing the training and validation results in Perceptilabs' Statistics View as you experiment with different values as shown in Figure 8: The Statistics View shows real-time metrics including the predicted segmentation overlayed on ground truth (upper left) and the Intersection Over Union (IoU) (middle right) for validation and training across epochs. IoU is a great method to assess the model's accuracy. It goes beyond pixel accuracy (which can be unbalanced due to having more background than object-level pixels) by comparing how much the objects in the output overlap those in ground truth. You can also view this for the model's test data in PerceptiLabs' Test View as shown in Figure 9: Alternatively, you can build U-Nets from scratch in PerceptiLabs.

The manufacturing of products often requires careful quality control, especially when health and safety depend on the quality of the product. For example, in the case of medical pills, small physical defects may not only impact the appearance of the product, but can result in incorrect dosages. To help automate the detection of such defects, we set out to build an image recognition model in PerceptiLabs that could identify defective pills by analyzing images. A model like this could potentially help pharmaceutical companies, pharmacists, or medical practitioners identify physically defective pill products. To train our model, we used the images from the Pill defect dataset.

Industrial IoT is playing a significant role in Industry 4.0, especially for automating manufacturing processes. With the integration of sensors, cameras, and AI at the edge, organizations can now automate numerous processes such as visual quality control inspections. Take, for example, the manufacturing of wood veneers. An important part of the manufacturing process involves drying the wood sheets at temperatures of up to 320 F to obtain a moisture content level of around 8% to 12%¹. Following this drying process, the sheets must then be subjected to several verifications to ensure they have been dried correctly and meet quality control standards.

Ocular diseases are extensively-studied in the healthcare world as they affect millions of people. With this in mind, we decided to build an ML model in PerceptiLabs that applies image recognition techniques on fundus images to detect possible cataracts in patients. Using a model like this could help doctors, optometrists, and researchers to more easily classify and detect such conditions. To train our model, we grabbed the Ocular Disease Recognition dataset on Kaggle that comprises fundus images representing seven ocular-related conditions and well as normal images (i.e., those depicting no-ocular-related conditions). For our use case, we narrowed down the dataset to 293 images representing normal images and 293 representing cataracts.

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. Both types of users benefit from PerceptiLabs' rich set of visualizations that include the ability to see a model's architecture, experiment and see how parameter and code changes affect models in real time, and view a rich set of training and validation stats. Given that PerceptiLabs runs TensorFlow behind the scenes, we thought we'd walk through the framework so you can understand its basics, and how it is utilized by PerceptiLabs.

Over the last several years, machine learning (ML) has transitioned from a discipline once reserved for researchers and PhDs into a lucrative field comprised of a growing and diversifying set of users and ML practitioners. This is due in part to the increased processing power found in today's hardware, the discovery of new ML algorithms, and the growing number of open source ML tools, frameworks, and datasets. Collectively, these factors are democratizing ML by putting new and more powerful ML capabilities into the hands of more users and ML practitioners than ever before. However, despite all of these advances, many ML tools and frameworks fail to address the overall workflow of designing, training, and tuning ML models. Many frameworks such as TensorFlow and PyTorch, while incredibly powerful, are still fundamentally programmatic frameworks aimed at coders.

Today I would like to introduce the new #PerceptLabs Framework to you. But that is not the point. Each framework has its pro and contras. You can play / build a model from scratch and see how a model works. This is very important for the beginning (#AI / #ML).

Data governance in data-driven organizations is a set of practices and guidelines that define where responsibility for data quality lives. The guidelines support the operation's business model, especially if AI and machine learning applications are at work. Data governance is an operations issue, existing between strategy and the daily management of operations, suggests a recent account in the MIT Sloan Management Review. "Data governance should be a bridge that translates a strategic vision acknowledging the importance of data for the organization and codifying it into practices and guidelines that support operations, ensuring that products and services are delivered to customers," stated author Gregory Vial is an assistant professor of IT at HEC Montréal. To prevent data governance from being limited to a plan that nobody reads, "governing" data needs to be a verb and not a noun phrase as in "data governance."

TensorFlow is arguably the most popular machine learning (ML) framework today because of its rich multi-layer API. However, as a framework for ML modeling via code, TensorFlow can be a handful for beginners. Even experienced data scientists and developers can find it difficult when working with large sets of code to visualize the model, to see how changes to logic and hyperparameters affect the model, and to track down bugs. Just released PerceptiLabs 0.11, is quickly becoming the GUI and visual API for TensorFlow that aims to solve these challenges. It's built around a sophisticated visual ML modeling editor in which you drag and drop components and connect them together to form your model.

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.