Training an Image Classification Model for Mobile using TensorFlow Lite

This article will explain how to reduce the size of an image classification machine learning model for mobile using TensorFlow Lite, in order to make it fit and work on mobile devices. Predicting whether a person in an image is wearing a mask or not. Training an Image Classification Model for Mobile using TensorFlow Lite

Implementing a Fritz AI Machine Learning Model in an iOS app

In this article, we’ll walk you through the process of creating an image labeling ML model that can identify different car logos, and then integrating the model into an iOS app.

Modeling a Language Translation System using LSTM for Mobile Devices or Web

Now, to convert our TensorFlow model to the TensorFlow Lite model, we first need to build and train a TensorFlow model. Here, we will train our language translation model then finally we will convert our model to TensorFlow Lite so that we can utilize it for our mobile devices.

Improvements to Fritz AI Studio’s UX

Improvements to Fritz AI Studio’s UX. Easier project creation, new project dashboards, and a redesigned annotation workflow

Creating a Style Transfer Snapchat Lens with Fritz AI and SnapML in Lens Studio

Creating a Style Transfer Snapchat Lens with Fritz AI and SnapML in Lens Studio. Leveraging Fritz AI’s no-code model building Studio to quickly prototype a style transfer Snapchat Lens

Isolation Forest Algorithm for Anomaly Detection

Anomaly detection is one of the most popular machine learning techniques. In this article, we will learn concepts related to anomaly detection and how to implement it as a machine learning model.

Snapchat Lens Creator Spotlight: JP Pirie

Exploring JP’s AR work as well his perceptions on SnapML in Lens Studio 3.x. Our interview with Lens Creator JP Pirie -- and a closer look at some of his "horror" Lenses, perfect for the Halloween season.

Using Google Cloud AutoML Multi-Label Image Classification Models in Python

This post follows up from the post earlier on training a multi-label image classification model and covers how to run the trained model in a python environment.

Enterprise Scale ML Jumpstart Kit — FastAI + RabbitMQ + Docker

ML Environment jumpstart kit — robust, scalable, functional, load balanced, asynchronous, containerized, ready to deploy — for enterprise.Enterprise Scale ML Jumpstart Kit — FastAI + RabbitMQ + Docker. ML Environment jumpstart kit — robust, scalable, functional, load balanced, ...

Building Production Machine Learning Systems on Google Cloud Platform

Scaling out to a cloud platform for fast model training, evaluation, inferencing, logging, and monitoring. In this article, we will continue building production machine learning systems on GCP with a special focus on the design of hybrid ML systems which covers the following.

Deploying your Language Model with Google Cloud

This article will be answering the above questions—except for the last. I will leave the scaling of the app for another day, as that topic warrants its own investigation.

Semantic Image Inpainting with Context Encoders

Understanding Image Inpainting and its Techniques with a Focus on the State-of-the-Art Context-Encoders and a Generative Model-Based Approach. In this post, we are going to understand the underlying concepts of semantic image inpainting and various techniques developed for the same.

Mobile Machine Learning: By the Data

Mobile Machine Learning: By the Data. Part 1: Exploring access to machine learning expertise for organizations with mobile projects

Implementing Mobile BERT for Next Sentence Prediction

In this article, we are going to discuss this type of prediction, especially if the prediction has to happen on a mobile device. In this article, we’re going to discuss one of the MobileBERT implementations, called MobileBertForNextSentencePrediction.

Building Production Machine Learning Systems on Google Cloud Platform

In this article, we will continue the exploration of production machine learning systems on GCP with a special focus on the design of high-performance ML systems.

New Industry Report: State of Mobile Machine Learning in 2020

In this report, experts from Fritz AI and Spell dive headfirst into a burgeoning sector of the larger AI and machine learning (ML) industry — mobile machine learning. This report will delve into these opportunities, barriers to entry, and the perceptions of industry leaders when it comes to this transformational technology.

Monitoring Social Distancing Using People Detection

Here I will explain how to actually implement our social distance monitoring tool. For implementing the people detection, we will use Facebook’s Detectron library which has all the trained weights for RetinaNet for people detection.

Image Augmentations with Albumentations

Learn how to Implement Image Augmentation for Your Deep Learning Models. In this article, we’ll see how this can be done using the open-source Albumentation package.

The key components of a successful MLOps strategy

The key components of a successful MLOps strategy. Unified experiment management, automated training and comparison, automated deployment, and automated monitoring

Q Learning With The Frozen Lake Environment In Android

Create a Python-like Environment and Agent with Kotlin. This article assumes that the readers are familiar with terms like state, action, episode, and reward in the context of reinforcement learning. Basic knowledge of Q-learning would be helpful.