Building robust machine learning models with no prerequisite knowledge — Following up from my earlier blogs on training and using TensorFlow models on the edge in Python, in this eighth blog post in this series, I’ll be talking about how to train a multi-label image classification model that can be used with TensorFlow. Series Pit Stops Training a TensorFlow Lite Image Classification model…

Part 1: Know thy tools! — If you’ve been following my blogs lately, you might have noticed that I’ve been writing a lot on edge machine learning, for both mobile and desktop. While building models and writing code that runs inference on them is one thing, it’s equally important to also package your solution in a…

Following up from my earlier blogs on training and using TensorFlow models on the edge in Python, in this seventh blog in the series; I wanted to cover a topic that’s generally not talked about enough—optimizing the performance and latency of your TensorFlow models. Series Pit Stops Training a TensorFlow Lite Image Classification…

Good things come in (TF)lite packages! — This blog is the sixth blog in the series and a follow-up to my previous blog post on running TensorFlow Lite image classification models in Python. If you haven’t read that post, you can read it here: Running Tensorflow Lite Image Classification Models in Python Good things come in (TF)lite packages!heartbeat.comet.ml Series Pit Stops Training a TensorFlow Lite Image Classification model using AutoML Vision Edge

Good things come in (TF)lite packages! — Following up on my earlier blogs on running edge models in Python, this fifth blog in the series of Training and running Tensorflow models will explore how to run a TensorFlow Lite image classification model in Python. If you haven’t read my earlier post on model training for this task…

This blog is the fourth one in my series on training and running Tensorflow models in a Python environment. If you haven’t read my earlier blogs centered on AutoML and machine learning on edge devices, I’d suggest that you do so before continuing with this post. Here’s the post in…

If you’ve read my earlier blogs centered on AutoML and machine learning on edge devices, you know how easy it is to train and test a custom ML model with little to no prerequisite knowledge. Training a TensorFlow Lite Image Classification model using AutoML Vision Edge Using Google’s AutoML solution for mobile-ready model training.heartbeat.comet.ml Creating a TensorFlow Lite Object Detection Model using Google Cloud AutoML Leveraging Google’s AutoML to train a mobile-ready object detection model without codeheartbeat.comet.ml

Custom error logs, de-obfuscated crash reports, and more — This is the follow-up blog post from my previous article on using Google Analytics for Firebase. …

Out-of-the-box metrics, custom event tracking, and more — As a mobile developer, getting user feedback on what features to implement next and what areas to improve upon in your app is an essential part of the development process. …