Part 1: Know thy tools!

Image for post
Image for post
Photo by jesse ramirez on Unsplash

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 way that lets your end-users actually use them.

This is extremely easy to do as a mobile developer since tools like Android Studio and Xcode take this burden away from you as a developer and handle the packaging of the code itself.

But as someone who’s writing desktop apps with Python (using frameworks like PySide2 or PyQt5); this process isn’t very straightforward. There isn’t any IDE that does this packaging for you, nor is there any definitive guide that you can follow to package your apps without running into significant issues. …


Image for post
Image for post
Image Source

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 which I outline how to train a custom object detection model of your own in less than a few hours:

Series Pit Stops


Image for post
Image for post
Photo by Panos Sakalakis on Unsplash

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.

However, just training an ML model isn’t enough. You also need to know how to use them to make predictions. Maybe you need to build a cross-platform app using tools like QT, or maybe you want to host your model on a server to serve requests via an API. …


Custom error logs, de-obfuscated crash reports, and more

Image for post
Image for post
Photo by Ricky Kharawala on Unsplash

This is the follow-up blog post from my previous article on using Google Analytics for Firebase. While GA allows you to measure and understand user behavior, Crashlytics instead allows you to track and keep a log of all the crashes that have happened on the devices using your app—regardless of whether they choose to report the said crash or not.

If you haven’t read my earlier post on using GA on Android, you can read it here:

If you are working on Firebase as a mobile developer, you might also want to go through a series I wrote earlier on working with…


Out-of-the-box metrics, custom event tracking, and more

Image for post
Image for post
Photo by Edho Pratama on Unsplash

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. And this is true not just for improving app features, but also for ensuring that users who aren’t happy with the app don’t get rid of it for good!

Traditional ways of acquiring feedback relied on survey forms or in-person interviews. While still relevant, a lot of developers can’t afford the time or budget on such intense feedback sessions. Instead, many are focusing their attention on available alternatives. …

About

Harshit Dwivedi

Has an *approximate* knowledge of many things. https://aftershoot.co

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store