Photo by Ivan Bandura on Unsplash


And why such excitement? Because this is the one. Or that is what some people say. Some say…

As promised — following the findings in my previous article — we’ll now focus on Custom Vision “compact” models. What are they? Use cases? What is their performance like compared with standard models?

Photo by Jamie Street on Unsplash

Compact world

When installed in a device, it would allow obtaining quick inferences (since it doesn’t need to hit an external API) without an internet connection. So an app could provide real-time features and/or have offline support.

How it works

In Summary

“Routing” — Photo by Javier Allegue Barros on Unsplash

Our example

The following diagram summarises it:

In Summary

Custom Vision has been on my radar for a while. The platform, created by Microsoft and part of the Azure ecosystem, allows users to easily upload and tag images to build and train custom Machine Learning (ML) models that can be used to perform classification and object detection. …

In summary


However, even when the information exists (it’s stored somewhere), we face several challenges…

  • Where is the information located? …

The progress of AI in the last decade…

What can make all this progress possible is the diversity of the technologies we have available. From now on, each innovation, from leading artificial intelligence techniques like deep learning, to mobile technologies (like Fetch, our very own augmented reality technology), is making it faster and more economical to pursue various forms of human-computer interaction.

We see…

In Summary

When working on a project that requires building and training a custom machine learning model, TensorFlow makes the work much easier. It provides you with most of the tools you’ll need to:

  • Handle your datasets.
  • Pre-process the data and post-process the model results.
  • Train your model.
  • Transform the information into useful visualizations (especially, when running on a Jupyter notebook).
  • Save your pre-trained models.
  • Test inferences.
  • Etc.

TensorFlow’s possibilities are really well documented, from complete beginner to an advanced point of…

In this occasion, I’d like to share a couple of ways to enhance the deployment of your Lambda functions, aiming for its complete automation:

  • Handling tasks not achievable with CloudFormation.
  • Use of CF resources in your code via environment variables.
Photo by Rodney Minter-Brown on Unsplash

Is Cloud Formation enough?

Through the Serverless configuration system, which in its most basic form consists of a single serverless.yml file, we are able to deploy our whole stack. …

In my last article I related how to request a GraphQL API provided by AppSync from AWS Lambda making use of Cognito User Pools for authentication. A quick summary of the steps followed would be as:

  • Creation of a new Client App for our Pool that would allow server-side authentication (ADMIN_NO_SRP_AUTH).
  • Obtaining a JW Token for our user in the pool.
  • Initialising the AppSync client with the token given and our API configuration.
  • Querying and/or mutating the data.

As it was already mentioned in the article, the main goal was achieved but there was significant room for enhancement. So, I’d…

I wasn’t always sure about AWS Lambda as the best option to provide the full back-end of a medium-large app. Nevertheless, my opinion has recently changed (probably thanks to the enthusiasm of my colleague Luke and the discovery of the framework Serverless) and while I won’t go through the advantages of using a serverless architecture in this article, I will admit I’ve become a true believer in its potential and I’m convinced it will be part of the chosen architecture solution for most of our apps in the future.

For our latest hackathon adventure, the team opted for a serverless…

Jesus Larrubia

Senior Full Stack Engineer at @gravitywelluk

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