Every IoT project needs to provision devices that are going to be available in the field. Leveraging open standards, Golioth cuts down on the required time and hassle for IoT development teams.
Update your firmware over the network using the Golioth platform. Bundle multiple artifacts (binaries) together into a release and easily deploy firmware packages to specific devices in your fleet. In this video Alvaro shows how to update the firmware of an nRF52 over Ethernet using a Featherwing board using Golioth’s DFU capabilities.
Alvaro shows how to map LightDB Stream data, which allows for instant, continuous data output from your IoT devices on the Golioth network. Stream data back from a sensor, a device measurement, or anything else you can think of. Grafana allows for charting and slicing of data in a local instance, for fast diagnosis of problems, and longer term trend charting.
Device tags are a key part of Golioth. Managing device deployments—large or small—gets easier when you start grouping devices together. Adding “tags” on the Golioth platform allows you to notate an individual device belongs to a broader group. If you’re developing something like smart light bulb and you have devices tagged “kitchen”, you can send an update over LightDB to the “kitchen” tagged devices and turn all the bulbs on or off. You can also group devices by hardware type, by end customer, by firmware deployment test group anything you can think of!
Vamos ver o passo a passo de como configurar um ambiente de integração continua e deploy continuo usando ferramentas gerenciadas no Google Cloud, sem se queimar tendo que gerenciar servidores. Demos Demo em Flask https://github.com/alvarowolfx/flask-demo Demo de Múltiplos Ambientes https://github.com/alvarowolfx/gcloud-ci-cd-demo Use Case em IoT https://medium.com/google-cloud/serverless-continuous-integration-and-ota-update-flow-using-google-cloud-build-and-arduino-d5e1cda504bf https://github.com/alvarowolfx/gcloud-ota-arduino-update Referencias Deploy de aplicativos móveis Android APK https://cloud.google.com/community/tutorials/building-android-apk-with-cloud-build-gradle-docker-image Flutter e Cloud Build https://medium.com/@lidemin/flutter-ci-cd-with-cloud-build-android-9cd12ade8306 Outros ambientes de execução Google App Engine https://medium.com/google-cloud/continuous-delivery-in-google-cloud-platform-cloud-build-with-app-engine-8355d3a11ff5 Cloud Functions https://cloud.google.com/cloud-build/docs/deploying-builds/deploy-functions https://medium.com/swlh/how-to-ci-cd-on-google-cloud-platform-1e631cded335 https://cloud.google.com/devops https://github.com/GoogleCloudPlatform/github-actions/blob/master/get-secretmanager-secrets/README.md https://docs.github.com/en/actions/reference/context-and-expression-syntax-for-github-actions#github-context https://cloud.google.com/cloud-build/docs/configuring-builds/substitute-variable-values#yaml_2 https://cloud.google.com/cloud-build/docs/building/build-go#building_using_go_modules Cloud Run Quickstart - https://www.youtube.com/watch?v=3OP-q55hOUI https://fireship.io/lessons/ci-cd-with-google-cloud-build/
I noticed that has been a while since I wrote an actual article and I was kind of not satisfied with my current state of some of my content being spread between my Github, Medium and Youtube. I was always trying to get my website to be up to date, but was the process was too manual. So, as I good engineer, I decided to over-engineer my own personal website and try to put everything into a central place.
A junção das áreas de Machine Learning em ambiente embarcado/IoT tem crescido bastante, sendo atualmente chamada de TinyML. Já temos modelos robustos e pequenos o suficientes para rodar até mesmo em micro controladores com 16kb de memória. Nessa palestra vou mostrar as diferentes formas de se trazer modelos de Machine Learning para ambiente embarcado usando o ecossistema do Tensorflow.
Developing APIs over a RESTful interface with JSON payloads is kind of the de-facto standard nowadays, but it still lacks an easy way to build it with a well-defined interface and document it to be used by others. What if we can leverage gRPC’s fast, type-safe, and modern way of building APIs and still be able to provide an interface over REST/JSON ? Check this talk to find out how.
A junção das áreas de Machine Learning em ambiente embarcado/IoT tem crescido bastante, sendo atualmente chamada de TinyML. Já temos modelos robustos e pequenos o suficientes para rodar até mesmo em micro controladores com 16kb de memória. Nessa palestra vou mostrar as diferentes formas de se trazer modelos de Machine Learning para ambiente embarcado usando o ecossistema do Tensorflow.