Testing on the CI
It's your time to set up your testing on Bitrise! In the third article of our Android testing series, we will give you some hints on how to effectively run a virtual device on your CI.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
How to migrate your iOS app from Jenkins and fastlane to Bitrise
This is a step-by-step guide with hands-on tips for migrating your iOS apps from Jenkins and fastlane to Bitrise, quickly and easily.
Extending Connectivity to Cloud Native and VM-based Applications
We all know that what customers see in the market is, in fact, only a small percent of the shifts happening within our organizations. Recently, Time Magazine stated that “Every Company is a Tech Company… The Disruption is Just Beginning.” We’re seeing it in the way we wait in lines, find places to stay when traveling and work from anywhere. The disruption is distribution, and it impacts how we live and build applications.
Confluent IPO: An exciting time for the community
If you’ve been following Apache Kafka over the past few years, you’ll know a very important milestone has now been reached. The company that was spun out of LinkedIn to propel this open source technology to the future is now going Public. Well done Confluent, congratulations on your IPO!
Building a Single Pipeline for Data Integration and ML with Azure Synapse Analytics and Iguazio
Across organizations large and small, ML teams are still faced with data silos that slow down or halt innovation. Read on to learn about how enterprises are tackling these challenges, by integrating with any data types to create a single end-to-end pipeline and rapidly run AI/ML with Azure Synapse Analytics with Iguazio.
Bitrise Open Source & the API: Bitrise Wall mobile app
At its core, Bitrise has open-source components. In fact, many of the Steps that make up Bitrise Workflows are open-source developments. But did you know that Bitrise has an open API and users are actively creating extra apps and other components that make use of the API?
Community Creations: Bitrise Open Source & the API - Introducing the Bitrise Wall mobile app
Being able to extend the functionality that Bitrise provides, by writing your own Apps that exploit the openness of the API is an added value feature of Bitrise. And, good use of this possibility has already been made by existing Bitrise users. A case in point is Bitrise Wall, created by Jas Manigundan. This is an app for both iOS and Android that allows you to monitor the activity of your builds and even download the latest app version directly from your mobile phone.
[MLOps] The Clear SHOW - S02E10 - Everything You Wanted To Know About Model Stores*
Ariel (ft. G. Raffa) discusses the reasoning behind model stores, why you might want to build one, and reviews a model store library vs. ClearML to understand what needs to be built "on top" of our open-source MLOps Engine. + Operator AI ClearML is the only open-source tool to manage all your MLOps in a unified and robust platform providing collaborative experiment management, powerful orchestration, easy-to-build data stores, and one-click model deployment.
Comprehending ClearML and MLOps - Enabling the New A-Z (ODSC East '21)
ClearML is an industry leading MLOps suite, fully open source and free in the best sense. Designed to ease the start, running and management of experiments and orchestration for every day practitioners, we will also see how it provides a clear path to deployment. Starting with a high level overview of the parts built into ClearML, we will then journey into what is and also, importantly, what is not part of ClearML's mandate. Along the way we will demonstrate how-to integrate into your PyTorch code, as well as the capabilities of reporting and possible workflows that could be made easier by pipeline usage.
Deconstructing MLOps (ODSC East '21 Keynote)
Whether you are a veteran Data Science practitioner, a novice ML engineer, or a hard-working DevOps ninja, you probably heard about MLOps. But what are MLOPs? And do they only relate to applied machine learning in production?