Systems | Development | Analytics | API | Testing

Community Creations: Bitrise Reports by Ubiratan Soares

Developer Advocate Kevin Toms talked with Ubiratan Soares, creator of Bitrise Reports about this open source project and Bitrise's open source API. 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.

Transforming supply chain and logistics analytics at Avnet with ThoughtSpot and Azure Synapse

Supply chain and logistics operations can be a company's biggest source of financial risk or competitive advantage. The key is reconciling external supplier data like tariff and shipping information with internal data to deliver insights across teams and geographies.

Future of Data Meetup: Hello, Kafka! (An Introduction to Apache Kafka)

Our “Hello, “ series of introductory “Big Data” topic-focused meetups returns to Boston in July as we deliver our fifth event. This meetup will introduce you to Apache Kafka without assuming you’ve heard anything about the Apache development project, the problems that Kafka was designed to solve or the role it currently plays in modern enterprise data architectures.

Hands-on tips for decreasing the duration of iOS UI tests via fastlane and Bitrise

Mobile test automation is a vital part of the Mobile DevOps cycle. Without it, we can’t deliver mobile apps to our customers. This article will explain how a fastlane plugin for parallelizing iOS UI tests can make your life easier.

[MLOps] The Clear SHOW - S02E11 - DIY Strikes Back! Building the Model Store!

Ariel extends ClearML's "experiment first" approach towards a "model first" approach - by building a model store. See how easy it is to add metadata to the model artifacts. + Colab notebook (uses the demo server, just run it and see what happens) 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.

Top 7 IoT Tools & Platforms for App Development

The connected reality of gadgets presented by the Internet of Things (IoT) can no longer be considered as just a buzz as it has already transformed our living to a great extent. The connected things made our homes, workplaces, and transportation smarter. No wonder IoT apps are now one of the most popular app categories. As the latest statistics reveal, the global market share of IoT apps is expected to touch a whopping 520 billion USD by the end of 2021.

[MLOPS] From #GTC21: Workshop - Demonstrating an End-to-End Pipeline for ML/DL Leveraging GPUs

Learn how to take models from research into deployment in an efficient and scalable manner. We'll demonstrate workflows and methodologies so that your data science team can make the most of their NVIDIA hardware systems and software tools (including TRITON!).

[MLOPS] From #GTC21: How to Supercharge Your Team's Productivity with MLOps

Learn how to structure a data scientist-first orchestration setup that allows your DS team to self-manage their allocated NVIDIA GPU clusters, without needing continuous hand-holding from DevOps/IT. We'll demonstrate this setup while using NVIDIA Clara Train SDK to walk through best practices in orchestration, experiment management, and data operations and pipelining. While examples will be health-care-focused, the concepts demonstrated are agnostic to any ML/DL use case in any industry.

[MLOPS] From #GTC21: Best Practices in Handling Machine Learning Pipelines on DGX Clusters

Learn how to set up and orchestrate end-to-end ML pipelines, leveraging large DGX clusters. We'll demonstrate how to orchestrate your training and inference workloads on DGX clusters, with optional setup of remote development environments leveraging the multi-instance GPUs on the NVIDIA A100. We'll also show how pipelines can be built to serve both research and deployment workloads, all while leveraging the compute inherent in the DGX cluster.