Visual Studio Code with the DevContaier feature has always been one of the things I have most appreciated. With DockerDesktop a lot of automation is now hidden, but if you want just docker-cli and the same feature of VSCode? On windows can be a nightmare! This beautiful guide solves all the problems, for me it was very helpful I hope it will be for you too!
Kubernetes is now the de-facto standard for container orchestration. With more and more organizations adopting Kubernetes, it is essential that we get our fundamental ops-infra in place before any migration. This post will focus on pushing out new releases of the application to our Kubernetes cluster i.e. Continuous Delivery Source: Continuous Delivery Pipeline for Kubernetes using Spinnaker Always interesting to read this kind of CD proposals, an article well done, essential and a good starting point to deepen
I have found it really very interesting and I believe that it will be extremely useful. A step-by-step guide to building a Kubernetes federation for managing multiple regions’ clusters with KubeFed Source: Build a Federation of Multiple Kubernetes Clusters With Kubefed V2
A different point of view on how the idea of Agile is applied also in the process of understanding and evolution of AI models Agile data science: Evaluation and baseline model Infrastructure that enables rapid prototyping for model development Source: Agile data science: Evaluation and baseline model
This article is very useful because it discuss and compare two different options for AI model deployment: model servers with remote procedure calls (RPCs), and natively embedding models into Kafka client applications. This example uses TensorFlow, but the underlying principles are also valid for other machine learning/deep learning frameworks or products, such as H2O.ai, Deeplearning4j, Google’s Cloud Machine Learning Engine, and SAS. Is very useful also the other details on this topic, present on the video recording and slides from my Kafka Summit San Francisco 2019 presentation: Event-Driven Model Serving: Stream Processing vs. RPC with Kafka and TensorFlow. Learn how...
A very interesting article that I find very useful! Here are shared some of the common producer models, which show how to transform a classic architecture to an Event Sourcing model. Below is the link to the complete article! In part one, we learned about how at Nordstrom we’ve been exploring and implementing event-sourcing as an architectural pattern. In part… Source: Event-sourcing at Nordstrom: Part 2 Here a scort preview of patter Produce directly to the ledger at the moment of the event Transform an existing stream Write-through a database and use change data capture Poll an existing request/response service...
The linked post covers how Apache Kafka and its Streams API are used for storing and processing all the articles ever published by The New York Times Source: Publishing with Apache Kafka at The New York Times | Confluent
The ecosystem of Big Data analysis has evolved in recent years with new databases, streaming data and machine learning solutions which require more than the classic deployment model. The revolution of Container technologies try to cover these new objectives and there are possible to accomplish in the organizations. Below 2 articles where you can start thinking about what is most suitable for your games: Building GPU Accelerated Workflows with TensorFlow and Kubernetes Daniel Whitenack spoke at the recent KubeCon + CloudNativeCon North America 2017 Conference about GPU based deep learning workflows using TensorFlow and Kubernetes technologies. He discussed the open...
Recommended reading: Sahil Dua explained how Booking.com has been able to scale machine learning (ML) models for recommending destinations and accommodation to their customers using Kubernetes, at QCon London conference. In particular, he stressed how Kubernetes elasticity and resource starvation avoidance on containers helps them run computationally (and data) intensive, hard to parallelize, machine learning models. Source: How Booking.com Uses Kubernetes for Machine Learning
Learn about the ten machine learning algorithms that you should know in order to become a data scientist. Source: 10 Machine Learning Algorithms You Should Know to Become a Data Scientist – DZone AI
