Engineering notes and architectural breakdown of Uber’s autonomous AI agent platform for developer workflows, code generation, and test execution.
Running VSCode DevContainers with plain docker-cli on Windows (no Docker Desktop): a practical setup guide for reproducible dev environments.
Continuous Delivery pipelines for Kubernetes with Spinnaker: release automation, fundamentals of ops-infra, and rollout strategy.
Step-by-step guide to building a federation of multiple Kubernetes clusters with KubeFed V2 for multi-region management.
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
ML model serving with Apache Kafka: stream processing vs RPC for real-time analytics, with TensorFlow examples from Kafka Summit.
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
