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Logging real remote IPs in Tomcat logs with Google Load Balancer
As part of our technical support operations, we have noticed that our customer’s Tomcat server on Google Cloud Platform is not using the correct remote IP address in the access log.
Predicting the Geospatial Availability of Mobility Services like Bird and Lime
Building and deploying production-grade machine learning models can be somewhat tricky. Even with technologies like Google Cloud AutoML, Cloud ML Engine and other out-of-the-box machine learning tools, training models and using them in production systems commonly requires a vast set of skills that can include some advanced Python programming, understanding complex models, SQL and DB technologies. This blog post demonstrates how to build a prediction system for shared cars/bikes/scooters using very simple tools!
Autoscaling K8s HPA with Google HTTP/S Load-Balancer RPS EXTERNAL Stackdriver Metrics
Most of the time, we scale our Kubernetes deployments based on metrics such as CPU or memory consumption, but sometimes we need to scale based on external metrics. In this post, I’ll guide you through the process of setting up Horizontal Pod Autoscaler (HPA) autoscaling using any Stackdriver metric; specifically we’ll use the Request Per Second from a Google Cloud HTTP/S Load Balancer.
XGBoost or TensorFlow?
Both XGBoost and TensorFlow are very capable machine learning frameworks but how do you know which one you need? Or perhaps you need both?
Ephi — The ephemeral bot for Slack built with Google App Engine Task Queues
Ephi is a very simple bot for Slack that allows users to send ephemeral messages that are automatically deleted for everyone (i.e. self-destruct messages) after a set period of time. Yes, it’s like Snapchat for Slack, but without the nudity, hopefully ;-)
KubeRBS for automatic Kubernetes rollbacks so you can sleep better at night!
With the massive adoption of Kubernetes and the fact the continuous delivery has become a standard practice, the rollout of new versions is now more automated than ever. But what happens if you deploy a faulty version? How much time and effort it will take to rollback to the previous, non-faulty version?
Get your kid ETA from school using Google Home, Cloud Functions, Datastore, Maps Directions API, and some Cloud KMS ;-)
Recently, I have completed two Google Cloud certifications, - Professional Cloud Architect and Professional Data Engineer. The team at Google Tel-Aviv were nice about it and gave me a Google Home device as a token of their appreciation (thank you Nir Atias & Ifat Yanai!)
SageMaker ML: 5 Easy Steps to Predict Taxi Ride Fare!
TL;DR: Amazon SageMaker offers an unprecedented easy way of implementing machine learning pipelines, significantly shortening the time to market for data scientists and engineers.
Don’t get the Google Cloud Bill Shock!
With Google BigQuery ML you can now predict your Google Cloud spend in just a few minutes and without leaving your BigQuery Console UI.
Breaking down Google Cloud costs by location
One of the popular question I am getting often is “how do I break down my Google Cloud costs by location?”. Today, I am going to show how to make Iris and DoiT Cloud Intelligence work together to collect and visualize this information for you.
Google Kubernetes Engine without going NAT with kubeIP!
Many applications need to be whitelisted by consumers based on source IP address, usually for security reasons. As of today, Google Kubernetes Engine doesn’t support assigning a static pool of addresses to GKE cluster and requires the deployment of a complex NAT based solution which is expensive, hard to maintain and requires a complex set of rules for load-balancing and redundancy.
Say goodbye to Mixpanel. Meet Banias!
Meet Banias — high performance analytics pipeline built on top of Kubernetes, Apache Beam and Google BigQuery