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Airflow and superQuery
Monitoring your BigQuery costs in real-time with the SuperQueryOperator Photo by Soheb Zaidi on Unsplash “What is the cost?” A question asked so frequently in the tech world that every person at a small start-up shudders slightly when it is asked. The answer of which invariably is: “We’re not sure”. One of the best tools for […]
New Generation of AI training — Part II
Training GANs using mixed precision VS single precision
New Generation of AI training — Part I
Tensor Cores boost your neural network training
DoiT International acqui-hires boutique consulting firm Mentory
DoiT International today announced the acqui-hiring of the co-founders behind boutique consulting company Mentory. The CTO Nimrod Ticozner, Moshe Ohayon, Tal Cohen and Evgeny Varela-Savela will help DoiT International meet increasing demand for talent by joining Customer Reliability Engineering teams to help our customers build, expand and migrate big data workloads to the cloud.
Multi-Cloud Engineering 2018 — A Year in Review
2018 is over, and since we are well into 2019 it’s a good time to summarize the state of the multi-cloud business as we have experienced it at DoiT International during 2018, and how we see the future of multi-cloud during 2019 and beyond.
Kaggle Days Paris — Winning Solution
Kaggle Days is (almost) officially the most interesting event to meet, learn and compete against the most talented data scientists worldwide. And this is how we won it.
Embracing our future: One Global DoiT International
From the very beginning and since the establishment of DoiT International in late 2010, our vision was to create a global cloud-solution-reseller business while providing our customers with outstanding support on core cloud infrastructure, machine learning, and data analytics.
Kubernetes external API calls — the right way
Sometimes you will need to call the Kubernetes API from outside the cluster whether it’s to run a job or to list deployments, or anything else the Kubernetes API provides.
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?