How to Run Spark Over Kubernetes to Power Your Data Science Lifecycle
October 28, 2019
A step by step tutorial on working with Spark in a Kubernetes environment to modernize your data science ecosystem
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Iguazio + NVIDIA EGX: Unleash Data Intensive Processing at the Intelligent Edge
October 22, 2019
Discover Iguazio’s “cloud-like” Intelligent Edge, powered by NVIDIA EGX, which enables data and compute intensive processing with seamless usability.
MLOps NYC Recorded Sessions
October 18, 2019
It’s a wrap! We had a full house at MLOps NYC, Iguazio’s annual conference about managing and automating machine learning pipelines in order to bring data science
Modernize your IT Infrastructure Monitoring by Combining Time Series Databases with Machine Learning
August 12, 2019
Let’s explore the complexity and vulnerability of IT infrastructure and how to build a modern IT infrastructure monitoring solution, using a combination of time series databases with machine learning.
Python Pandas at Extreme Performance
August 8, 2019
Today we all choose between the simplicity of Python tools (pandas, Scikit-learn), the scalability of Spark and Hadoop, and the operation readiness of Kubernetes. We end up using them all.
Why is it So Hard to Integrate Machine Learning into Real Business Applications?
July 20, 2019
You’ve played around with machine learning, learned about the mysteries of neural networks, almost won a Kaggle competition and now you feel ready to bring all this to real world impact. It’s time to build some real AI-based applications.
Automating Machine Learning Pipelines on Azure and Azure Stack
July 15, 2019
Ever wonder if it’s possible to train machine learning (ML) models with regulated data which can’t be sent to the cloud? Has your edge solution gathered so much data that it just doesn’t make sense to send it all to
the cloud?
Horovod for Deep Learning on a GPU Cluster
July 1, 2019
Here’s the problem: we are always under pressure to reduce the time it takes to develop a new model, while datasets only grow in size. Running a training job on a single node is pretty easy, but nobody wants to wait hours and then run it again, only to realize that it wasn’t right to begin with.
Data Science in the Post Hadoop Era
June 18, 2019
With all the turmoil and uncertainty surrounding large Hadoop distributors in the past few weeks, many wonder what’s happening to the data framework we’ve all been working on for years?
Paving the Data Science Dirt Road
June 3, 2019
We need to break free of the belief that the dirt road we are on is the only road possible and, instead, think about how to pave it.
Real-time Streaming for Data Science
May 27, 2019
We use a Nuclio serverless function to “listen” to a Kafka stream and then ingest its events into our time series table.
Kubernetes, The Open and Scalable Approach to ML Pipelines
May 15, 2019
Still waiting for ML training to be over? Tired of running experiments manually? Not sure how to reproduce results? Wasting too much of your time on devops and data wrangling?