Top Trends for Data Science in 2020
December 18, 2019
2020 will be about simplifying the way from data science to production, with an emphasis on bringing real – and scalable – business value.
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The Easiest Way to Track Data Science Experiments with MLRun
December 2, 2019
A step by step tutorial covering experiment tracking complexity concerns and how to solve them with MLRun, a new open source framework which optimizes the management of machine learning operations.
SUSE and Iguazio Break the Mold by Providing an Open Source Solution for Enterprise Data Science Teams
November 19, 2019
The notions of collaborative innovation, openness and portability are driving enterprises to embrace open source technologies. Anyone can download and install Kubernetes, Jupyter, Spark, TensorFlow and Pytorch to run machine learning applications, but making these applications enterprise grade is a whole different story.
How to Run Spark Over Kubernetes to Power Your Data Science Lifecycle
November 10, 2019
A step by step tutorial on working with Spark in a Kubernetes environment to modernize your data science ecosystem
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?