2020 will be about simplifying the way from data science to production, with an emphasis on bringing real – and scalable – business value.
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
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.
A step by step tutorial on working with Spark in a Kubernetes environment to modernize your data science ecosystem
Modernize your IT Infrastructure Monitoring by Combining Time Series Databases with Machine Learning
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.
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.
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.
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?
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?
Data gravity and privacy concerns require federated solutions across public clouds and multiple edge locations. For example, retail stores embed cameras and sensors to track customer purchases, monitor inventory and provide real-time recommendations, but face challenges as forwarding massive volumes of video and sensor data to the cloud for processing is not practical and adds…