2020 will be about simplifying the way from data science to production, with an emphasis on bringing real – and scalable - business value.
2020 will be about simplifying the way from data science to production, with an emphasis on bringing real – and scalable - business value.
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.
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?