How can developers learn to utilize machine learning in their DevOps practice? In this article, Prasanthi Korada goes over some basic approaches that can help developers apply cutting edge tech like machine learning to their everyday work.
With artificial intelligence and machine learning becoming increasingly relevant for modern enterprises, many companies might be feeling the pressure to invest in an AI strategy, before fully understanding what they are aiming to achieve. In this session, Sumanas Sarma and Rob Hinds explain how you can go from theory to production in adopting machine learning solutions.
Machine learning inevitably adds black boxes to automated systems and there is clearly an ethical debate about the acceptability of appropriating ML for a number of uses. The risks can be mitigated with five straightforward principles.
The future of digital technology is here. 2017 saw incredible progression for things like data science, artificial intelligence, and machine learning. Where will they go in 2018? In this article, Maria Thomas explores the future of data science and how well it can be combined with predictive analysis.
Open source and machine learning go together like peanut butter and jelly. But why? In this article, Kayla Matthews explores why many of the best machine learning tools are open source.
Wherever you are, you can livestream the opening keynote for the Machine Learning Conference and join Xander Steenbrugge as he discusses “Cracking open the black box of neural networks”.
An in-memory computing platform with continuous learning capabilities enables a range of real-time decision making use cases. What might some of these cases be and how will they affect the future of machine learning and deep learning?
Looking to deploy and monitor large-scale deep learning applications for the enterprise? Polyaxon makes it easier to manage workloads for teams without losing control of your data.
Manifold may just have the solution for a problem that has been facing many ML teams. Let’s take a look at Torus: a new toolkit that promises to bring DevOps practices to machine learning. Open up the box and see what’s inside.
We spoke to data expert Canburak Tümer about how machine learning is being used to detect fraud in sales transactions. Find out how ML technology is helping to keep this tricky job under control and what it looks for when crunching the data.
With all the hype around machine learning, there’s plenty of people asking what it is exactly. If you want a quick primer on what’s important, read this.
Do you know where your data is moving? Dr. Alan Nichol and Ricardo Wölker will show you how to build and run your own GDPR-compliant Natural Language Understanding (NLU) service with the open-source Rasa NLU library. You can query it over HTTP without Python knowledge and it leaves you fully in charge of your data.
Deep down ML is a pure numbers game. With very few exceptions, the actual input to an ML Model is always a collection of float values. We talked with Christoph Henkelmann about the way ML algorithms work on words and letters, the difference between image and text and how to handle textual input properly.