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Not an easy skill

Applying machine learning to DevOps

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.

Watch Sumanas Sarma and Rob Hinds' JAX London 2017 session

Agile machine learning: From theory to production

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.

Minimizing machine learning risks

How to develop machine learning responsibly

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.

Looking to the future

The state of machine learning in 2018

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 makes everything better

Why are so many machine learning tools open source?

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.

Building your own in-house HTTP service for natural language processing

Machine learning and data sovereignty in the age of GDPR

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.

Interview with Christoph Henkelmann

Machine Learning algorithms: Working with text 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.