On 5 December 2015, the European Parliament, the Council and the Commission reached agreement on the new data protection rules, establishing a modern and harmonised data protection framework across the EU. Then on 14th April 2016 the Regulations and Directives were adopted by the European Parliament.
The EU GDPR comes into effect on the 25th May, 2018.
Are you ready ?
The EU GDPR will affect every country around the World. As long as you capture and use/analyse data captured with the EU or by citizens in the EU then you have to comply with the EU GDPR.
Over the past few months we have seen a increase in the amount of blog posts, articles, presentations, conferences, seminars, etc being produced on how the EU GDPR will affect you. Basically if your company has not been working on implementing processes, procedures and ensuring they comply with the regulations then you a bit behind and a lot of work is ahead of you.
Like I said there was been a lot published and being talked about regarding the EU GDPR. Most of this is about the core aspects of the regulations on protecting and securing your data. But very little if anything is being discussed regarding the use of machine learning and customer profiling.
Do you use machine learning to profile, analyse and predict customers? Then the EU GDPRs affect you.
Article 22 of the EU GDPRs outlines some basic capabilities regarding machine learning, and in additionally Articles 13, 14, 19 and 21.
Over the coming weeks I will have the following blog posts. Each of these address a separate issue, within the EU GDPR, relating to the use of machine learning.
- Part 2 – Do I have permissions to use the data for data profiling?
- Part 3 – Ensuring there is no Discrimination in the Data and machine learning models.
- Part 4 – (Article 22: Profiling) Why me? and how Oracle 12c saves the day
Over the past 12 months there has been an increase in the number of Machine Learning notebooks becoming available.
What is a Machine Learning notebook?
As the name implies it can be used to perform machine learning using one or more languages and allows you to organise your code, scripts and other details in one application.
The ML notebooks provide an interactive environment (sometimes browser based) that allows you to write, run, view results, share/collaborate code and results, visualise data, etc.
Some of these ML notebooks come with one language and others come with two or more languages, and have the ability to add other ML related languages. The most common languages are Spark, Phython and R.
Based on these languages ML notebooks are typically used in the big data world and on Hadoop.
Examples of Machine Learning notebooks include: (Starting with the more common ones)
- Apache Zeppelin
- Jupyter Notebook (formally known as IPython Notebook)
- Azure ML R Notebook
- Beaker Notebook
At Oracle Open World (2016), Oracle announced that they are currently working creating their own ML notebook and it is based on Apache Zeppelin. They seemed to indicate that a beta version might be available in 2017. Here are some photos from that presentation, but with all things that Oracle talk about you have to remember and take into account their Safe Habor.
I’m looking forward to getting my hands on this new product when it is available.