CodeJam on the topic “SAP Leonardo Machine Learning” at consenso in Bielefeld – a successful event in every way!”

22nd November 2018

On Friday, 09^th November 2018, consenso hosted the SAP CodeJam on the topic “SAP Leonardo Machine Learning with SAP HANA, express edition” in Bielefeld.

SAP Leonardo – what exactly is that? This is not really an easy question to answer. SAP Leonardo, described by SAP as “Digital Innovation System”, is actually not so much a system, but it is rather a pool of technologies, services and applications around digital innovations, such as the Internet of Things (IoT), machine learning, block chain, analytics, artificial intelligence (AI) and big data, as well as around innovative methods like design thinking.

The name giver: the universal talent Leonardo da Vinci, blessed with an innovative power across all sorts of disciplines. The objective: support of companies and organisations in their entire digital innovation strategy. With machine learning, the IoT, advanced analysis tools and other new technologies.

Taking the example “machine learning”, this so far rather seemingly theoretical approach should be put into praxis in the course of the SAP CodeJam.

Professionally directed by CodeJam leader Abdel Dadouche (SAP), the almost 40 participants started with the installation of an SAP HANA express edition.

SAP Tutorial Navigator provided suitable training examples. Every participant could choose between creating a weather forecast app or a recommender app for films. Depending on the choice, appropriate example data were loaded into the HANA database – the basis for machine learning. Preconfigured algorithms use the available data – historical or real-time data – draw conclusions, derive forecasts and actions. In this case for weather forecasts and films, but also in many other areas. For quality controls in production via machine vision and algorithms for the identification of deviations. Or in the medical field, where algorithms can swiftly process more data and recognise more patterns than medical and research teams can do over many years. Only some examples for the varied applications of machine learning.

Important for the – “non-machine-based” – learning of the CodeJam participants: Everybody could define his/her own working speed. Abdel directly dealt with upcoming questions and supported – if necessary – in the implementation.

Optimal provisions were made for the trappings as well: at lunchtime, a tasteful warm buffet ensured new strength for the continuation of the coding session. And of course, the focus was not only on learning, but there was also sufficient time for networking, so that everybody interested could collect some new contacts before heading off towards the weekend.

A successful event in every way!