NDC London

January 27-31 | Queen Elizabeth II Centre, Westminster | London, UK
About NDC London

Since its start-up in Oslo 2008, the Norwegian Developers Conference (NDC) quickly became one of Europe`s largest conferences for .NET & Agile development. Today NDC Conferences are 5-day events with 2 days of pre-conference workshops and 3 days of conference sessions.

NDC London

In December 2013 NDC did the first NDC London conference. The conference was a huge success and we are happy to announce that the 6th NDC London is set to happen the week of 28 Jan - 1 Feb 2019.

Conference Website
https://ndc-london.com
Conference Location

Broad Sanctuary, Westminster, London SW1P 3EE, UK

Register Now!
Here's what we are talking about!
Workshop: A Deep Dive in to Machine Learning in .NET. Part 1/2
By Alexander Slotte
Workshop: A Deep Dive in to Machine Learning in .NET. Part 2/2
By Alexander Slotte
Workshop: A Deep Dive in to Machine Learning in .NET. Part 1/2
By Alexander Slotte
Presentation time
January 30th
4:20p.m.
Register Now!

It’s imperative in today’s world to be able to make split-second decisions based on real-time data. Reports based on batch data are great for looking back at trends and potentially making long-term decision, but old data is in many cases already obsolete, and the opportunity to have an actionable impact on the success of a specific process may have been lost. Furthermore, incorporating machine learning algorithms in your real-time data pipeline enables you to derive great insight on the fly and truly set your organization up for your success.

The best part, it is not as difficult as you may think!

In this workshop, you will cut through all the foreign jargon and give participants a solid machine learning and stream processing foundation.

By the end of the workshop you will be able to:

  • Understand the basics of Machine Learning and Deep Learning
  • Train custom machine learning models using
  • ML.NET
  • AutoML
  • Azure Machine Learning Service
  • Jupyter Notebooks with ScikitLearn/Pandas and Numpy
  • Deploy your machine learning models to an Azure Function and/or Azure Container Instance
  • Setup a real-time data pipeline using Azure Stream Analytics
  • Understand the concept of temporal windows
  • Integrate your machine learning models into your data pipeline
Workshop: A Deep Dive in to Machine Learning in .NET. Part 2/2
By Alexander Slotte
Presentation time
January 30th
5:40p.m.
Register Now!

It’s imperative in today’s world to be able to make split-second decisions based on real-time data. Reports based on batch data are great for looking back at trends and potentially making long-term decision, but old data is in many cases already obsolete, and the opportunity to have an actionable impact on the success of a specific process may have been lost. Furthermore, incorporating machine learning algorithms in your real-time data pipeline enables you to derive great insight on the fly and truly set your organization up for your success.

The best part, it is not as difficult as you may think!

In this workshop, you will cut through all the foreign jargon and give participants a solid machine learning and stream processing foundation.

By the end of the workshop you will be able to:

  • Understand the basics of Machine Learning and Deep Learning
  • Train custom machine learning models using
  • ML.NET
  • AutoML
  • Azure Machine Learning Service
  • Jupyter Notebooks with ScikitLearn/Pandas and Numpy
  • Deploy your machine learning models to an Azure Function and/or Azure Container Instance
  • Setup a real-time data pipeline using Azure Stream Analytics
  • Understand the concept of temporal windows
  • Integrate your machine learning models into your data pipeline
Workshop: A Deep Dive in to Machine Learning in .NET. Part 1/2
By Alexander Slotte

It’s imperative in today’s world to be able to make split-second decisions based on real-time data. Reports based on batch data are great for looking back at trends and potentially making long-term decision, but old data is in many cases already obsolete, and the opportunity to have an actionable impact on the success of a specific process may have been lost. Furthermore, incorporating machine learning algorithms in your real-time data pipeline enables you to derive great insight on the fly and truly set your organization up for your success.

The best part, it is not as difficult as you may think!

In this workshop, you will cut through all the foreign jargon and give participants a solid machine learning and stream processing foundation.

By the end of the workshop you will be able to:

  • Understand the basics of Machine Learning and Deep Learning
  • Train custom machine learning models using
  • ML.NET
  • AutoML
  • Azure Machine Learning Service
  • Jupyter Notebooks with ScikitLearn/Pandas and Numpy
  • Deploy your machine learning models to an Azure Function and/or Azure Container Instance
  • Setup a real-time data pipeline using Azure Stream Analytics
  • Understand the concept of temporal windows
  • Integrate your machine learning models into your data pipeline
Workshop: A Deep Dive in to Machine Learning in .NET. Part 2/2
By Alexander Slotte

It’s imperative in today’s world to be able to make split-second decisions based on real-time data. Reports based on batch data are great for looking back at trends and potentially making long-term decision, but old data is in many cases already obsolete, and the opportunity to have an actionable impact on the success of a specific process may have been lost. Furthermore, incorporating machine learning algorithms in your real-time data pipeline enables you to derive great insight on the fly and truly set your organization up for your success.

The best part, it is not as difficult as you may think!

In this workshop, you will cut through all the foreign jargon and give participants a solid machine learning and stream processing foundation.

By the end of the workshop you will be able to:

  • Understand the basics of Machine Learning and Deep Learning
  • Train custom machine learning models using
  • ML.NET
  • AutoML
  • Azure Machine Learning Service
  • Jupyter Notebooks with ScikitLearn/Pandas and Numpy
  • Deploy your machine learning models to an Azure Function and/or Azure Container Instance
  • Setup a real-time data pipeline using Azure Stream Analytics
  • Understand the concept of temporal windows
  • Integrate your machine learning models into your data pipeline