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SkillsCast

Predicting the future as a service with Azure ML

22nd June 2016 in London at CodeNode

There are 24 other SkillsCasts available from Progressive .NET Tutorials 2016

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Everyday we are noticing that applications are becoming more intelligent. They can predict your online shopping preferences, movies you want to watch or interesting articles. Actually nowadays it’s hard to imagine a successful business that is not making profit from some data forecasts.

Unfortunately, usually any kind of predictive analytics requires deep knowledge on machine learning. Companies need to think of hiring skilled staff able to build and manage complex models. This is the place when Azure Machine Learning Studio comes in. It offers low cost, easy to use and managed environment for developers of all skills levels. During this talk, Barbara will provide information about the following topics:

  • Creating an Azure ML experiment

  • Using variety of data manipulation techniques

  • Customizing ML process by using R modules

  • Publishing and consuming an endpoint for predictive analysis

  • Retraining the ML model

After this talk, you will get to know basics of Azure ML Studio. You will be able to use variety of data sources, create experiments and use the predictions in your own systems. The talk will provide you with the information on how to enrich your system by reasoning from data using proven and highly scalable ML technologies in an easy and low cost way.

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Thanks to our sponsors

Predicting the future as a service with Azure ML

Barbara Fusinska

Barbara is a Data Solution Architect with strong software development background. While working with a variety of different companies, she gained experience in building diverse software systems. This experience brought her focus to the Data Science and Big Data field. She believes in the importance of the data and metrics when growing a successful business. Alongside collaborating around data architectures, Barbara still enjoys programming activities. Currently speaking at conferences in-between working in London.

SkillsCast

Please log in to watch this conference skillscast.

578171015 640

Everyday we are noticing that applications are becoming more intelligent. They can predict your online shopping preferences, movies you want to watch or interesting articles. Actually nowadays it’s hard to imagine a successful business that is not making profit from some data forecasts.

Unfortunately, usually any kind of predictive analytics requires deep knowledge on machine learning. Companies need to think of hiring skilled staff able to build and manage complex models. This is the place when Azure Machine Learning Studio comes in. It offers low cost, easy to use and managed environment for developers of all skills levels. During this talk, Barbara will provide information about the following topics:

  • Creating an Azure ML experiment

  • Using variety of data manipulation techniques

  • Customizing ML process by using R modules

  • Publishing and consuming an endpoint for predictive analysis

  • Retraining the ML model

After this talk, you will get to know basics of Azure ML Studio. You will be able to use variety of data sources, create experiments and use the predictions in your own systems. The talk will provide you with the information on how to enrich your system by reasoning from data using proven and highly scalable ML technologies in an easy and low cost way.

YOU MAY ALSO LIKE:

Thanks to our sponsors

About the Speaker

Predicting the future as a service with Azure ML

Barbara Fusinska

Barbara is a Data Solution Architect with strong software development background. While working with a variety of different companies, she gained experience in building diverse software systems. This experience brought her focus to the Data Science and Big Data field. She believes in the importance of the data and metrics when growing a successful business. Alongside collaborating around data architectures, Barbara still enjoys programming activities. Currently speaking at conferences in-between working in London.

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