Data is everywhere. Open source world gives us an opportunity to use advanced technical solutions to handle not uncommon problems of huge amounts of data that require low latency access. Can we use our favorite language such as F# for that? The answer is yes! This talk is focused on showing how to create a Cassandra cluster based on Docker, and how to build an application that connects to it and uses it with F# in a cross platform way! I will show how easy it is to use fully open source tools such as F# with Project Scaffold in Xamarin Studio on OSX to create a solution that uses advanced open source distributed database Cassandra on Docker. view the video on Channel 9 .

Machine learning is gaining momentum with the increase of necessity to understand data much more efficiently, to predict better for competitive profit and research. In this talk we'll run over various machine learning algorithms available in the Accord.NET - a framework for machine learning and scientific computing in .NET. We'll also have a look at sample tasks to see how we can apply machine learning algorithms using Accord.NET framework with F# functional approach and C#.

I have also spoken at QCon in San Francisco with the same topic.

This talk will show the lightweight approaches for web based visualization of server side computations or data science jobs, which are primarily written in F#. In terms of these jobs you'd often launch some analytic algorithms on input data and the algorithms may run either on a single local server or be distributed across the cloud, clusters or other locations. In other words there can be different data sources, different places or locations where algorithms are being performed, and various kinds of jobs that produce the updated overall result each time when data changes, updates or new data comes. For example we can use MBrace or other cloud computing framework for performing jobs, Deedle for time series analysis or any other tool. So if updates are frequent and it is necessary to keep an eye on that and to watch changes it would be pretty convenient to have an interactive visualization for the results.

During the talk we will take a number of jobs, go through the options we have and see the examples of how to use and combine F#, SignalR web sockets and JS together for visualization of various computing job results to display them in a beautiful web interactive and flexible way.

You can watch the talk on the SkillsMatter page here.

The interview I had with Seth Juarez at .NET FRINGE conference in Portland, OR. I gave a talk about functional approach for cloud computations and big data using F# language and MBrace framework and in this interview I am taking you through using MBrace to distribute work across multiple machines using F# - view the video on Channel 9 .

The talk is about F#, MBrace and Azure as uber-innovative open source tools for distributed programming, cloud computing and big data inspired by functional paradigm and wonders of monads.

I will describe computation expressions or monadic computations, which is the unique F# feature that makes it possible to introduce language integrated domain specific language to control the custom flow, side effects, ambiguous unpredictable behavior and shows absolutely innovative usage of F# language.

You'll see the open-source MBrace framework built on the base of F# computation expressions as a distributed computational framework engine for cloud computing and performing highly loaded jobs on big data, executing in non-blocking way, managing the state across the cluster of same-network nodes and making all that stuff easy for the software engineer. I will give you a tour through MBrace framework constructions, its architectural design and practical implementation model and demonstrate the full solution potential with Azure infrastructural services and open source Brisk Engine.

As a result, you'll see the undeniable advantages of both individual technologies: F#, MBrace, Azure and their combination in solving complex and frequently used tasks, like highly scalable data-intensive computations, processing of huge amounts of distributed data in cloud environment.

Architecture construction examples, code fragments, demo of parallel computation tasks execution and the result will be presented, clearly showing that .NET Open Source stack is finally shooting ahead.