The open-source development model has played a pivotal role in the steady emergence of machine learning and artificial intelligence into the mainstream. Many libraries and frameworks are available to developers as open source code, and cloud computing giants like Google, Microsoft, and AWS have led the way in providing many of these projects. One reason for the influx of open source machine learning and AI projects is that it lowers the barriers to entry for developers who can experiment and become proficient with high-quality frameworks, libraries, and applications. Most enterprises are familiar with the exciting use cases for machine learning and AI apps. In fact, 54 percent of executives are already actively investing in AI.

As of last month Alphabet Inc.'s AI division, Google DeepMind, has open-sourced their new machine learning platform DeepMind Lab. Artificial Intelligence is the technology of the moment, constantly debated and attracting massive attention from investors. Despite warnings from influential figures including Professor Stephen Hawking, Google's decision to open up their software to other developers is part of a mass movement to advance the capabilities of AI. Facebook open sourced its own deep learning software last year, and Elon Musk's non-profit organisation OpenAI recently released Universe, an open software platform that can be used to train AI systems. So, why have Google, OpenAI and others made these platforms public, and how will this affect the adoption of Artificial Intelligence and machine learning as a whole?

AI in Production – AI is only used by banks in production in a few key use cases such as high-frequency trading, fraud detection and credit scoring. In 2016 many machine learning R&D projects started across other business functions. In 2017 banks will move from testing machine learning models to putting models into production to make a real impact on business KPIs. Open-Source AI Platforms – Leading on from the last point, banks will have to consider if the best strategy for operationalizing models is to use a major cloud vendor, proprietary tech, open-source tech or in-house build. I think the winning combination is an open-source core machine learning platform supported by in-house R&D higher up the stack, and cloud provider focused mostly on the lower level compute tasks.

As of last month Alphabet Inc.'s AI division, Google DeepMind, has open-sourced their new machine learning platform DeepMind Lab. Artificial Intelligence is the technology of the moment, constantly debated and attracting massive attention from investors. Despite warnings from influential figures including Professor Stephen Hawking, Google's decision to open up their software to other developers is part of a mass movement to advance the capabilities of AI. Facebook open sourced its own deep learning software last year, and Elon Musk's non-profit organisation OpenAI recently released Universe, an open software platform that can be used to train AI systems. So, why have Google, OpenAI and others made these platforms public, and how will this affect the adoption of Artificial Intelligence and machine learning as a whole?

In the 1.4 release of Seldon we have added an alpha release of gRPC endpoints to complement our REST and Javascript endpoints. Remote Procedure Calls (RPC) and Google's version of this (gRPC) provides several advantages over REST. However, gRPC may be unfamiliar to many and requires a certain expertise for the developer building a gRPC client or server. For the 1.4 release of Seldon we have added gRPC as an external prediction endpoint as well as allowing prediction microservices to be deployed as gRPC servers internal to Seldon. The stages to deploy a gRPC service are discussed in detail in our docs and our summarized below.

AI in Production – AI is only used by banks in production in a few key use cases such as high frequency trading, fraud detection and credit scoring. In 2016 many machine learning R&D projects started across other business functions. In 2017 banks will move from testing machine learning models to putting models into production to make a real impact on business KPIs. Open-Source AI Platforms – Leading on from the last point, banks will have to consider if the best strategy for operationalising models is to use a major cloud vendor, proprietary tech, open-source tech or in-house build. I think the winning combination is an open-source core machine learning platform supported by in-house R&D higher up the stack, and cloud provider focused mostly on the lower level compute tasks.

When millions of people across the world tuned in to watch DeepMind's machine beat the human Go world champion Lee Sedol, they also witnessed a historic victory for open-source. DeepMind used a scientific computing framework called Torch extensively in the development and execution of AlphaGo's neural networks. Torch was first released back in 2002 under a BSD open-source license with algorithms that are still commonly used by data scientists such as multi-layer perceptrons, support vector machines and K-nearest neighbours. Torch also supported ensembles -- a popular technique that combines the output of multiple algorithms, usually with a weighted average. It's not just open-source software that contributed to the growth of machine learning.

There is so much data today that no one can possibly process it all. While a significant amount of companies have data that can reveal customer satisfaction and attrition, many don't know how to use or even find it. There is hope from a field called machine learning, and the next big wave in this field is all about democratizing the technology from a few to many. Open source tools are reshaping the potential of data management with machine learning. Learn now about ways in-memory compute engines can unify developers, data scientists and data engineers in a user-friendly format.

We just finished Barclays Accelerator London, the world's leading fintech accelerator programme powered by Techstars. It was such an honour to share what Seldon has been building at Demo Day on 18th April 2016. Pitching to a 1000-strong audience at the O2 was a fitting finale to an incredible 13-week Techstars programme -- we all learned so much, connected with hundreds of great people, and established a new market for Seldon in the finance sector. We were delighted to announce a project with Barclaycard to help identify customers that default after receiving credit increases. This could not only save millions in missed payments, but would also result in fewer customers in financial difficulty.

Over the past few years the field of Machine Learning has entered the general parlance. From free massive open online courses to image recognition benchmarks being broken and decades of Atari games being mastered. During the same period developers have witnessed the release of several popular open source frameworks and libraries. The chart below shows different open source machine learning projects by initial commit date and programming language. The size represents the popularity of a project based on number of Github stargazers.