On 6–7th August, Sydney will host the 2nd international conference for Predictive APIs and Apps. PAPIs will bring together machine learning practitioners from industry, government and academia to present new developments, identify new needs and trends, and discuss the challenges of building real-world predictive applications.
I look forward to discussing the lessons learned from the development of Seldon, which was originally a closed, black-box predictive API, now transitioned to an open-source model. Here’s the abstract from my speaking proposal.
Large organisations and start-ups are increasingly investing in building in-house predictive data science and trying to figure out how machine intelligence can be deployed to solve real-world business problems. Data scientists are demanding more control and flexibility than existing predictive APIs delivered over HTTP by third parties can offer.
By closely examining my experience of being the first commercial predictive API to switch to an open-source model after operating at scale, I will explore an important paradigm shift that will affect everyone in the industry. Over the last decade, most of the industry-changing innovation has occurred lower down in the data science stack. Why has it taken so long for innovation to move higher up the stack?
The standard method for building predictive APIs was to connect the separate open-source components that make a modern predictive data science infrastructure, create algorithms that can build predictive models from live behavioural data, and then deliver the service to projects or consumers via an API. Communities of contributing developers helped to accelerate the innovation of new open source technologies -such as Spark- moving from early brittle releases to the prime time. Data scientists have been building “black boxes” to protect IP; however this leads to many companies working to reinvent the wheel.
We are now entering a new phase of commoditization by using open-source across the entire data science stack, all the way from hardware to OS to algorithms. This openness brings with it an exciting phase of accelerated innovation and opportunities for collaboration, even between organisations that had previously considered themselves to be competitors.
This fundamental shift in industry economics requires a rethinking of existing business models and comes at a time when predictive APIs are specialising to serve vertical business model because each industry requires domain-specific knowledge.
Open platforms are an opportunity for standardisation of the back-end predictive data science stack, with pluggable architectures that enable developers of front-end algorithms to focus on solving the last mile of domain-specific problems. Open innovation and tools are particularly important because there is a shortage of skilled data scientists with specialist industry knowledge (e.g. finance or genomics) and accelerating demand.
An open data science stack reduces time to market, enabling data scientists to focus on solving the problems specific to their business and deploy cutting edge machine intelligence within their organisation. Enabling faster adoption of both open and closed machine intelligence advancements leads to exciting new possibilities. For example, neural networks that can make sense of images in the field of computer vision and transcribe audio can be combined with predictive models and decision-making expert systems to make machines more human.
If you have any comments on my talk abstract, let’s kick off the discussion in advance. And if you enjoyed this post, please hit the recommend button to say thanks. I’d love to hear from anyone who will be attending PAPIs 2015 on 6–7th August or KDD 2015 on 10–13th August in Sydney. Get a 20% discount on PAPIs 2015 registration by using the promo code SELDON.
To find out more about Seldon, join our newsletter, read our technical docs, and star, watch and fork us on Github, follow @seldon_io on Twitter and like our Facebook Page. And feel free to drop me a line directly.
London has certainly become an artificial intelligence centre of excellence, with Google’s acquisition of Deepmind in 2014 and world-class research filtering through from Imperial, UCL, Oxford and Cambridge.
At Seldon’s HQ, Warner Yard, we have seen an increase in start-ups with AI at the core of their offering in the last year, particular from the most recent TechStars cohort.
Our investors at Playfair Capital are particularly interested in the rapidly accelerating AI space and have organised an event called “Machine Intelligence 2015”, that will take place this Friday 5th June, hosted by Bloomberg and streaming on Bloomberg TV.
It’s an honour to be speaking alongside people from Deepmind, Centre of Existential Risk, Skype, Skimlinks, Swiftkey, Yahoo Research, VocalIQ, Snips, FT, Bloomberg, Balderton, Imperial and UCL. I will talk about the importance of open source for progressing the development of AI, commercial applications, and explore another potential future path for AGI.
Here are a few words about the event from Playfair:
Bringing together expert leaders in their respective fields, be that cutting-edge startups, leading academics or large company executives, we will delve into each main area of artificial intelligence. Whether its product recommendation engines, predictive search, text and speech synthesis, computer vision, we are moving towards a world of ubiquitous technology. A world where the rate of advancements in technology impacting the way we live, work and play is accelerating at an exponential rate. How will this change you or your business in the future?
Seldon has been committed to R&D focused around machine learning and natural language processing since 2011 – we built a high-performance recommendation engine API. At the point where the platform had scaled to hundreds of millions of API calls per month, we broadened our scope to deliver an open-source general-purpose prediction platform. We hit a major milestone on this journey this week with our v0.93 release which includes a new predict endpoint.
For more info, check out the event page on Playfair’s website.
There will be a couple of Seldon engineers joining me at the event on Friday and we hope to see you there.
CEO @ Seldon
We are excited by the response to Seldon VM, the virtual machine that we released last month. Based on your feedback have also created an AMI so that you can quickly get started on Amazon Web Services.
The Seldon AMI is a self-contained environment with the full Seldon platform pre-configured for you to test with your service data. It contains the same tools as Seldon VM, and a movie recommender demo that serves as an example to show you how to load your data.
To get the Seldon AMI, please request access using this form and we will whitelist your AWS account.
For more information, please read the Seldon AMI for Amazon Web Services documentation.
We’re excited that the Telegraph ran a story about Seldon today! This is the first mention of Seldon in the national press since we came our of stealth last month.
“Other companies only offer black box solutions,” he said. “They hide all the technology that’s happening underneath the surface so you’re forced to keep pushing your data into these systems.”
Seldon is building a “glass-walled platform” in order to accelerate the development of its system. Black box versions take much longer to build; with open-source, hundreds of developers help by creating “add-ons” – unique pieces of code that make the system relevant to their business.
Read the full story by Rebecca Burn-Callander.
Thanks to the organisers of City Meets Tech for selecting Seldon to pitch last week at Level39 in Canary Wharf. It’s a fantastic event that helps to bridge the gap between start-ups and people in the finance world who want to engage with startups in various capacities.
Here’s an extract:
So why is Seldon different? Billion-dollar companies are hooking their customers into monolithic proprietary black box platforms. And they want to break free. This is why Seldon is going open source…
The thing is: modern organisations want more control and are investing in data science right now. We’re shipping an enterprise-grade platform based on three years and a couple of million pounds R&D and building an ecosystem around it. We’re unleashing a glass wall data science stack and it is a game changer.
You can read the full pitch on Medium – apparently it takes 3 minutes!
Seldon @ City Meets Tech