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Open Sourcing a Predictive API - PAPIs 2015, Sydney
After operating for three years as a “black box” predictive API, Seldon recently open-sourced it’s entire predictive stack. Alex will talk about Seldon’s journey from closed to open: the challenges and pitfalls, architectural considerations, case studies, changes to business models, and new opportunities for partnership across the full stack - between both open and closed technology providers.
INTRO It’s exciting to have travelled all the way from London to be with you here today in Sydney. We released Seldon’s open source predictive platform in Feb after four years of R&D from an exceptional team of data scientists. Seldon is tried and tested as a closed API in demanding enterprise environment serving billions of recommendations every month.
Last AUGUST- 2 choices I will talk about how OS helped us to survive and thrive. This is not meant to be a an evangelical talk about how I have “seen the light”, I want you share with you some of the things we have learned from moving closed to open.
[set the scene] You’re in a hot, Sweaty board room, mid-Summer,
We were a SaaS predictive API with two PRODUCTS: Recommendation Engine and Predictive Social Sharing.
FACED WITH 2 OPTIONS: 1. GET ACQUI-HIRED 2. START SOMETHING NEW
We had spent the prev 9 months speaking to potential acquirers. During this process I had a front row seat on the priorities of some of world’s leading media, ecommerce, technology companies.
COMMON PATTERN emerged: large companies putting data science and prediction at the very top if their strategic agenda for investment. They were acquisitive and recruiting expensive teams.
Made me think: WHAT WOULD BE THE IMPACT OF MAKING THE FULL PREDICTIVE STACK OPEN SOURCE?
So I decided to take the business in a new open source direction.
WHY? Companies want more CONTROL to solve the problems specific to their business.
PARATOS LAW in action – people should be spending 80% of their time solving the 20% of the domain-specific problems that make the biggest impact on their business, but otherwise data scientists are focusing on the remaining 80% and wasting time on reinventing the wheel.
The DJ analogy: Each channel on the mixer represents a predictive model. Parameters are the controls on the DJ mixer – the controls represent hyper-parameters. Data scientist is the DJ listening to the audience and adjusting the controls accordingly. Seldon gives the DJ super powers. Enabling them to play all of the stages of a festival at the same time, so your audiences is not stuck listening to Lionel Richie (collaborative filtering) if you want to listen to Metallica (matrix factorization)
But remember, you can always tap the DJ on the shoulder and make special requests.
Build in house: DEMAND - there are far fewer for truly skilled machine learning and AI developers than big data engineers. Improving internal data science capabilities is increasingly important for companies. So they are hiring or aquahiring teams of data scientists.
3rd party: DATA SECURITY - there are many companies with data control policies that require the hosting of consumer data behind their firewall, which a flexible open source solution will allow. Sometimes there’s no transparency on algorithms.
Open Source. MARKET DISRUPTION Open source technologies such as Docker, Hadoop, and Apache Spark, have superseded proprietary operating systems and databases. Meanwhile, most vendors higher up the data science stack (i.e. providing predictive analytics, recommendations, and machine learning APIs) are effectively licensing black box solutions. Seldon wants to reduce barriers to entry and get the Seldon’s technology that we believe in the hands of as many developers as possible.
Want to know a secret? Most people think the value of predictive APIs are in the algorithm. VALUE NOT IN ALGORITHM. Why is this? Instead in the model. Example: Torch, Spark mlib, etc.
Businesses wanting to solve their own / domain-specific problems.
Seldon isn’t an OS library, it’s a full stack solution. Include best of Open Source and algorithms built ourselves. 1. CONNECT YOUR DATA - Ingesting behavioural data from events that contains metadata and context such as device and location. 2. MODEL BUILDING - Multiple models are built based on desired Goals (Could be a KPI or an action/event). Behavioural data plus algorithms are used to train the predictive models. Models are optimised in a recursive way… computationally inefficient to store all the possible alternatives… realtime behavioural data needs to update the models in realtime. value not in the algorithm, the value is in the model. 3. OUTPUTS - there are currently two outputs for Seldon - one is a recommendation and another is a prediction (score). FEEDBACK LOOP
FURTHER INFO Seldon pulls in behavioral data from any digital environment, builds predictive models and outputs recommendations and predictions at SCALE. Working in media, advertising and ecommerce. But have built a generic platform with a broad range of applications including finance, insurance and healthcare.
There are INFINITE algorithm configurations to choose from. So which ones are best for my business? Example of when different models used: high/low churn, days of week. Userbase or product mix changes. Seasonal changes. YOU CAN USE YOUR OWN ALGORITHMS AND MODELS.
The model selection will vary depending on the user type. OLD WAY - AB testing. each test used to be a manual process measure the impact of recommendations on KPIs such as CTR, conversion rate, etc.. NEW WAY – CONTINUOUS TESTING of all models that diverts more traffic towards the model performing best at that specific point in time. Called a MULTI-ARMED BANDIT, inspired by a strategy to play a room full of slot machines. Give a user case of which algorithms would be used high churn news environment. You want to make the case that it’s not 1 algorithm/model but a combination of different models that will maximise your KPI. And that’s why you need Seldon. We A/B test from set of INDUSTRY MODELS to find a combination of model selections that work best for your business for a given user at a given time. TIME SAVED in choosing the best model. Seldon increases the productivity of your data science teams and helps your business to increase profits through rapid prototyping and better KPI performance.
3. OPEN SOURCE SETUP. SaaS platform grows roots, provisioning usually internal process with continuous integration and deployment. Rarely setup new infrastructure from scratch. DOCUMENTATION. SaaS businesses don’t need as much. Docs on Github so people can commit changes – first pull requests. Documentation gets the highest engagement to see how to use it. [show documentation] COMMUNITY – newsletter, github, detail release notes, clean codebase, future: events, etc. Important for us.
CHOOSING A LICENSE. Reason for Apache 2 vs LGPL/GPL – better for business because they don’t have to make modifications open source. SALES CYCLE – pros: more leads, and cons: longer w light touch. Previously contract / trial with decision makers before getting tech in hands of developers. TRACKING – companies can be much further along the funnel because they don’t have to sign up to get started. Example call from major tech company. VALUE CHAIN – where we sit now we have disrupted ourselves. TIMING (OS more strategic and sometimes SaaS is a better option). SAAS – open source is a great driver of SaaS customers. Many companies want to start with SaaS and have a longer term plan to move on prem and work with custom algos.
4. TECHNICAL PART MAIN COMPONENTS – REST API SERVERS, ZOOKEEPER, FLUENTD, KAFKA, SPARK, MEMCACHE, JDBC database… VIRTUAL MACHINE - Setting up infrastructure is complex. Portable - Developer can download it and straight away access the infrastructure. Movie demo. DOCKER / AMIs - technology that allows you to use on different platforms (docker container = shipping container. Part of our infrastructure in each container). Deploy on premise, cloud (AWS, Google) or SaaS. [encourage people to register for AMIs]
MICROSERVICES – make the Seldon stack completely pluggable with third party code / algos developed by your data scientists IN ANY LANGUAGE (R, Python, etc) that can be put into production and utilise the same pipeline as core Seldon algorithms. OPEN SOURCE LIBRARIES – we already used microservices to connect an OS library called Vowpal Wabbit (Yahoo research) to power our new predict endpoint. This enables regression, binary and multi-class classification. We are connected to the leading machine intelligence libraries such as Torch. CLOSED APIs – third parties who provide We can leverage third party APIs such as IBM Watson for personality insights or text to speech. ENTERPRISE DISTRIBUTIONS – Seldon are already planning integrations with some of the leading enterprise distributions.
(quite often this is the elephant in the room – it’s often the first question we get asked) OS is revolutionising the way in which we do business. Since we released Seldon in Feb it is already starting to be used by world’s largest companies.
FREE - First, don’t expect more than a small percentage of activated users to pay.
ADVISORY. ML is complicated and more projects have unique requirements. Advisory services, enables us to spend more time with customers, understanding their problems so ultimately leads to building a better platform.
MLAAS. Finally, we have a SaaS platform! Many companies find us via the OS but SaaS the best starting point. Companies like the MIGRATION PATH [can reference later in call for open models]
ECOSYSTEM. Third party technology, APIs and models. Value in creating an ecosystem. Seldon can offer distribution and monetization.
BEFORE TAKING SOME QUESTIONS, I’LL FINISH WITH A QUESTION. Can we change the way in which people view their competition? London is become a centre of excellence for AI. And companies that collaborate as part of an ecosystems have a competitive advantage. Since Seldon went OS had an open door policy about speaking to “competitors”. Download Seldon’s open source, VM or AMI... visit seldon.io or develors head on over to docs.seldon.io Contact me if you would like to help out or to discuss how we can help add machine intelligence into your business. Or other AI companies that want to partner. THANK YOU
BONUS SLIDE (from recent AI talk at Bloomberg)
Take some of the ideas and things we’ve been talking about at Seldon; extrapolate to the different field of AI and look into the future… DISTRIBUTED – Would you prefer for a single org to control AGI and wield all the power? Or a distributed network of specialized nodes with no central point of control like blockchain? There are already many disconnected ANIs. They’re everywhere. On our handsets, cars, robotics used in manufacturing. With this comes enormous volumes of data on which machine learning models can be run. There are thousands of models to choose from depending on the specific user case. Different companies are specializing in fields of ANI. Could one route to AGI be to connect these ANIs in an open network… This could lead to the development of an open AGI that would would be greater than sum of parts. For example Daniel was talking about self driving cars. Cameras in home and body sensors observe that you do not look happy and might be stressed. They instruct your self driving car to take a different route to maximize your happiness levels. At each step in the process, the AGI would need to choose from lots of different models which is the best to use to try and determine the next action. Computer vision connected > Reasoning > Natural Language Processing > Automated Planning & Scheduling > Robotics OPEN STANDARD - Need to connect AI developments and models through an open standard. Whether that's a communication protocol or open source project. Example: PMML open standard for predictive models. Predictive Model Markup Language. Seldon could end up connecting nodes and models.
LEAVE OUT A closed AGI could be harmful for humanity if the decisions it make were not under public scrutiny.
Open Sourcing a Predictive API - PAPIs 2015, Sydney
Machine Intelligence for Enterprise
FOUNDER AND CEO
Open Sourcing a Predic0ve API
7th August 2015, Sydney
Data scien0sts want control
How to add machine intelligence to your company
3rd Party API
Control Model EvaluaCon Time Data ScienCsts Cost
Industry Models +
Monitor Impact on
How Seldon works
Selec0ng the best model
Open Source Launch
Fully Integrated Open-Source Ecosystem
Microservices API Open Source Libraries
Closed APIs Enterprise DistribuCons
How do you make money?
(the elephant in the room)
• Data scienCsts want more control.
• Open source costs less but ocen takes longer than SaaS.
• When given the opCon, many want to start with SaaS.
• Developers love open source and are awesome advocates.
• Microservices are the future.
• Let’s build an ecosystem and work together.
Alex Housley @ahousley firstname.lastname@example.org
Thank you! Ques0ons?
Seldon’s vision for the future: distributed arCﬁcial general intelligence
Open AGI Network