at Astra Zeneca Data Science Meetup on Nov 28, 2019 [reveal]
Neil D. Lawrence, University of Cambridge

Abstract

Machine learning solutions, in particular those based on deep learning methods, form an underpinning of the current revolution in “artificial intelligence” that has dominated popular press headlines and is having a significant influence on the wider tech agenda. In this talk I will give an overview of where we are now with machine learning solutions, and what challenges we face both in the near and far future. These include practical application of existing algorithms in the face of the need to explain decision making, mechanisms for improving the quality and availability of data, dealing with large unstructured datasets.

What is Machine Learning? 

What is machine learning? At its most basic level machine learning is a combination of

$$\text{data} + \text{model} \xrightarrow{\text{compute}} \text{prediction}$$

where data is our observations. They can be actively or passively acquired (meta-data). The model contains our assumptions, based on previous experience. That experience can be other data, it can come from transfer learning, or it can merely be our beliefs about the regularities of the universe. In humans our models include our inductive biases. The prediction is an action to be taken or a categorization or a quality score. The reason that machine learning has become a mainstay of artificial intelligence is the importance of predictions in artificial intelligence. The data and the model are combined through computation.

In practice we normally perform machine learning using two functions. To combine data with a model we typically make use of:

a prediction function a function which is used to make the predictions. It includes our beliefs about the regularities of the universe, our assumptions about how the world works, e.g. smoothness, spatial similarities, temporal similarities.

an objective function a function which defines the cost of misprediction. Typically it includes knowledge about the world’s generating processes (probabilistic objectives) or the costs we pay for mispredictions (empiricial risk minimization).

The combination of data and model through the prediction function and the objectie function leads to a learning algorithm. The class of prediction functions and objective functions we can make use of is restricted by the algorithms they lead to. If the prediction function or the objective function are too complex, then it can be difficult to find an appropriate learning algorithm. Much of the acdemic field of machine learning is the quest for new learning algorithms that allow us to bring different types of models and data together.

A useful reference for state of the art in machine learning is the UK Royal Society Report, Machine Learning: Power and Promise of Computers that Learn by Example.

You can also check my post blog post on What is Machine Learning?..

Artificial Intelligence and Data Science 

Machine learning technologies have been the driver of two related, but distinct disciplines. The first is data science. Data science is an emerging field that arises from the fact that we now collect so much data by happenstance, rather than by experimental design. Classical statistics is the science of drawing conclusions from data, and to do so statistical experiments are carefully designed. In the modern era we collect so much data that there’s a desire to draw inferences directly from the data.

As well as machine learning, the field of data science draws from statistics, cloud computing, data storage (e.g. streaming data), visualization and data mining.

In contrast, artificial intelligence technologies typically focus on emulating some form of human behaviour, such as understanding an image, or some speech, or translating text from one form to another. The recent advances in artifcial intelligence have come from machine learning providing the automation. But in contrast to data science, in artifcial intelligence the data is normally collected with the specific task in mind. In this sense it has strong relations to classical statistics.

Classically artificial intelligence worried more about logic and planning and focussed less on data driven decision making. Modern machine learning owes more to the field of Cybernetics (Wiener 1948) than artificial intelligence. Related fields include robotics, speech recognition, language understanding and computer vision.

There are strong overlaps between the fields, the wide availability of data by happenstance makes it easier to collect data for designing AI systems. These relations are coming through wide availability of sensing technologies that are interconnected by celluar networks, WiFi and the internet. This phenomenon is sometimes known as the Internet of Things, but this feels like a dangerous misnomer. We must never forget that we are interconnecting people, not things.

Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data (1981/1/28)

Data Science Africa 

Data Science Africa is a bottom up initiative for capacity building in data science, machine learning and artificial intelligence on the African continent.

As of 2019 there have been five workshops and five schools, located in Nyeri, Kenya (twice); Kampala, Uganda; Arusha, Tanzania; Abuja, Nigeria; Addis Ababa, Ethiopia and Accra, Ghana. The next event is scheduled for June 2020 in Kampala, Uganda.

The main notion is end-to-end data science. For example, going from data collection in the farmer’s field to decision making in the Ministry of Agriculture. Or going from malaria disease counts in health centers to medicine distribution.

The philosophy is laid out in (Lawrence 2015). The key idea is that the modern information infrastructure presents new solutions to old problems. Modes of development change because less capital investment is required to take advantage of this infrastructure. The philosophy is that local capacity building is the right way to leverage these challenges in addressing data science problems in the African context.

Data Science Africa is now a non-govermental organization registered in Kenya. The organising board of the meeting is entirely made up of scientists and academics based on the African continent.

Example: Prediction of Malaria Incidence in Uganda 

As an example of using Gaussian process models within the full pipeline from data to decsion, we’ll consider the prediction of Malaria incidence in Uganda. For the purposes of this study malaria reports come in two forms, HMIS reports from health centres and Sentinel data, which is curated by the WHO. There are limited sentinel sites and many HMIS sites.

The work is from Ricardo Andrade Pacheco’s PhD thesis, completed in collaboration with John Quinn and Martin Mubangizi (Andrade-Pacheco et al. 2014; Mubangizi et al. 2014). John and Martin were initally from the AI-DEV group from the University of Makerere in Kampala and more latterly they were based at UN Global Pulse in Kampala.

Malaria data is spatial data. Uganda is split into districts, and health reports can be found for each district. This suggests that models such as conditional random fields could be used for spatial modelling, but there are two complexities with this. First of all, occasionally districts split into two. Secondly, sentinel sites are a specific location within a district, such as Nagongera which is a sentinel site based in the Tororo district.

(Andrade-Pacheco et al. 2014; Mubangizi et al. 2014)

The common standard for collecting health data on the African continent is from the Health management information systems (HMIS). However, this data suffers from missing values (Gething et al. 2006) and diagnosis of diseases like typhoid and malaria may be confounded.

World Health Organization Sentinel Surveillance systems are set up “when high-quality data are needed about a particular disease that cannot be obtained through a passive system”. Several sentinel sites give accurate assessment of malaria disease levels in Uganda, including a site in Nagongera.

In collaboration with the AI Research Group at Makerere we chose to investigate whether Gaussian process models could be used to assimilate information from these two different sources of disease informaton. Further, we were interested in whether local information on rainfall and temperature could be used to improve malaria estimates.

The aim of the project was to use WHO Sentinel sites, alongside rainfall and temperature, to improve predictions from HMIS data of levels of malaria.

Early Warning Systems

Health monitoring system for the Kabarole district. Here we have fitted the reports with a Gaussian process with an additive covariance function. It has two components, one is a long time scale component (in red above) the other is a short time scale component (in blue).

Monitoring proceeds by considering two aspects of the curve. Is the blue line (the short term report signal) above the red (which represents the long term trend? If so we have higher than expected reports. If this is the case and the gradient is still positive (i.e. reports are going up) we encode this with a red color. If it is the case and the gradient of the blue line is negative (i.e. reports are going down) we encode this with an amber color. Conversely, if the blue line is below the red and decreasing, we color green. On the other hand if it is below red but increasing, we color yellow.

This gives us an early warning system for disease. Red is a bad situation getting worse, amber is bad, but improving. Green is good and getting better and yellow good but degrading.

Finally, there is a gray region which represents when the scale of the effect is small.

These colors can now be observed directly on a spatial map of the districts to give an immediate impression of the current status of the disease across the country.

Supply Chain 

On Sunday mornings in Sheffield, I often used to run across Packhorse Bridge in Burbage valley. The bridge is part of an ancient network of trails crossing the Pennines that, before Turnpike roads arrived in the 18th century, was the main way in which goods were moved. Given that the moors around Sheffield were home to sand quarries, tin mines, lead mines and the villages in the Derwent valley were known for nail and pin manufacture, this wasn’t simply movement of agricultural goods, but it was the infrastructure for industrial transport.

The profession of leading the horses was known as a Jagger and leading out of the village of Hathersage is Jagger’s Lane, a trail that headed underneath Stanage Edge and into Sheffield.

The movement of goods from regions of supply to areas of demand is fundamental to our society. The physical infrastructure of supply chain has evolved a great deal over the last 300 years.

Cromford 

Richard Arkwright is known as the father of the modern factory system. In 1771 he set up a Mill for spinning cotton yarn in the village of Cromford, in the Derwent Valley. The Derwent valley is relatively inaccessible. Raw cotton arrived in Liverpool from the US and India. It needed to be transported on packhorse across the bridleways of the Pennines. But Cromford was a good location due to proximity to Nottingham, where weavers where consuming the finished thread, and the availability of water power from small tributaries of the Derwent river for Arkwright’s water frames which automated the production of yarn from raw cotton.

By 1794 the Cromford Canal was opened to bring coal in to Cromford and give better transport to Nottingham. The construction of the canals was driven by the need to improve the transport infrastructure, facilitating the movement of goods across the UK. Canals, roads and railways were initially constructed by the economic need for moving goods. To improve supply chain.

The A6 now does pass through Cromford, but at the time he moved there there was merely a track. The High Peak Railway was opened in 1832, it is now converted to the High Peak Trail, but it remains the highest railway built in Britain.

Cooper (1991)

Containerization 

Containerization has had a dramatic effect on global economics, placing many people in the developing world at the end of the supply chain.

For example, you can buy Wild Alaskan Cod fished from Alaska, processed in China, sold in North America. This is driven by the low cost of transport for frozen cod vs the higher relative cost of cod processing in the US versus China. Similarly, Scottish prawns are also processed in China for sale in the UK.

This effect on cost of transport vs cost of processing is the main driver of the topology of the modern supply chain and the associated effect of globalization. If transport is much cheaper than processing, then processing will tend to agglomerate in places where processing costs can be minimized.

Large scale global economic change has principally been driven by changes in the technology that drives supply chain.

Supply chain is a large-scale automated decision making network. Our aim is to make decisions not only based on our models of customer behavior (as observed through data), but also by accounting for the structure of our fulfilment center, and delivery network.

Many of the most important questions in supply chain take the form of counterfactuals. E.g. “What would happen if we opened a manufacturing facility in Cambridge?” A counter factual is a question that implies a mechanistic understanding of a system. It goes beyond simple smoothness assumptions or translation invariants. It requires a physical, or mechanistic understanding of the supply chain network. For this reason, the type of models we deploy in supply chain often involve simulations or more mechanistic understanding of the network.

In supply chain Machine Learning alone is not enough, we need to bridge between models that contain real mechanisms and models that are entirely data driven.

This is challenging, because as we introduce more mechanism to the models we use, it becomes harder to develop efficient algorithms to match those models to data.

SafeBoda 

SafeBoda is a Kampala based rider allocation system for Boda Boda drivers. Boda boda are motorcycle taxis which give employment to, often young men, across Kampala. Safe Boda is driven by the knowledge that road accidents are set to match HIV/AIDS as the highest cause of death in low/middle income families by 2030.

With road accidents set to match HIV/AIDS as the highest cause of death in low/middle income countries by 2030, SafeBoda’s aim is to modernise informal transportation and ensure safe access to mobility.

So many examples in terms of the need for intelligent decision making are based around the challenge of moving goods/energy/compute/water/medicines/drivers/people from where it is to where it needs to be. In other words matching supply with demand. That led me to a motto I developed while working in Amazon’s supply chain.

Solve Supply Chain, then solve everything else.

Turing AI Fellowship 

From December 2019 I begin a Senior AI Fellowship at the Turing Institute funded by the Office for AI to investigate the consequences of deploying complex AI systems.

The notion relates from the “Promise of AI”: it promises to be the first generation of automation technology that will adapt to us, rather than us adapting to it. The premise of the project is that this promise will remain unfulfilled with current approaches to systems design and deployment.

Project Description

It used to be true that computers only did what we programmed them to do, but today AI systems are learning from our data. This introduces new problems in how these systems respond to their environment.

We need to better monitor how data is influencing decision making and take corrective action as required.

Aim

Our aim is to scale our ability to deploy safe and reliable AI solutions. Our technical approach is to do this through data-oriented software engineering practices and deep system emulation. We will do this through a significant extension of the notion of Automated ML (AutoML) to Automated AI (AutoAI), this relies on a shift from Bayesian Optimisation to Bayesian System Optimisation. The project will develop a toolkit for automating the deployment, maintenance and monitoring of artificial intelligence systems.

SafeBoda and other projects like Kudu provide us with our motivating examples. Our aim is to create an ecosystem for machine learing system deployment that minimises the operational load. Ideally, we would like complex AI systems to be maintainable by a small team, e.g. two people, with Masters-level education from the institutions that host Data Science Africa (e.g. Ashesi University, Makerere University, Dedan Kimathi University of Technology, AUST, AIST, Addis Ababa).

As of 24th October 2019, the Turing Institute announced that this work has been funded through a Turing Institute Senior AI Fellowship. This is the first Senior AI fellowship and it provides funding for five years.

The project partners are Element AI, Open ML, Professor Sylvie Delacroix and Data Science Africa.

Inclusive Project

There is no way that the team we’re building will be able to deliver on this agenda alone, so please join us in addressing these challenges!

The Three Ds of Machine Learning Systems Design 

We can characterize the challenges for integrating machine learning within our systems as the three Ds. Decomposition, Data and Deployment.

You can also check my blog post on blog post on The 3Ds of Machine Learning Systems Design..

The first two components decomposition and data are interlinked, but we will first outline the decomposition challenge. Below we will mainly focus on supervised learning because this is arguably the technology that is best understood within machine learning.

Decomposition 

Machine learning is not magical pixie dust, we cannot simply automate all decisions through data. We are constrained by our data (see below) and the models we use.1 Machine learning models are relatively simple function mappings that include characteristics such as smoothness. With some famous exceptions, e.g. speech and image data, inputs are constrained in the form of vectors and the model consists of a mathematically well-behaved function. This means that some careful thought has to be put in to the right sub-process to automate with machine learning. This is the challenge of decomposition of the machine learning system.

Any repetitive task is a candidate for automation, but many of the repetitive tasks we perform as humans are more complex than any individual algorithm can replace. The selection of which task to automate becomes critical and has downstream effects on our overall system design.

Pigeonholing

Some aspects to take into account are

1. Can we refine the decision we need to a set of repetitive tasks where input information and output decision/value is well defined?
2. Can we represent each sub-task we’ve defined with a mathematical mapping?

The representation necessary for the second aspect may involve massaging of the problem: feature selection or adaptation. It may also involve filtering out exception cases (perhaps through a pre-classification).

All else being equal, we’d like to keep our models simple and interpretable. If we can convert a complex mapping to a linear mapping through clever selection of sub-tasks and features this is a big win.

For example, Facebook have feature engineers, individuals whose main role is to design features they think might be useful for one of their tasks (e.g. newsfeed ranking, or ad matching). Facebook have a training/testing pipeline called FBLearner. Facebook have predefined the sub-tasks they are interested in, and they are tightly connected to their business model.

It is easier for Facebook to do this because their business model is heavily focused on user interaction. A challenge for companies that have a more diversified portfolio of activities driving their business is the identification of the most appropriate sub-task. A potential solution to feature and model selection is known as AutoML (Feurer et al., n.d.). Or we can think of it as using Machine Learning to assist Machine Learning. It’s also called meta-learning. Learning about learning. The input to the ML algorithm is a machine learning task, the output is a proposed model to solve the task.

One trap that is easy to fall in is too much emphasis on the type of model we have deployed rather than the appropriateness of the task decomposition we have chosen.

Recommendation: Conditioned on task decomposition, we should automate the process of model improvement. Model updates should not be discussed in management meetings, they should be deployed and updated as a matter of course. Further details below on model deployment, but model updating needs to be considered at design time. This is the domain of AutoML.

To form modern decision-making systems, many components are interlinked. We decompose our complex decision making into individual tasks, but the performance of each component is dependent on those upstream of it.

This naturally leads to co-evolution of systems; upstream errors can be compensated by downstream corrections.

To embrace this characteristic, end-to-end training could be considered. Why produce the best forecast by metrics when we can just produce the best forecast for our systems? End-to-end training can lead to improvements in performance, but it would also damage our systems decomposability and its interpretability, and perhaps its adaptability.

The less human interpretable our systems are, the harder they are to adapt to different circumstances or diagnose when there’s a challenge. The trade-off between interpretability and performance is a constant tension which we should always retain in our minds when performing our system design.

Data 

It is difficult to overstate the importance of data. It is half of the equation for machine learning but is often utterly neglected. We can speculate that there are two reasons for this. Firstly, data cleaning is perceived as tedious. It doesn’t seem to consist of the same intellectual challenges that are inherent in constructing complex mathematical models and implementing them in code. Secondly, data cleaning is highly complex, it requires a deep understanding of how machine learning systems operate and good intuitions about the data itself, the domain from which data is drawn (e.g. Supply Chain) and what downstream problems might be caused by poor data quality.

A consequence of these two reasons, data cleaning seems difficult to formulate into a readily teachable set of principles. As a result, it is heavily neglected in courses on machine learning and data science. Despite data being half the equation, most University courses spend little to no time on its challenges.

Anecdotally, talking to data modelling scientists. Most say they spend 80% of their time acquiring and cleaning data. This is precipitating what I refer to as the “data crisis”. This is an analogy with software. The “software crisis” was the phenomenon of inability to deliver software solutions due to increasing complexity of implementation. There was no single shot solution for the software crisis, it involved better practice (scrum, test orientated development, sprints, code review), improved programming paradigms (object orientated, functional) and better tools (CVS, then SVN, then git).

The Data Crisis 

Anecdotally, talking to data modelling scientists. Most say they spend 80% of their time acquiring and cleaning data. This is precipitating what I refer to as the “data crisis”. This is an analogy with software. The “software crisis” was the phenomenon of inability to deliver software solutions due to increasing complexity of implementation. There was no single shot solution for the software crisis, it involved better practice (scrum, test orientated development, sprints, code review), improved programming paradigms (object orientated, functional) and better tools (CVS, then SVN, then git).

However, these challenges aren’t new, they are merely taking a different form. From the computer’s perspective software is data. The first wave of the data crisis was known as the software crisis.

The Software Crisis

In the late sixties early software programmers made note of the increasing costs of software development and termed the challenges associated with it as the “Software Crisis”. Edsger Dijkstra referred to the crisis in his 1972 Turing Award winner’s address.

The major cause of the software crisis is that the machines have become several orders of magnitude more powerful! To put it quite bluntly: as long as there were no machines, programming was no problem at all; when we had a few weak computers, programming became a mild problem, and now we have gigantic computers, programming has become an equally gigantic problem.

Edsger Dijkstra (1930-2002), The Humble Programmer

The major cause of the data crisis is that machines have become more interconnected than ever before. Data access is therefore cheap, but data quality is often poor. What we need is cheap high-quality data. That implies that we develop processes for improving and verifying data quality that are efficient.

There would seem to be two ways for improving efficiency. Firstly, we should not duplicate work. Secondly, where possible we should automate work.

What I term “The Data Crisis” is the modern equivalent of this problem. The quantity of modern data, and the lack of attention paid to data as it is initially “laid down” and the costs of data cleaning are bringing about a crisis in data-driven decision making. This crisis is at the core of the challenge of technical debt in machine learning (Sculley et al. 2015).

Just as with software, the crisis is most correctly addressed by ‘scaling’ the manner in which we process our data. Duplication of work occurs because the value of data cleaning is not correctly recognised in management decision making processes. Automation of work is increasingly possible through techniques in “artificial intelligence”, but this will also require better management of the data science pipeline so that data about data science (meta-data science) can be correctly assimilated and processed. The Alan Turing institute has a program focussed on this area, AI for Data Analytics.

Data is the new software, and the data crisis is already upon us. It is driven by the cost of cleaning data, the paucity of tools for monitoring and maintaining our deployments, the provenance of our models (e.g. with respect to the data they’re trained on).

Three principal changes need to occur in response. They are cultural and infrastructural.

First of all, to excel in data driven decision making we need to move from a software first paradigm to a data first paradigm. That means refocusing on data as the product. Software is the intermediary to producing the data, and its quality standards must be maintained, but not at the expense of the data we are producing. Data cleaning and maintenance need to be prized as highly as software debugging and maintenance. Instead of software as a service, we should refocus around data as a service. This first change is a cultural change in which our teams think about their outputs in terms of data. Instead of decomposing our systems around the software components, we need to decompose them around the data generating and consuming components.2 Software first is only an intermediate step on the way to becoming data first. It is a necessary, but not a sufficient condition for efficient machine learning systems design and deployment. We must move from software orientated architecture to a data orientated architecture.

Data Quality

Secondly, we need to improve our language around data quality. We cannot assess the costs of improving data quality unless we generate a language around what data quality means.

Data Readiness Levels (Lawrence 2017) are an attempt to develop a language around data quality that can bridge the gap between technical solutions and decision makers such as managers and project planners. The are inspired by Technology Readiness Levels which attempt to quantify the readiness of technologies for deployment.

See this blog onblog post on Data Readiness Levels..

Data-readiness describes, at its coarsest level, three separate stages of data graduation.

• Transition: data becomes electronically available
• Transition: pose a question to the data.

The important definitions are at the transition. The move from Grade C data to Grade B data is delimited by the electronic availability of the data. The move from Grade B to Grade A data is delimited by posing a question or task to the data (Lawrence 2017).

Recommendation: Build a shared understanding of the language of data readiness levels for use in planning documents and costing of data cleaning and the benefits of reusing cleaned data.

Move Beyond Software Engineering to Data Engineering

Thirdly, we need to improve our mental model of the separation of data science from applied science. A common trap in our thinking around data is to see data science (and data engineering, data preparation) as a sub-set of the software engineer’s or applied scientist’s skill set. As a result, we recruit and deploy the wrong type of resource. Data preparation and question formulation is superficially similar to both because of the need for programming skills, but the day to day problems faced are very different.

Data Science as Debugging 

One challenge for existing information technology professionals is realizing the extent to which a software ecosystem based on data differs from a classical ecosystem. In particular, by ingesting data we bring unknowns/uncontrollables into our decision-making system. This presents opportunity for adversarial exploitation and unforeseen operation.

blog post on Data Science as Debugging.

Starting with the analysis of a data set, the nature of data science is somewhat difference from classical software engineering.

One analogy I find helpful for understanding the depth of change we need is the following. Imagine as a software engineer, you find a USB stick on the ground. And for some reason you know that on that USB stick is a particular API call that will enable you to make a significant positive difference on a business problem. You don’t know which of the many library functions on the USB stick are the ones that will help. And it could be that some of those library functions will hinder, perhaps because they are just inappropriate or perhaps because they have been placed there maliciously. The most secure thing to do would be to not introduce this code into your production system at all. But what if your manager told you to do so, how would you go about incorporating this code base?

The answer is very carefully. You would have to engage in a process more akin to debugging than regular software engineering. As you understood the code base, for your work to be reproducible, you should be documenting it, not just what you discovered, but how you discovered it. In the end, you typically find a single API call that is the one that most benefits your system. But more thought has been placed into this line of code than any line of code you have written before.

An enormous amount of debugging would be required. As the nature of the code base is understood, software tests to verify it also need to be constructed. At the end of all your work, the lines of software you write to actually interact with the software on the USB stick are likely to be minimal. But more thought would be put into those lines than perhaps any other lines of code in the system.

Even then, when your API code is introduced into your production system, it needs to be deployed in an environment that monitors it. We cannot rely on an individual’s decision making to ensure the quality of all our systems. We need to create an environment that includes quality controls, checks and bounds, tests, all designed to ensure that assumptions made about this foreign code base are remaining valid.

This situation is akin to what we are doing when we incorporate data in our production systems. When we are consuming data from others, we cannot assume that it has been produced in alignment with our goals for our own systems. Worst case, it may have been adversarially produced. A further challenge is that data is dynamic. So, in effect, the code on the USB stick is evolving over time.

It might see that this process is easy to formalize now, we simply need to check what the formal software engineering process is for debugging, because that is the current software engineering activity that data science is closest to. But when we look for a formalization of debugging, we find that there is none. Indeed, modern software engineering mainly focusses on ensuring that code is written without bugs in the first place.

Recommendation: Anecdotally, resolving a machine learning challenge requires 80% of the resource to be focused on the data and perhaps 20% to be focused on the model. But many companies are too keen to employ machine learning engineers who focus on the models, not the data. We should change our hiring priorities and training. Universities cannot provide the understanding of how to data-wrangle. Companies must fill this gap.

Recommendation: We need to share best practice around data deployment across our teams. We should make best use of our processes where applicable, but we need to develop them to become data first organizations. Data needs to be cleaned at output not at input.

Deployment 

Much of the academic machine learning systems point of view is based on a software systems point of view that is around 20 years out of date. In particular we build machine learning models on fixed training data sets, and we test them on stationary test data sets.

In practice modern software systems involve continuous deployment of models into an ever-evolving world of data. These changes are indicated in the software world by greater availability of technologies like streaming technologies.

Continuous Deployment

Once the decomposition is understood, the data is sourced and the models are created, the model code needs to be deployed.

To extend the USB stick analogy further, how would as software engineer deploy the code if they thought that the code might evolve in production? This is what data does. We cannot assume that the conditions under which we trained our model will be retained as we move forward, indeed the only constant we have is change.

This means that when any data dependent model is deployed into production, it requires continuous monitoring to ensure the assumptions of design have not been invalidated. Software changes are qualified through testing, in particular a regression test ensures that existing functionality is not broken by change. Since data is continually evolving, machine learning systems require ‘continual regression testing’: oversight by systems that ensure their existing functionality has not been broken as the world evolves around them. An approach we refer to as progression testing. Unfortunately, standards around ML model deployment yet been developed. The modern world of continuous deployment does rely on testing, but it does not recognize the continuous evolution of the world around us.

Progression tests are likely to be statistical tests in contrast to classical software tests. The tests should be monitoring model performance and quality measures. They could also monitor conformance to standardized fairness measures.

If the world has changed around our decision-making ecosystem, how are we alerted to those changes?

Recommendation: We establish best practice around model deployment. We need to shift our culture from standing up a software service, to standing up a data as a service. Data as a Service would involve continual monitoring of our deployed models in production. This would be regulated by ‘hypervisor’ systems3 that understand the context in which models are deployed and recognize when circumstances have changed, and models need retraining or restructuring.

Data Oriented Architectures 

In a streaming architecture we shift from management of services, to management of data streams. Instead of worrying about availability of the services we shift to worrying about the quality of the data those services are producing.

Historically we’ve been software first, this is a necessary but insufficient condition for data first. We need to move from software-as-a-service to data-as-a-service, from service oriented architectures to data oriented architectures.

Streaming System

Characteristics of a streaming system include a move from pull updates to push updates, i.e. the computation is driven by a change in the input data rather than the service calling for input data when it decides to run a computation. Streaming systems operate on ‘rows’ of the data rather than ‘columns’. This is because the full column isn’t normally available as it changes over time. As an important design principle, the services themselves are stateless, they take their state from the streaming ecosystem. This ensures the inputs and outputs of given computations are easy to declare. As a result, persistence of the data is also handled by the streaming ecosystem and decisions around data retention or recomputation can be taken at the systems level rather than the component level.

Recommendation: We should consider a major re-architecting of systems around our services. In particular we should scope the use of a streaming architecture (such as Apache Kafka) that ensures data persistence and enables asynchronous operation of our systems.4 This would enable the provision of QC streams, and real time dash boards as well as hypervisors.

Importantly a streaming architecture implies the services we build are stateless, internal state is deployed on streams alongside external state. This allows for rapid assessment of other services’ data.

Apache Flink is a stream processing framework. Flink is a foundation for event driven processing. This gives a high throughput and low latency framework that operates on dataflows.

Data storage is handled by other systems such as Apache Kafka or AWS Kinesis.

stream.join(otherStream)
.where(<KeySelector>)
.equalTo(<KeySelector>)
.window(<WindowAssigner>)
.apply(<JoinFunction>)

Apache Flink allows operations on streams. For example, the join operation above. In a traditional data base management system, this join operation may be written in SQL and called on demand. In a streaming ecosystem, computations occur as and when the streams update.

The join is handled by the ecosystem surrounding the business logic.

Milan 

Milan is a data-oriented programming language and runtime infrastructure.

https://github.com/amzn/milan

The Milan language is a DSL embedded in Scala. The output is an intermediate language that can be compiled to run on different target platforms. Currently there exists a single compiler that produces Flink applications.

The Milan runtime infrastructure compiles and runs Milan applications on a Flink cluster.

As a simple example we’ll consider a high frequency trading system. Anne wishes to build a share trading system. She has access to a high frequency trading system which provides prices and allows trades at millisecond intervals. She wishes to build an automated trading system.

Let’s assume that price trading data is available as a data stream. But the price now is not the only information that Anne needs, she needs an estimate of the price in the future.

import pandas as pd
import numpy as np
import os
# Generate an artificial trading stream
days=pd.date_range(start='21/5/2017', end='21/05/2020')
z = np.random.randn(len(days), 1)
x = z.cumsum()+400
prices = pd.Series(x, index=days)
hypothetical = prices.loc['21/5/2019':]
real = prices.copy()
real['21/5/2019':] = np.NaN

Hypothetical Streams

We’ll call the future price a hypothetical stream.

A hypothetical stream is a desired stream of information which cannot be directly accessed. The lack of direct access may be because the events happen in the future, or there may be some latency between the event and the availability of the data.

Any hypothetical stream will only be provided as a prediction, ideally with an error bar.

The nature of the hypothetical Anne needs is dependent on her decision-making process. In Anne’s case it will depend over what period she is expecting her returns. In MDOP Anne specifies a hypothetical that is derived from the pricing stream.

It is not the price stream directly, but Anne looks for future predictions from the price stream, perhaps for price in T days’ time.

At this stage, this stream is merely typed as a hypothetical.

There are constraints on the hypothetical, they include: the input information, the upper limit of latency between input and prediction, and the decision Anne needs to make (how far ahead, what her upside, downside risks are). These three constraints mean that we can only recover an approximation to the hypothetical.

What is the advantage to defining things in this way? By defining, clearly, the two streams as real and hypothetical variants of each other, we now enable automation of the deployment and any redeployment process. The hypothetical can be instantiated against the real, and design criteria can be constantly evaluated triggering retraining when necessary.

Let’s consider a ride sharing app, for example the SafeBoda system.

Anne is on her way home now; she wishes to hail a car using a ride sharing app.

The app is designed in the following way. On opening her app Anne is notified about drivers in the nearby neighborhood. She is given an estimate of the time a ride may take to come.

Given this information about driver availability, Anne may feel encouraged to enter a destination. Given this destination, a price estimate can be given. This price is conditioned on other riders that may wish to go in the same direction, but the price estimate needs to be made before the user agrees to the ride.

Business customer service constraints dictate that this price may not change after Anne’s order is confirmed.

In this simple system, several decisions are being made, each of them on the basis of a hypothetical.

When Anne calls for a ride, she is provided with an estimate based on the expected time a ride can be with her. But this estimate is made without knowing where Anne wants to go. There are constraints on drivers imposed by regional boundaries, reaching the end of their shift, or their current passengers mean that this estimate can only be a best guess.

This best guess may well be driven by previous data.

Ride Sharing: Service Oriented to Data Oriented 

The modern approach to software systems design is known as a service-oriented architectures (SOA). The idea is that software engineers are responsible for the availability and reliability of the API that accesses the service they own. Quality of service is maintained by rigorous standards around testing of software systems.

In data driven decision-making systems, the quality of decision-making is determined by the quality of the data. We need to extend the notion of service-oriented architecture to data-oriented architecture (DOA).

The focus in SOA is eliminating hard failures. Hard failures can occur due to bugs or systems overload. This notion needs to be extended in ML systems to capture soft failures associated with declining data quality, incorrect modeling assumptions and inappropriate re-deployments of models. We need to focus on data quality assessments. In data-oriented architectures engineering teams are responsible for the quality of their output data streams in addition to the availability of the service they support (Lawrence 2017). Quality here is not just accuracy, but fairness and explainability. This important cultural change would be capable of addressing both the challenge of technical debt (Sculley et al. 2015) and the social responsibility of ML systems.

Software development proceeds with a test-oriented culture. One where tests are written before software, and software is not incorporated in the wider system until all tests pass. We must apply the same standards of care to our ML systems, although for ML we need statistical tests for quality, fairness and consistency within the environment. Fortunately, the main burden of this testing need not fall to the engineers themselves: through leveraging classical statistics and emulation we will automate the creation and redeployment of these tests across the software ecosystem, we call this ML hypervision (WP5 ).

Modern AI can be based on ML models with many millions of parameters, trained on very large data sets. In ML, strong emphasis is placed on predictive accuracy whereas sister-fields such as statistics have a strong emphasis on interpretability. ML models are said to be ‘black boxes’ which make decisions that are not explainable.5

For the ride sharing system, we start to see a common issue with a more complex algorithmic decision-making system. Several decisions are being made multilple times. Let’s look at the decisions we need along with some design criteria.

1. Driver Availability: Estimate time to arrival for Anne’s ride using Anne’s location and local available car locations. Latency 50 milliseconds
2. Cost Estimate: Estimate cost for journey using Anne’s destination, location and local available car current destinations and availability. Latency 50 milliseconds
3. Driver Allocation: Allocate car to minimize transport cost to destination. Latency 2 seconds.

So we need:

1. a hypothetical to estimate availability. It is constrained by lacking destination information and a low latency requirement.
2. a hypothetical to estimate cost. It is constrained by low latency requirement and

Simultaneously, drivers in this data ecosystem have an app which notifies them about new jobs and recommends them where to go.

Further advantages. Strategies for data retention (when to snapshot) can be set globally.

A few decisions need to be made in this system. First of all, when the user opens the app, the estimate of the time to the nearest ride may need to be computed quickly, to avoid latency in the service.

This may require a quick estimate of the ride availability.

Information Dynamics 

With all the second guessing within a complex automated decision-making system, there are potential problems with information dynamics, the ‘closed loop’ problem, where the sub-systems are being approximated (second guessing) and predictions downstream are being affected.

This leads to the need for a closed loop analysis, for example, see the “Closed Loop Data Science” project led by Rod Murray-Smith at Glasgow.

Conclusion 

We operate in a technologically evolving environment. Machine learning is becoming a key coponent in our decision-making capabilities, our intelligence and strategic command. However, technology drove changes in battlefield strategy. From the stalemate of the first world war to the tank-dominated Blitzkrieg of the second, to the asymmetric warfare of the present. Our technology, tactics and strategies are also constantly evolving. Machine learning is part of that evolution solution, but the main challenge is not to become so fixated on the tactics of today that we miss the evolution of strategy that the technology is suggesting.

Data oriented programming offers a set of development methodologies which ensure that the system designer considers what decisions are required, how they will be made, and critically, declares this within the system architecture.

This allows for monitoring of data quality, fairness, model accuracy and opens the door to Auto AI: a more sophisticated form of auto ML where full redployments of models are considered while analyzing the information dynamics of a complex automated decision-making system.

Auto AI 

Supervised machine learning models are data-driven statistical functional estimators. Each ML model is trained to perform a task. Machine learning systems are created when these models are integrated as interacting components in a more complex system that carries out a larger scale task, e.g. an autonomous drone delivery system.

Artificial Intelligence can also be seen as algorithmic decision-making. ML systems are data driven algorithmic decision-makers. Designing decision-making engines requires us to firstly decompose the system into its component parts. The decompositions are driven by (1) system performance requirements (2) the suite of ML algorithms at our disposal (3) the data availability. Performance requirements could be computational speed, accuracy, interpretability, and ‘fairness’. The current generation of ML Systems is often based around supervised learning and human annotated data. But in the future, we may expect more use of reinforcement learning and automated knowledge discovery using unsupervised learning.

The classical systems approach assumes decomposability of components. In ML, upstream components (e.g. a pedestrian detector in an autonomous vehicle) make decisions that require revisiting once a fuller picture is realized at a downstream stage (e.g. vehicle path planning). The relative weaknesses and strengths of the different component parts need to be assessed when resolving conflicts.

In long-term planning, e.g. logistics and supply chain, a plan may be computed multiple times under different constraints as data evolves. In logistics, an initial plan for delivery may be computed when an item is viewed on a webpage. Webpage waiting-time constraints dominate the solution we choose. However, when an order is placed the time constraint may be relaxed and an accuracy constraint or a cost constraint may now dominate.

Such sub-systems will make inconsistent decisions, but we should monitor and control the extent of the inconsistency.

One solution to aid with both the lack of decomposability of the components and the inconsistency between components is end-to-end learning of the system. End-to-end learning is when we use ML techniques to fit parameters across the entire decision pipeline. We exploit gradient descent and automated differentiation software to achieve this. However, components in the system may themselves be running a simulation (e.g. a transport delivery-time simulation) or optimization (e.g. a linear program) as a subroutine. This limits the universality of automatic differentiation. Another alternative is to replace the entire system with a single ML model, such as in Deep Reinforcement Learning. However, this can severely limit the interpretability of the resulting system.

We envisage AutoAI as allowing us to take advantage of end-to-end learning without sacrificing the interpretability of the underlying system. Instead of optimizing each component individually, we introduce Bayesian system optimization (BSO). We will make use of the end-to-end learning signals and attribute them to the system sub-components through the construction of an interconnected network of surrogate models, known as emulators, each of which is associated with an individual component from the underlying ML-system. Instead of optimizing each component individually (e.g. by classical Bayesian optimization) in BSO we account for upstream and downstream interactions in the optimization, leveraging our end-to-end knowledge without damaging the interpretability of the underlying system.

References

Andrade-Pacheco, Ricardo, Martin Mubangizi, John Quinn, and Neil D. Lawrence. 2014. “Consistent Mapping of Government Malaria Records Across a Changing Territory Delimitation.” Malaria Journal 13 (Suppl 1). https://doi.org/10.1186/1475-2875-13-S1-P5.

Cooper, Brian. 1991. Transformation of a Valley: Derbyshire Derwent. Scarthin Books.

Feurer, Matthias, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, and Frank Hutter. n.d. “Efficient and Robust Automated Machine Learning.” In Advances in Neural Information Processing Systems.

Gething, Peter W., Abdisalan M. Noor, Priscilla W. Gikandi, Esther A. A. Ogara, Simon I. Hay, Mark S. Nixon, Robert W. Snow, and Peter M. Atkinson. 2006. “Improving Imperfect Data from Health Management Information Systems in Africa Using Space–Time Geostatistics.” PLoS Medicine 3 (6). Public Library of Science. https://doi.org/10.1371/journal.pmed.0030271.

Lawrence, Neil D. 2015. “How Africa Can Benefit from the Data Revolution.” The Guardian Media & Tech Network. https://www.theguardian.com/media-network/2015/aug/25/africa-benefit-data-science-information.

———. 2017. “Data Readiness Levels.” arXiv.

Mubangizi, Martin, Ricardo Andrade-Pacheco, Michael Thomas Smith, John Quinn, and Neil D. Lawrence. 2014. “Malaria Surveillance with Multiple Data Sources Using Gaussian Process Models.” In 1st International Conference on the Use of Mobile ICT in Africa.

Popper, Karl R. 1963. Conjectures and Refutations: The Growth of Scientific Knowledge. London: Routledge.

Sculley, D., Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, and Dan Dennison. 2015. “Hidden Technical Debt in Machine Learning Systems.” In Advances in Neural Information Processing Systems 28, edited by Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett, 2503–11. Curran Associates, Inc. http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf.

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1. We can also become constrained by our tribal thinking, just as each of the other groups can.

2. This is related to challenges of machine learning and technical debt (Sculley et al. 2015), although we are trying to frame the solution here rather than the problem.

3. Emulation, or surrogate modelling, is one very promising approach to forming such a hypervisor. Emulators are models we fit to other models, often simulations, but the could also be other machine learning models. These models operate at the meta-level, not on the systems directly. This means they can be used to model how the sub-systems interact. As well as emulators we should consider real time dash boards, anomaly detection, mutlivariate analysis, data visualization and classical statistical approaches for hypervision of our deployed systems.

4. These approaches are one area of focus for my own team’s research. A data first architecture is a prerequisite for efficient deployment of machine learning systems.

5. See for example “The Dark Secret at the Heart of AI” in Technology Review.