From Innovation to Deployment


at Data Science Africa, Ashesi University on Oct 24, 2019 [reveal]
Neil D. Lawrence, University of Cambridge


In this talk we introduce a five year project funded by the UK’s Turing Institute to shift the focus from developing AI systems to deploying AI systems that are safe and reliable. The AI systems we are developing and deploying are based on interconnected machine learning components. There is a need for AI-assisted design and monitoring of these systems to ensure they perform robustly, safely and accurately in their deployed environment. We address the entire pipeline of AI system development, from data acquisition to decision making. Data Oriented Architectures are an ecosystem that includes system monitoring for performance, interpretability and fairness. The will enable us to move from individual component optimisation to full system monitoring and optimisation.

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Artificial Intelligence (AI) solutions are based on machine learning algorithms (ML), but each ML solution is only capable of solving a restricted task, e.g. a supervised learning problem. Consequently, any AI that we deploy today takes the form of an ML System with interacting components. As these ML systems become larger and more complex, challenges in interpretation, explanation, accuracy and fairness arise. This project addresses these issues. The challenges include (Lawrence 2019): the decomposition of the system, the data availability, and the performance of the system in deployment. Collectively we refer to these challenges as the “Three Ds of ML Systems Design”.

Turing AI Fellowship [edit]

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.


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.

Motivating Examples

SafeBoda [edit]

Figure: SafeBoda is a ride allocation system for Boda Boda drivers. Let’s imagine the capabilities we need for such an AI system.

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.

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!


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!

Figure: Some software components in a ride allocation system. Circled components are hypothetical, rectangles represent actual data.

The Promise of AI [edit]

The promise of the fourth industrial revolution is that this wave of automation is the first wave of automation where the machines adapt to serve us rather than us adapting to serve the machine.

That promise will remain unfulfilled with our current approach to systems design.

This proposal is about addressing that gap, but to first understand the gap, let’s look at comparisons between the approach we take to systems design, and the way that natural systems evolve.

Artificial vs Natural Systems [edit]

Let’s take a step back from artificial intelligence, and consider natural intelligence. Or even more generally, let’s consider the contrast between an artificial system and an natural system. The key difference between the two is that artificial systems are designed whereas natural systems are evolved.

Systems design is a major component of all Engineering disciplines. The details differ, but there is a single common theme: achieve your objective with the minimal use of resources to do the job. That provides efficiency. The engineering designer imagines a solution that requires the minimal set of components to achieve the result. A water pump has one route through the pump. That minimises the number of components needed. Redundancy is introduced only in safety critical systems, such as aircraft control systems. Students of biology, however, will be aware that in nature system-redundancy is everywhere. Redundancy leads to robustness. For an organism to survive in an evolving environment it must first be robust, then it can consider how to be efficient. Indeed, organisms that evolve to be too efficient at a particular task, like those that occupy a niche environment, are particularly vulnerable to extinction.

This notion is akin to the idea that only the best will survive, popularly encoded into an notion of evolution by Herbert Spencer’s quote.

Survival of the fittest

Herbet Spencer, 1864

Darwin himself never said “Survival of the Fittest” he talked about evolution by natural selection.

Non-survival of the non-fit

Evolution is better described as “non-survival of the non-fit”. You don’t have to be the fittest to survive, you just need to avoid the pitfalls of life. This is the first priority.

So it is with natural vs artificial intelligences. Any natural intelligence that was not robust to changes in its external environment would not survive, and therefore not reproduce. In contrast the artificial intelligences we produce are designed to be efficient at one specific task: control, computation, playing chess. They are fragile.

The first rule of a natural system is not be intelligent, it is “don’t be stupid”.

A mistake we make in the design of our systems is to equate fitness with the objective function, and to assume it is known and static. In practice, a real environment would have an evolving fitness function which would be unknown at any given time.

You can also read this blog post on Natural and Artificial Intelligence..

The first criterion of a natural intelligence is don’t fail, not because it has a will or intent of its own, but because if it had failed it wouldn’t have stood the test of time. It would no longer exist. In contrast, the mantra for artificial systems is to be more efficient. Our artificial systems are often given a single objective (in machine learning it is encoded in a mathematical function) and they aim to achieve that objective efficiently. These are different characteristics. Even if we wanted to incorporate don’t fail in some form, it is difficult to design for. To design for “don’t fail”, you have to consider every which way in which things can go wrong, if you miss one you fail. These cases are sometimes called corner cases. But in a real, uncontrolled environment, almost everything is a corner. It is difficult to imagine everything that can happen. This is why most of our automated systems operate in controlled environments, for example in a factory, or on a set of rails. Deploying automated systems in an uncontrolled environment requires a different approach to systems design. One that accounts for uncertainty in the environment and is robust to unforeseen circumstances.

Currently, our main approach to systems design involves designing a system in a component-wise manner. Attempts to replicate the capabilities of evolved systems through specifying the objective, rather than evolving behaviour.

Technical Consequence [edit]

Classical systems design assumes that the system is decomposable. That we can decompose the complex decision making process into distinct and independently designable parts. The composition of these parts gives us our final system.

Nicolas Negroponte, the original founder of MIT’s media lab used to write a column called ‘bits and atoms’. This referred to the ability of information to effect movement of goods in the physical world. It is this interaction where machine learning technologies have the possibility to bring most benefit.

Computer Science Paradigm Shift

The next wave of machine learning is a paradigm shift in the way we think about computer science.

Classical computer science assumes that ‘data’ and ‘code’ are separate, and this is the foundation of secure computer systems. In machine learning, ‘data’ is ‘software’, so the decision making is directly influenced by the data. We are short-circuiting a fundamental assumption of computer science, we are breeching the code/data separation.

This means we need to revisit many of our assumptions and tooling around the machine learning process. In particular, we need new approaches to systems design, new approaches to programming languages that highlight the importance of data, and new approaches to systems security.

This gives vulnerabilities that we are exposing to the natural environment. Many security problems that we face today are the result of bugs that mean that code and data are not separate in thee systems we deploy, imagine what will happen when we deploy systems that purposefully short-circuit this protection into uncontrolled environments.

Peppercorns [edit]

Figure: A peppercorn is a system design failure which is not a bug, but a conformance to design specification that causes problems when the system is deployed in the real world with mischevious and adversarial actors.

Asking Siri “What is a trillion to the power of a thousand minus one?” leads to a 30 minute response1 consisting of only 9s. I found this out because my nine year old grabbed my phone and did it. The only way to stop Siri was to force closure. This is an interesting example of a system feature that’s not a bug, in fact it requires clever processing from Wolfram Alpha. But it’s an unexpected result from the system performing correctly.

This challenge of facing a circumstance that was unenvisaged in design but has consequences in deployment becomes far larger when the environment is uncontrolled. Or in the extreme case, where actions of the intelligent system effect the wider environment and change it.

These unforseen circumstances are likely to lead to need for much more efficient turn-around and update for our intelligent systems. Whether we are correcting for security flaws (which are bugs) or unenvisaged circumstantial challenges: an issue I’m referring to as peppercorns. Rapid deployment of system updates is required. For example, Apple have “fixed” the problem of Siri returning long numbers.

The challenge is particularly acute because of the scale at which we can deploy AI solutions. This means when something does go wrong, it may be going wrong in billions of households simultaneously.

You can also check this blog post on Decision Making and Diversity. and this blog post on Natural vs Artifical Intelligence..

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.

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.

In this talk, we will focus on the third challenge, the deployment challenge.

Deployment [edit]

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.

I normally use an analogy to describe data science to software engineers. Imagine, as a software engineer you are given a USB stick of unknown provenance with a software library on it. You are told to integrate that code into your system. All good software engineers would refuse to do this. But if they were forced to do it, they would do so very carefully.

This is the role of the data scientist, incorporating data into the system is equivalent to incorporating software of some unknown provenance.

You can also check my blog post on Data Science as Debugging.

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’ systems2 that understand the context in which models are deployed and recognize when circumstances have changed, and models need retraining or restructuring.

Data Oriented Architectures [edit]

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.3 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.


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 [edit]

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

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.

Trading System

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.

Figure: Anne has access to the share prices in the black stream but not in the blue stream. A hypothetical stream is the stream of future prices. Anne can define this hypothetical under constraints (latency, input etc). The need for a model is now exposed in the software infrastructure

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.

Hypothetical Advantage

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 [edit]

Figure: Service oriented architecture. The data access is buried in the cost allocation service. Data dependencies of the service cannot be found without trawling through the underlying code base.

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.

Figure: Data oriented architecture. Now the joins and the updates are exposed within the streaming ecosystem. We can programatically determine the factor graph which gives the thread through the model.

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.4

Figure: Data-oriented programing. There is a requirement for an estimate of the driver allocation to give a rough cost estimate before the user has confirmed the ride. In data-oriented programming, this is achieved through declaring a hypothetical stream which approximates the true driver allocation, but with restricted input information and constraints on the computational latency.

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 [edit]

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.

Emulation [edit]

Figure: Real world systems consiste of simulators, that capture our domain knowledge about how our systems operate. Different simulators run at different speeds and granularities.

In many real world systems, decisions are made through simulating the environment. Simulations may operate at different granularities. For example, simulations are used in weather forecasts and climate forecasts. The UK Met office uses the same code for both, but operates climate simulations one at greater spatial and temporal resolutions.

Figure: A statistical emulator is a system that reconstructs the simulation with a statistical model.

A statistical emulator is a data-driven model that learns about the underlying simulation. Importantly, learns with uncertainty, so it ‘knows what it doesn’t know’. In practice, we can call the emulator in place of the simulator. If the emulator ‘doesn’t know’, it can call the simulator for the answer.

Figure: A statistical emulator is a system that reconstructs the simulation with a statistical model. As well as reconstructing the simulation, a statistical emulator can be used to correlate with the real world.

Figure: In modern machine learning system design, the emulator may also consider the output of ML models (for monitoring bias or accuracy) and Operations Research models..

As well as reconstructing an individual simulator, the emulator can calibrate the simulation to the real world, by monitoring differences between the simulator and real data. This allows the emulator to characterise where the simulation can be relied on, i.e. we can validate the simulator.

Similarly, the emulator can adjudicate between simulations. This is known as multi-fidelity emulation. The emulator characterizes which emulations perform well where.

If all this modelling is done with judiscious handling of the uncertainty, the computational doubt, then the emulator can assist in desciding what experiment should be run next to aid a decision: should we run a simulator, in which case which one, or should we attempt to acquire data from a real world intervention.

Deep Emulation [edit]

Figure: A potential path of models in a machine learning system.

As a solution we can use of emulators. When constructing an ML system, software engineers, ML engineers, economists and operations researchers are explicitly defining relationships between variables of interest in the system. That implicitly defines a joint distribution, $p(\dataVector^*, \dataVector)$. In a decomposable system any sub-component may be defined as $p(\dataVector_\mathbf{i}|\dataVector_\mathbf{j})$ where $\dataVector_\mathbf{i}$ and $\dataVector_\mathbf{j}$ represent sub-sets of the full set of variables $\left\{\dataVector^*, \dataVector \right\}$. In those cases where the relationship is deterministic, the probability density would collapse to a vector-valued deterministic function, $\mappingFunctionVector_\mathbf{i}\left(\dataVector_\mathbf{j}\right)$.

Inter-variable relationships could be defined by, for example a neural network (machine learning), an integer program (operational research), or a simulation (supply chain). This makes probabilistic inference in this joint density for real world systems is either very hard or impossible.

Emulation is a form of meta-modelling: we construct a model of the model. We can define the joint density of an emulator as $s(\dataVector*, \dataVector)$, but if this probability density is to be an accurate representation of our system, it is likely to be prohibitively complex. Current practice is to design an emulator to deal with a specific question. This is done by fitting an ML model to a simulation from the the appropriate conditional distribution, $p(\dataVector_\mathbf{i}|\dataVector_\mathbf{j})$, which is intractable. The emulator provides an approximated answer of the form $s(\dataVector_\mathbf{i}|\dataVector_\mathbf{j})$. Critically, an emulator should incorporate its uncertainty about its approximation. So the emulator answer will be less certain than direct access to the conditional $p(\dataVector_i|\dataVector_j)$, but it may be sufficiently confident to act upon. Careful design of emulators to answer a given question leads to efficient diagnostics and understanding of the system. But in a complex interacting system an exponentially increasing number of questions can be asked. This calls for a system of automated construction of emulators which selects the right structure and redeploys the emulator as necessary. Rapid redeployment of emulators could exploit pre-existing emulators through transfer learning.

Automatically deploying these families of emulators for full system understanding is highly ambitious. It requires advances in engineering infrastructure, emulation and Bayesian optimization. However, the intermediate steps of developing this architecture also allow for automated monitoring of system accuracy and fairness. This facilitates AutoML on a component-wise basis which we can see as a simple implementation of AutoAI. The proposal is structured so that despite its technical ambition there is a smooth ramp of benefits to be derived across the programme of work.

In Applied Mathematics, the field studying these techniques is known as uncertainty quantification. The new challenge is the automation of emulator creation on demand to answer questions of interest and facilitate the system design, i.e. AutoAI through BSO.

At design stage, any particular AI task could be decomposed in multiple ways. Bayesian system optimization will assist both in determining the large-scale system design through exploring different decompositions and in refinement of the deployed system.

So far, most work on emulators has focussed on emulating a single component. Automated deployment and maintenance of ML systems requires networks of emulators that can be deployed and redeployed on demand depending on the particular question of interest. Therefore, the technical innovations we require are in the mathematical composition of emulator models (Damianou and Lawrence 2013; Perdikaris et al. 2017). Different chains of emulators will need to be rapidly composed to make predictions of downstream performance. This requires rapid retraining of emulators and propagation of uncertainty through the emulation pipeline a process we call deep emulation.

Recomposing the ML system requires structural learning of the network. By parameterizing covariance functions appropriately this can be done through Gaussian processes (e.g. (Damianou et al., n.d.)), but one could also consider Bayesian neural networks and other generative models, e.g. Generative Adversarial Networks (Goodfellow et al. 2014).

Figure: A potential path of models in a machine learning system.

Figure: A potential path of models in a machine learning system.

Bayesian System Optimization [edit]

We introduce the notion of Bayesian system optimisation. Standard Bayesian optimisation is about optimising individual components under a given (localised) optimisation criterion. Bayesian system optimisation is about realising that there are upstream and downstream effects, ‘no model is an island’. If we can use emulation to estimate those effects, then we can optimise individual components not just according to their own objective functions, but according to their situation in the wider system and their downstream effects.

Auto AI [edit]

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.

Conclusion [edit]

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.


Damianou, Andreas, Carl Henrik Ek, Michalis K. Titsias, and Neil D. Lawrence. n.d. “Manifold Relevance Determination.” In.

Damianou, Andreas, and Neil D. Lawrence. 2013. “Deep Gaussian Processes.” In, 31:207–15.

Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative Adversarial Nets.” In Advances in Neural Information Processing Systems 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 2672–80. Curran Associates, Inc.

Lawrence, Neil D. 2017. “Data Readiness Levels.” arXiv.

———. 2019. “Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design.” arXiv.

Perdikaris, Paris, Maziar Raissi, Andreas Damianou, Neil D. Lawrence, and George Em Karnidakis. 2017. “Nonlinear Information Fusion Algorithms for Data-Efficient Multi-Fidelity Modelling.” Proc. R. Soc. A 473 (20160751).

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.

  1. Apple has fixed this issue so that Siri no longer does this.

  2. 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.

  3. 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.

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