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Machine Learning and Data Science

Data First Design

Neil D. Lawrence

Department of Housing, Commmunities and Local Government

\[\text{data} + \text{model} \xrightarrow{\text{compute}} \text{prediction}\]

From Model to Decision

 \[\text{data} + \text{model} \xrightarrow{\text{compute}} \text{prediction}\]

Artificial Intelligence and Data Science

  • AI aims to equip computers with human capabilities
    • Image understanding
    • Computer vision
    • Speech recognition
    • Natural language understanding
    • Machine translation

Supervised Learning for AI

  • Dominant approach today:
    • Generate large labelled data set from humans.
    • Use supervised learning to emulate that data.
      • E.g. ImageNet Russakovsky et al. (2015)
  • Significant advances due to deep learning
    • E.g. Alexa, Amazon Go

Data Science

  • Arises from happenstance data.
  • Differs from statistics in that the question comes after data collection.

Amazon: Bits and Atoms

Supply Chain

Cromford

Deep Freeze

Deep Freeze

Machine Learning in Supply Chain

  • Supply chain: Large Automated Decision Making Network
  • Major Challenge:
    • We have a mechanistic understanding of supply chain.
    • Machine learning is a data driven technology.

The Tribal Mentality

  • \(\text{data} + \text{model}\) is not new.
    • Dates back to Newton, Laplace, Gauss
  • Plethora of fields: E.g.
    • Operations Research
    • Control
    • Econometrics
    • Statistics
    • Machine learning
    • Data science

The Tribal Mentality

  • This can lead to confusion:
    • Different academic fields are:
      • Born in different eras
      • Driven by different motivations
      • Arrive at different solutions

Tribalism Can be Good

  • Allows for consensus on best practice.
  • Shared set of goals
  • Ease of commiunication
  • Rapid deployment of robust solutions

Professional Tribes

  • This is the nature of professions
    • lawyers
    • medics
    • doctors
    • engineers
    • accountants

Different Views

\[\text{data} + \text{model}\]

  • For OR, control, stats etc.
  • More things unite us rather than divide us.

We’re no longer hunter gatherers …

  • The automation challenges we face require
    • all of our best ideas.
    • rethinking what \(\text{data}+\text{model}\) means
    • rapid deployment and continuous monitoring
  • This is the era of data science

Discomfort and Disconformation

  • Talking across field boundaries is critical.
  • It helps us disconfirm our beliefs.
  • It’s not comfortable, but it’s vital.

The Three Ds of Machine Learning Systems Design

  • Three primary challenges of Machine Learning Systems Design.
  1. Decomposition
  2. Data
  3. Deployment

The Three Ds of Machine Learning Systems Design

  • Three primary challenges of Machine Learning Systems Design.
  1. Decomposition
  2. Data
  3. Deployment

Data

  • Hard to overstate its importance.
  • Half the equation of \(\text{data} + \text{model}\).
  • Often utterly neglected.

Data Neglect

  • Arises for two reasons.
    1. Data cleaning is perceived as tedious.
    2. Data cleaning is complex.

Data Cleaning

  • Seems difficult to formulate into readily teachable princples.
  • Heavily neglected in data science, statistics and ML courses.
  • In practice most scientists spend around 80% of time data cleaning.

The Software Crisis

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 Data Crisis

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.

Me

Data Readiness Levels

https://arxiv.org/pdf/1705.02245.pdf Data Readiness Levels (Lawrence, 2017)

Three Grades of Data Readiness

  • Grade C - accessibility
    • Transition: data becomes electronically available
  • Grade B - validity
    • Transition: pose a question to the data.
  • Grade A - usability

Data Science as Debugging

80/20 in Data Science

  • Anecdotally for a given challenge
    • 80% of time is spent on data wrangling.
    • 20% of time spent on modelling.
  • Many companies employ ML Engineers focussing on models not data.

Outlook for Machine Learning

  • Risen to prominence to scale our activities.
  • To scale activities need more computer based automation.
  • Machine learning allows us to automate processes previously out of reach.

Data Oriented Architectures

  • Convert data to a first-class citizen.
  • View system as operations on data streams.
  • Expose data operations in a programmatic way.

Data Orientated Architectures

  • Historically we’ve been software first
    • A necessary but not sufficient condition for data first
  • Move from
    1. service orientated architectures
    2. data orientated architectures

Streaming System

  • Move from pull updates to push updates.
  • Operate on rows rather than columns.
  • Lead to stateless logic: persistence handled by system.
  • Example Apache Kafka + Apache Flink

Streaming Architectures

  • AWS Kinesis, Apache Kafka
  • Not just about streaming
    • Nodes in the architecture are stateless
    • They persist through storing state on streams
  • This brings the data inside out

Join

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

Milan

  • Data Oriented Programming Language and runtime.
  • DSL Embedded in Scala converts to an intermediate langugage.
  • Intermediate language for compilation on different platforms (currently Flink)

https://github.com/amzn/milan

Trading System

  • High frequency share trading.
  • Stream of prices with millisecond updates.
  • Trades required on millisecond time line

Real Price

Future Price

Hypothetical Streams

  • Real stream — share prices
    • derived hypothetical stream — share prices in future.
  • Hypothetical constrained by
    • input constraints.
    • decision functional
    • computational requirements (latency)

Hypothetical Advantage

  • Modelling is now required.
  • But modelling is declared in the ecosystem.
  • If it’s manual, warnings can be used
    • calibration, fairness, dataset shift
  • Opens door to Auto AI.

SafeBoda

SafeBoda

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.

Ride Allocation Prediction

Ride Sharing: Service Oriented

Ride Sharing: Data Oriented

Ride Sharing: Hypothetical

Information Dynamics

  • Potential for information feedback loops.
  • Hypothetical streams are instantiated.
  • Nature hypothesis (e.g. price prediction) can effect reality.
  • Leads to information dynamics, similar to dynamics of governors.
  • See e.g. Closed Loop Data Science at Glasgow.

Conclusion

  • Challenges in decomposition, data and model deployment for ML.
  • Data oriented architectures and data first thinking are the solution.
  • Data oriented programming creates systems that are ready to deploy.
  • Opens the door to AutoAI and information dynamics analysis.

Conclusion

  • Technologically evolving environment.
  • ML is a key component of decision-making.
  • Data is the key component of ML.
  • ML is critically dependent on data.
  • Challenges in design, data curation and model deployment

Thanks!

References

Lawrence, N.D., 2017. Data readiness levels. arXiv.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L., 2015. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115, 211–252. https://doi.org/10.1007/s11263-015-0816-y