Agile Deep Learning

Agile Deep LearningDavid Murgatroyd (@dmurga)

@dmurga

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)

Explore & Experiment Analyze Prioritize Productize

Agile Deep Learning

@dmurga

If a typical person can do a mental task with less than one second of thought, we can

probably automate it using AI either now or in the near future.

- Andrew Ng, HBR Nov 2016

Identifying a Problem: Perception

@dmurga

For any concrete, repeated event that we observe, we can reasonably try to predict the

outcome of the next such event. - Andrew Ng, NIPS 2016

Identifying a Problem: Prediction

@dmurga

If a desire for content is shared by many individuals but should be met in ways specific to each of them, we can probably automate

satisfying those desires with AI.- (yours truly, today :-)

Identifying a Problem: Personalization

@dmurga

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)


Agile Deep Learning

@dmurga

Need both:Offline: for quick experimentation -- model training and quality analysisOnline: for monitoring alignment with business goals

Identifying Metrics

@dmurga

Art of identifying some part of signal to try to predict it.

Example ways to identify signal:‣ Create it directly: humans annotate the right output.‣ Usage of feature: predict historical usage from data prior to it.‣ Usage of other features: identify other ways users satisfied need.‣ Outside product: predict related data in the same domain.

Identifying Metrics: Offline

@dmurga

Usage metrics:‣ How many users?‣ How much do they use it?‣ How often do they use it?‣ How do they use it?

Explicit feedback metrics:‣ Thumbs up or down, etc.

Identifying Metrics: Online

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)


Agile Deep Learning

@dmurga

Concrete data is the way deep learning products are specified. Uses‣ Truth: start with manual generation, especially by product

manager.‣ Fodder: not the ultimate output, but valuable for training

subcomponents‣ Baseline: output of simplest solution you can think of,

worst case random.

Watch out for bias against under-represented subpopulations!

Identifying Data

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)


Agile Deep Learning

@dmurga

Identifying Model(s)

Quantity of data?

Structure of data?

1D Discrete

Categorical

2D+ Continuous

1D Continuous

R

U

L

E

S SVM

CRF

CNN

RNN (BiLSTM)

GAN ...ever deeper with richer attention

FF

Deep RL

@dmurga

Seed with pre-trained models from similar tasks.

Consider other properties of model’s output:‣ interpretable‣ confidence scores‣ time / space performance

Identifying Model(s)

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)


Agile Deep Learning

@dmurga

Explore

@dmurga

Experiment

@dmurga

Experiment

Gradually increase:‣ Amount of data used to train / dev‣ Amount of data used to test‣ Complexity of model

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)


Agile Deep Learning

@dmurga

Analyze: Bugs v. Errors

‣ Error: incorrect output from a model despite the model being correctly implemented. Egregious examples are “howlers” or “WTFs”.

‣ Bug: implementation does something other than what was intended

This distinction is useful for managing expectations about cost of addressing.

Bug Error

@dmurga

Analyze: Isolate functional tests

Options:

Black-box style: ensure “can’t be wrong” (“earmark”) input/output pairs. Might lead to spurious test failures.

@dmurga

Analyze: Isolate functional tests

Options:

Black-box style: ensure “can’t be wrong” (“earmark”) input/output pairs. Might lead to spurious test failures.

Clear-box style: use a mock implementation of the model that produces expected answers.

@dmurga

Analyze: Automate all tests

Deep Learning’s dependence on data means changing anything changes everything.

Look at aggregate results across data sets to gauge importance.

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)


Agile Deep Learning

@dmurga

Prioritize

@dmurga

High training error?

More training epochs Bigger / new model

@dmurga

High development error? (train error OK)

@dmurga

High development error?

More data Regularize / new model

Dev errorDev error

@dmurga

High evaluation error? (train/dev OK)

Get more development data similar to test data so you can return to the

“High development error?” step.

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)


Agile Deep Learning

@dmurga

Kinds of overrides:‣ Always give this answer.‣ Never give this answer.

Beware of ‘whack a mole’.

Be sad when overrides are used.

Productize: Overrides

@dmurga

Productize: How and when to scale

Move from data parallelism to model parallelism as there’s first more data then more complex models.

Only scale rest of product when you’re sure what problem you’re solving.

@dmurga

Productize: Milestones

1. By hand examples2. Glued-together with some rules

(Prototype)3. Functions on some data (“Labs” /

Alpha)4. Measurable & inspectable (0.1% /

early Beta)5. Accurate, not slow, nice demo,

documented & configurable (1% / late Beta)

6. Simple & fast (100% / GA)7. Handle new domains (post-100%

/ post-GA)

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)


Agile Deep Learning: Early Iterations (Spikes)

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)


Agile Deep Learning: Middle Sprints

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)


Agile Deep Learning: Late Sprints

@dmurga

IdentifyProblem

Identify Metrics

IdentifyData

IdentifyModel(s)


Agile Deep Learning

Thanks! Questions?

David Murgatroyd (@dmurga)

Suggestions:

Pros and cons of different team organizations strategies?

What are the different roles in a Deep Learning oriented group?

Does Scrum or Kanban work better for Deep Learning?

What about “presentation bias” for measuring on historic data?

We’re hiring in Boston, NYC, and Stockholm!

Appendix

David Murgatroyd (@dmurga)

How does deep learning affect team organization?

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Machine Learning Expert

Encourages alignment with business goals.

Challenges machine learning collaboration, depth and reuse.

Best for products with many small, simpler models.

Option 1: integrated teams with cross-team groups (chapters!)

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Encourages machine learning collaboration, depth and reuse.

Challenges alignment with business goals.

Best for products with fewer large, complex model(s).

Option 2: independent machine learning team delivering models

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Just one kind of ML Expert?

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Carpenters

Blacksmiths

Miners51

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An Applied Machine Learning Engineer:● crafts specific (parts of) products ● by applying tools (e.g., libraries) ● to materials (e.g., data)

with an understanding of what sort of product is desired.

Carpenters

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A Machine Learning Toolist (Engineer/Scientist):● implements practical machine learning ideas into

industrial-strength tools (like a blacksmith firing metal into carpentry tools)

● understands the latest in ML theory and prototypes to see what’s practical (like a blacksmith smelting ore into metal)

Blacksmiths

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An Machine Learning Theoretician:● distills new material from nature to be made into tools● understands the fundamental characteristics of that material to inform its use

Miners

Carpenters

Blacksmiths

Miners55

CarpentersApplied ML Eng

BlacksmithsML Toolist

MinersML Theoretician

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Agile Deep Learning

Technology

Transcript of Agile Deep Learning