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Monitoring AI with AI
Stepan Pushkarev
CTO of Hydrosphere.io

2.
Mission: Accelerate Machine Learning to Production
Opensource Products:
- ML Lambda: ML Deployment and Serving
- Sonar: Data and ML Monitoring
- Mist: Serverless proxy for Spark
Business Model: PaaS and hands-on consulting
About

3.
Traditional Software Machine Learning applications
Explicit business rules ML generated model
Unit testing Model Evaluation
(Micro)service Model as a Service
Docker per service Docker per Model
1 version of Microservice in prod 1-10-20 model versions in prod at a time
Eng + QA team owning a service 1 ML Engineer owning 10-20 models
Fail loudly (exception, stack trace) Fail silently
Can work forever if verified Performance declines over time
Needs continuous retraining / redeployment
App metrics monitoring Data Monitoring | Model Metrics Monitoring

4.
Cost of an AI/ML Error
● Fun
© http://blog.ycombinator.com/how-adversarial-attacks-work/

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● Fun
● Not fun
Cost of an AI Error

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● Fun
● Not fun
● Not fun at all...
Cost of an AI Error

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● Fun
● Not fun
● Not fun at all…
● Money
Cost of an AI Error

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● Fun
● Not fun
● Not fun at all…
● Money
● Business
Cost of an AI Error

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Where/why may AI fail in prod?

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Where/why may AI fail in prod?
Everywhere!

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Where/why may AI fail in prod?
● Bad training data
● Bad serving data
● Training/serving data skew
● Misconfiguration
● Deployment issue
● Retraining issue
● Performance
● Concept Drift
Everywhere!

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AI Reliability Pyramid

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Reliable Training-Serving pipelines
Comfort Zone for Data Scientist in the
middle of Production

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AI Reliability Pyramid

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Model Deployment and integration
model.pkl model.zip
How to integrate it into AI Application?

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Model server = Model Artifact +
Metadata + Runtime + Deps + Sidecar
/predict
input:
string text;
bytes image;
output:
string summary;
JVM DL4j
GPU
matching_model v2
[
....
]
gRPC HTTP server
routing, shadowing
pipelining
tracing
metrics
autoscaling
A/B, canary
sidecar
serving
requests

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Model Deployment takeaways
● Eliminates hand-off between Data Scientist -> ML Eng ->
Data Eng -> SA Eng -> QA -> Ops
● Sticks components together: Data + Model + Applications +
Automation = AI Application
● Enables quick transition from research to production. ML
engineers can deploy models many times a day
But wait… This is not safe!
How to ensure we’ll not break things in prod?

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AI Reliability Pyramid
1) Is the model degraded?
2) What is the reason?

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Data Format Drift

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Concept Drift

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Concept Drift

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Data exploration in production
Research:
Data Scientist makes
assumptions based on results
of data exploration

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Data exploration in production
Research:
Data Scientist explores
datasets and makes
assumptions/hypothesis
Production:
The model works if and only
if the format and statistical
properties of prod data are
the same as in research
Push to Prod

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Data exploration in production
Research:
Data Scientist makes
assumptions based on results
of data exploration
Production:
The model works if and only
if format and statistical
properties of prod data are
the same as in research
Push to Prod
Continuous data exploration
and validation?

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Automatic Data Profiling
● Avro/Protobuf schema can catch data format drifts
● Statistical properties of input features are to be
captured and continously validated
{"name": "User",
"fields": [
{"name": "name", "type": "string", "min_length": 2, "max_length": 128},
{"name": "age", "type": ["int", "null"], "range": "[10, 100]"},
{"name": "sex", "type": ["string", "null"], " enum": "[male, female, ...]"},
{"name": "wage", "type": ["int", "null"], "validator": "a-distance"}
]
}

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Quality metrics generated from
data profile checks

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How to deal with
- multidimensional dataset
- data timeliness
- data completeness
- image data
- complicated seasonality?

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Anomaly detection
● Rule based programs -> statistical models -> machine
learning models
● Deal with multidimensional datasets, timeliness and
complicated seasonality

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Model Monitoring Metrics on streaming data
● System metrics (latency/throughput)
● Kolmogorov-Smirnov
● Q-Q plot, t-digest
● Spearman and Pearson correlations
● Density based clustering algorithms with Elbow or
Silhouette methods
● Deep Autoencoders
● Generative Adversarial Networks
● Random Cut Forest (AWS paper)
● “Bring your own” metric

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GANs for monitoring data quality at serving time
{production input}
{good}
{drift (fake)}

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Model server = Metadata + Model Artifact +
Runtime + Deps + Sidecar + Training Metadata
/predict
input:
output:
JVM DL4j / TF / Other
GPU
CPU
model v2
[
....
]
gRPC HTTP server
sidecar
serving
requests
training data stats:
- min, max
- range
- clusters
- quantiles
- autoencoder
compare with prod
data in runtime

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Change of the Paradigm
Shifts experimentation to
prod/shadowed environment

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Use Case: Kolmogorov-Smirnov in action

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Use Case: Monitoring NLU system
Figure from: Bapna, Ankur, et al. "Towards zero-shot frame semantic parsing for domain scaling."
arXiv preprint arXiv:1707.02363 (2017).

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Use Case: Monitoring NLU system
Source image: Kurata, Gakuto, et al. "Leveraging sentence-level information with encoder lstm for semantic slot filling." arXiv preprint
arXiv:1601.01530 (2016).
● Train and test offline on restaurants domain
● Deploy do prod
● Feed the model with new random Wiki data
● Monitor intermediate input representations (neural network hidden states)

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Use Case: Monitoring NLU system
● Red and Purple - cluster
of “Bad” production data
● Yellow and Blue - dev and
test data

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AI Reliability Pyramid

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Drift Handling
● Unexpected or dramatic drift? - Alert and add
ML/Data Engineer into the loop.
● Expected drift? - Retrain.
Open question to be solved with ML: classify expected
vs. unexpected drift.

39.
Model Retraining - common questions
When to retrain?
When/how to push to prod?
What data to retraining with?
Manually on demand
Works well for 1 model
But does not scale

40.
Model Retraining - common questions
When to retrain?
When/how to push to prod safely?
What data to retraining with?
Manually on demand
Works well for 1 model
But does not scale
Automatically with the
latest batch
Not safe
Can be expensive
The latest batch may
not be representative

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Solution: Reactive AI powered retraining

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Thank you
- Stepan Pushkarev
- @hydrospheredata
- https://github.com/Hydrospheredata
- https://hydrosphere.io/
- spushkarev@hydrosphere.io