![Scalar proxies:
- RMSE
- RMSLE
- [adjusted] R2
- ...
R2=0.91
RMSE = 692.3
Pearson R=0.96
Optimization Metric:
What’s the essence of what I care about?](https://image.slidesharecdn.com/pydata2015bloom-150729213347-lva1-app6892/85/PyData-2015-Keynote-A-Systems-View-of-Machine-Learning-9-320.jpg)
![Scalar proxies:
- RMSE
- RMSLE
- [adjusted] R2
- ...
R2=0.91
RMSE = 692.3
Pearson R=0.96
scatter
outliers
bias
Optimization Metric:
What’s the essence of what I care about?](https://image.slidesharecdn.com/pydata2015bloom-150729213347-lva1-app6892/85/PyData-2015-Keynote-A-Systems-View-of-Machine-Learning-10-320.jpg)
![Datasets for Data Science Comparison
• ]
• -
Slicing
Induces
Copy
Immutable
Columns
Query
Transfer
Speed to
Python
C++
SDK
Distributed
Memory
Efficiency
Categorical
Optimized
Sparse &
Dense
Pandas
DataFrame
Sequences No Yes N/A No No Medium Medium Yes
GraphLab
SFrame Yes Yes Yes Low Yes Yes High No Yes
Spark
DataFrame Yes Yes Yes Very Low No Yes Low No Yes
Dask Yes No Yes N/A No Yes Medium No No
Blaze No No Yes N/A No Yes Medium No No
Wise
DataSet
Copy-
on-write
No Yes Very High Yes Yes
Very
High
High Yes
See also: Rob Story, today](https://image.slidesharecdn.com/pydata2015bloom-150729213347-lva1-app6892/85/PyData-2015-Keynote-A-Systems-View-of-Machine-Learning-17-320.jpg)
![NLP
{broken: 3, “blue screen”: 2, ...}
computer
vision
{eyes: [{“location”: [21,13],
“bounding”: [...]}]..}
metadata
Sparse
Dense{num_pages: 12, channel:
“email”...}
Nested
3rd party {author_klout: 34.0, ...} Missing/Noisy
timeseries [2014-12-01T12:03:12,
2014-12-01T12:05:12]
Streaming
Real Data != Benchmark Data](https://image.slidesharecdn.com/pydata2015bloom-150729213347-lva1-app6892/85/PyData-2015-Keynote-A-Systems-View-of-Machine-Learning-23-320.jpg)
![[So]'What'should'be'the'machine'learning'engineering'process?”'
“Machine'learning'disrupts'so_ware'engineering'
- Leon Bottou (Facebook)](https://image.slidesharecdn.com/pydata2015bloom-150729213347-lva1-app6892/85/PyData-2015-Keynote-A-Systems-View-of-Machine-Learning-36-320.jpg)
Uploaded byJoshua Bloom
PyData 2015 Keynote: "A Systems View of Machine Learning"
The document discusses a holistic perspective on machine learning systems in science and industry, emphasizing the balance between algorithms, software, and hardware while recognizing the challenges of data dependencies and system architecture. It highlights that a significant portion of production code in machine learning is not directly related to machine learning itself, suggesting the dominance of 'glue code' in operational frameworks. Additionally, the document outlines the importance of monitoring, optimizing, and testing machine learning systems to ensure reliability and effectiveness in practical applications.
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PyData 2015 Keynote: "A Systems View of Machine Learning"
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- 3. http://research.google.com/pubs/pub43146.html • Complex modelserode abstraction boundaries • Data dependencies cost more than code dependencies • System-level Spaghetti • Changing External World “It may be surprising to the academic community to know that only a fraction of the code … is actually doing ‘machine learning’. A mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code.”
- 4. Algorithms Software Hardware Project Staff Consumers Organization +Society ML System Components Agenda - inside-out discussion of component parts & some interconnects - presentation of some facilitating new tools - impact on problem definetion teams
- 5. Linear/ Logistic Regression Naive Bayes Decision Trees SVMs Bagging Boosting Decision Forests Neural Nets Deep Learning Nearest Neighbors Gaussian/ Dirichlet Processes Splines Lasso XGBoost …. Some Algos/Models/Approaches Used inPractice LDA/LSI RNN Software Instantiations in the Python Ecosystem BOW word2vec
- 6. Nguyen'et'al,'CVPR'2015' All Models ofLearning Have Flaws http://hunch.net/?p=224 “It’s common to forget the flaws of the model that you are most familiar…while the flaws of new models get exaggerated.” - John Langford (2007, Microsoft research) Concepts$≠$Statistics Convolutional# networks# can#be# fooled.
- 7. Nguyen'et'al,'CVPR'2015' The$impact$of$dataset$bias Training/testing#on#biased#datasets#gives#unrealistic#results. !E.g.#:#Torralba and#Efros,#Unbiased2look2at2dataset2bias,#CVPR#2011. Torralba/Efros11 viaL. Bottou (ICML 2015) All Models of Learning Have Flaws http://hunch.net/?p=224 “It’s common to forget the flaws of the model that you are most familiar…while the flaws of new models get exaggerated.” - John Langford (2007, Microsoft research) Concepts$≠$Statistics Convolutional# networks# can#be# fooled. (Nguyen#et#al,#CVPR#2015) Magri*e,'ICML,'1929'
- 8. What are youoptimizing for?Component What Algorithm/Model Learning rate, convexity, error bounds, scaling, … + Software/Hardware Accuracy, Memory usage, Disk usage, CPU needs, time to learn, time to predict + Project Staff time to implement, people/ resource costs, reliability, maintainability, experimentability + Consumers direct value, useability, explainability, actionability + Society indirect value - multi-axis optimizations in a given component - highly coupled optimization considerations between components - myoptic view can be costly further up the stack
- 9. Scalar proxies: - RMSE -RMSLE - [adjusted] R2 - ... R2=0.91 RMSE = 692.3 Pearson R=0.96 Optimization Metric: What’s the essence of what I care about?
- 10. Scalar proxies: - RMSE -RMSLE - [adjusted] R2 - ... R2=0.91 RMSE = 692.3 Pearson R=0.96 scatter outliers bias Optimization Metric: What’s the essence of what I care about?
- 11. which classifier isbest? depends... Optimization Metric: What’s the essence of what I care about?
- 12.
- 13. 11 “We evaluated someof the new methods offline but the additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment.” Xavier'Amatriain'and'Jus0n'Basilico'(April'2012) On the Prize
- 14. WiseFactory automated feature extraction,learning, prediction, deployment WiseTransfer efficient manipulation of large objects WiseDataSet WiseML high-productivity data science in Python WiseAlgorithm WindTunnel detect drift in CPU, Mem, Accuracy, Statistics Quality Wrapping High-Level API Deployment & Monitoring C++ SDK Core ML Stack at Wise.io G. Blanco D. Eads J. Richards P. Baines H. Brink
- 15. Wise DataSet BaseVariableGroup BaseVariableGroupBaseVariableGroup InstanceGroup InstanceGroup InstanceGroup RowSparse RowMajor HeterogeneousCache AlgoRepo ColSparse MemMapped Variable Mapper Level Mapper • fast, highly memory-efficient • heterogeneous • distributed Goal: easily surface algorithms (written in C++ to be cache exploitative) to Python WiseDataSets
- 16. Language-agnostic C++ BaseClasses Python-specific Derived Classes Output Input Iterator Processor Array Processor String Processor FrameBuilder SeriesBuilder StringBuilder Frame Processor R-specific Derived Classes • expose flexible interface from Python, to high- performance, Python-agnostic C++ code • pass arbitrary data between layers using “Protocol Master” (like Protobufs) • write C++ code generically for GraphLab, Spark, pandas, and Wise WiseTransfer
- 17. Datasets for DataScience Comparison • ] • - Slicing Induces Copy Immutable Columns Query Transfer Speed to Python C++ SDK Distributed Memory Efficiency Categorical Optimized Sparse & Dense Pandas DataFrame Sequences No Yes N/A No No Medium Medium Yes GraphLab SFrame Yes Yes Yes Low Yes Yes High No Yes Spark DataFrame Yes Yes Yes Very Low No Yes Low No Yes Dask Yes No Yes N/A No Yes Medium No No Blaze No No Yes N/A No Yes Medium No No Wise DataSet Copy- on-write No Yes Very High Yes Yes Very High High Yes See also: Rob Story, today
- 18. Enforcing (Weak) Contracts:Monitoring Deployments Build DS workflow on test set, like the offline testing accuracy deploy & start monitoring results online, accuracy is worse than expected ? 1. Bang head to find (subtle) overfitting in model 2. Retrain: with new data (mo’ data, better answers) 3. Concept Drift: if retraining doesn’t help, jigger the DS workflow 4. Maybe that’s ok: Prediction influenced outcome. Hold out some live. What to do: see also, Chris Harland’s talk yesterday; Mike Manapat, today
- 19. unit tests Regression Tests IntegrationTests Of course you’re doing this… ETL Testing is my contract affected by the (changing) update? Model Deployment Testing @treycausey (yesterday) some tools: Engarde Hypothesis Feature Forge Software Tests Enforcing (Weak) Contracts: Monitoring Deployments 1. Need to know when things are too different than before 2. Then alert a real human 3. Use automated tools to try to isolate cause of change: data or code.
- 20. reproducibility • every deployment& drift test given unique hash • generate data files & script with hash • Perform sampling on known-good deployments • Monitor RAM, CPU, accuracy metrics over time • Probabalistic testing component of our continuous integration of ML, 10k++ tests Wise “WindTunnel”
- 21. “Weak Contracts” ie. Abstractions within componentsbleed through to other components cf. Sculley … 1. A'smart'programmer'makes'an' invenQve'use'of'a'trained'object' recognizer.' 2. The'object'recognizer'receives'data'that' does'not'resemble'the'tesQng'data'and' outputs'nonsense.' 3. The'code'of'the'smart'programmer'does' not'work.' Example (via Bottou)
- 22. Platonic Form Data, aswe act like it is… Plutonic Form …as it is.
- 23. NLP {broken: 3, “bluescreen”: 2, ...} computer vision {eyes: [{“location”: [21,13], “bounding”: [...]}]..} metadata Sparse Dense{num_pages: 12, channel: “email”...} Nested 3rd party {author_klout: 34.0, ...} Missing/Noisy timeseries [2014-12-01T12:03:12, 2014-12-01T12:05:12] Streaming Real Data != Benchmark Data
- 25.
- 26. http://mltsp.io pip install mltsp MLtsp. Machine Learning Time-Series Platform R. AllenM. SilverF. Peréz JSB Domain scientists AstroSeismoNeuro Funding bodies S. van der Walt A. Creillin-Quick Comp/ Stat/Eng An open-source web platform for distributed time-series analysis → •Selection of sophisticated feature extraction algorithms •Distributed computation •Sandboxed execution of custom code
- 27. Flask CLI (under developement) REST /learn /upload UI Disco W1W2 Wn Disco worker pool datastore DB Demo!
- 28. -- MLTSP Continuous Integration github.com/drone github.com/mltsp/mltsp Test Container with MLTSP Custom Feature Extractor Sandbox Worker Pullrequest triggers webhook Workers- Disco SSH Drone calls GitHub status API
- 29. http://bigmacc.info Results from MLTSP TheAstrophysical Journal Supplement Series, 203:32 (27pp), 2012 December Published Work before MLTSP MVP: Reproduce main results of a scientific paper
- 30. Probabilistic Classfication of VariableStars Shivvers,JSB,Richards MNRAS,2014 106 “DEB” candidates 12 new mass-radii 15 “RCB/DYP” candidates 8 new discoveries Triple # of Galactic DYPer Stars Miller, Richards, JSB,..ApJ 2012 5400 Spectroscopic Targets Miller, JSB, Richards,..ApJ 2015 Turn synoptic imaged into ~spectrographs
- 31. WISE SUPPORT FROM SUBJECT DESCRIPTION Support Ticket DATE FROM SUBJECT DESCRIPTION SupportTicket DATE FROM SUBJECT DESCRIPTION Support Ticket DATE FROM SUBJECT DESCRIPTION Support Ticket DATE FROM SUBJECT DESCRIPTION Support Ticket DATE FROM SUBJECT DESCRIPTION Support Ticket DATE FROM SUBJECT DESCRIPTION Support Ticket DATE TIER 1 AUTOMATED RESPONSE CUSTOMER FROM COMPLEXITY TO CLARITY INTELLIGENT ROUTING RECOMMENDED RESPONSE AUTOMATED REPLY Wise Support >30% faster avg. response time more consistent answers faster scaling of support teams
- 32. Fault Tolerant ML augmentationvs. full automation Random forest prediction of body segment in Xbox Kinect gmail
- 35. https://www.reddit.com/r/funny/comments/3e7gy4/yes_netflix_because_my_6_year_old_will_enjoy_the/ “Yes Netflix, because my6 year old will enjoy the animated fun of Sons of Anarchy”
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- 37. ỉπ vs. (or “DataScience is a Team Sport”) deep domain skill/knowledge/training deep methodological knowledge/skill deep domain or methodological skill/knowledge/training strong methodological or domain knowledge/skill Goal: empower teams of gamma’s to excel ML Systems: It Takes a Village
- 38. ‣ Novel testingcan strengthen abstractions within components, and contracts between ‣ Machine Learning Systems require optimizations across components - so we’d better understand the true loss function ‣ (End user) fault tolerance is a must Parting Thoughts ‣ Build ML into Systems because to have to…
- 39. Area Man Bites offmore than he can chew PyData 2014
- 40.