Download - Next.ml Boston: Data Science Dev Ops
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Data Science DevOps
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yhat
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Blog
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Hi, my name is Eric… and I’m a software engineer.
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{ "pred_class" : 0, "prob": 0.87}
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Data Science DevOps
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Today’s Agenda1. Data science or
machine learning?2. A story about protein3. What makes DevOps
hard4. Attempting solutions5. Q & A
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First, let’s get a couple things out of the way.
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Next.ML
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Next.ML
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Data Science or Machine Learning?!?
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Data Science“Applying machine learning to organizational problems.”
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Different ProblemsSoftware engineering, not strictly engineering.
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Different ProblemsHow do I work with others?
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Different ProblemsHow do I version this?
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Different ProblemsHow do I not break things?
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Data ScienceIn the end still machine learning.
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Bro, do you even big data?
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Data mining vs. model building
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Data mining vs. model building
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Okay, story time
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Let’s talk about protein
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(Crystallography Team)
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(Crystallography Team)
●Uses “brute force” approach to crystallize proteins
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(Crystallography Team)
●Uses “brute force” approach to crystallize proteins
●Manually scores images one at a time
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Crystal
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$$$$$
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Murky stuff
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Lighting difference
s
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Important murky stuff
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What the hell is this line?
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R & D
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R & D Production
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Porting this was hard
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Will it make my job easier?
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:(
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yhat
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R & D Production
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Difficult to encapsulate
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How to make them production ready?
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“Production” - Reliable- Reproducible- Scalable
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Some reasons about why this is hard to achieve
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Model != Service(bare with me on this one)
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Software stacks are complicated
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Software stacks are complicated
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All technologies can be connected to over a network
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Where does machine learning fit into this stack?
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What stops us from doing this?
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Machine learning is stateful
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import alg…model = alg.train(data)…model.predict(newdata)
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import alg…model = alg.train(data)…model.predict(newdata)
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- Source code doesn’t encapsulate program
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- Source code doesn’t encapsulate program- Training is expensive (don’t want to do it every time)
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Serialization
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Serialization$python>>> import pickle>>> x = 3>>> p = pickle.dumps(x)>>> y = pickle.loads(p)>>> y3
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Sterilization has it’s own set of problems
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Traceback (most recent call last): File "diabetes.py", line 33, in <module>
R2 = cross_val_score(clf, X, y=y, cv=KFold(y.size, K), n_jobs=1).mean() File "/home/eric/programming/python/env/local/lib/python2.7/site-packages/sklearn/cross_validation.py", line 1361, in cross_val_score
for train, test in cv) File "/home/eric/programming/python/env/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 659, in __call__
self.dispatch(function, args, kwargs) File "/home/eric/programming/python/env/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 406, in dispatch
job = ImmediateApply(func, args, kwargs) File "/home/eric/programming/python/env/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 140, in __init__
self.results = func(*args, **kwargs) File "/home/eric/programming/python/env/local/lib/python2.7/site-packages/sklearn/cross_validation.py", line 1459, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params) File "/home/eric/programming/python/env/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py", line 273, in fit
for i, t in enumerate(trees)) File "/home/eric/programming/python/env/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 659, in __call__
self.dispatch(function, args, kwargs) File "/home/eric/programming/python/env/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 406, in dispatch
job = ImmediateApply(func, args, kwargs) File "/home/eric/programming/python/env/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 140, in __init__
self.results = func(*args, **kwargs) File "/home/eric/programming/python/env/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py", line 94, in _parallel_build_trees
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False) File "/home/eric/programming/python/env/local/lib/python2.7/site-packages/sklearn/tree/tree.py", line 227, in fit
raise ValueError("min_weight_fraction_leaf must in [0, 0.5]")ValueError: min_weight_fraction_leaf must in [0, 0.5]
Customer - “My model isn’t working”
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What’s the problem?…R2 = cross_val_score(clf, X, y=y, cv=KFold(y.size, K), n_jobs=1).mean()…tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
raise ValueError("min_weight_fraction_leaf must in [0, 0.5]")
ValueError: min_weight_fraction_leaf must in [0, 0.5]
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What’s the problem? - Pickle a scikit-learn 0.16.1 model- Unpickle it in 0.15.1
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Interpreted languages have a lot of run time dependencies
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Reproducing dependencies is critical
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Reproducing dependencies is criticalDependency detection can be hard to automate
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Reproducing dependencies is criticalPackage managersaren’t perfect
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Example: pip
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Example: pip- Not standard
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Example: pip- Not standard- Can do a poor job of installing dependencies
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Example: pip- Not standard- Can do a poor job of installing dependencies- Only recently precompiled
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Example: r
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Example: r- Can’t install specific version of package
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Example: r- Can’t install specific version of package- No, seriously
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Solution
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Solution- Use a better package manager
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Solution- Use a better package manager- Ship your dependencies
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Complied languages are easier- Matlab (MCC), Scala
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Complied languages are easier- Matlab (MCC), Scala- Linking still an issue
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Data transforms can be critical to the model
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PMML
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PMML ?def tokenize(s): s = s.lower() s = s.split(" ") return s
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$ python>>> def tokenize(s):... return s.lower().split(" ")...>>> import pickle>>> pickle.dumps(tokenize)'c__main__\nclean_sentence\np0\n.'
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$ python>>> def tokenize(s):... return s.lower().split(" ")...>>> import pickle>>> pickle.dumps(tokenize)'c__main__\nclean_sentence\np0\n.'
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Trying to get models onto a network
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Databases are great
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1) Compute regression
2) Shove coefficients in database
3) …4) Profit?!?
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Simple Web Servers
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Simple Web ServersYou’re still stuck with environment management problems
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Simple Web ServersSome modeling languages are not languages you want to write a server in…
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Simple Web Servers- Division of roles- NPR uses flask for visualization dev but not production website
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A solution we (yhat) have decided on
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Containers FTW
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Containers FTWContainers address a lot of previous concerns
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Containers FTWReproducibility, managing environments, etc.
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Containers FTWCheap
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Containers FTWWord of warning:
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Containers FTWWord of warning:If you choose this route you will be manage models and Docker
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{ "pred_class" : 0, "prob": 0.87}
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Take a model
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“Deploy” to our platform
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Defer to Docker
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$ pip install foo==0.2.4$ pip install bar==1.4.9Attempt to
recreate env
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$ pip install foo==0.2.4$ pip install bar==1.4.9Replicate as
necessary
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The dev team is always trying to learn of better ways of doing this
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Thanks!
And remember to be nice to your DevOps.