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Machine Learning as a Service An Entertaining Comparison by Sandeep Parikh

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Agenda Who offers machine learning services? How do the services work? How do you use it in your applications? What we’ll cover

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Hi, I’m Sandeep I’m a Solutions Architect at Google, working on Google Cloud Platform; I spend my time designing and documenting architectural patterns and solutions. Before that I worked for MongoDB. Before that...well a bunch of other places. I’ve been in Austin for about 12 years so I get to complain about everything. @crcsmnky

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Just To Be Clear I am not a data scientist This talk is not sanctioned by my employer I will do my best to be unbiased

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The Contenders Amazon Web Services Google Cloud Platform Microsoft Azure

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Required Reading Thanks to Inês Almeida [1] for putting together an incredibly detailed and thorough Machine Learning Service Comparison [2]. She covers: ● Data sourcing ● Data preprocessing ● Model building ● Model evaluation [1] https://blog.onliquid.com/author/isbalmeida/ [2] https://blog.onliquid.com/machine-learning-service-benchmark/

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Developer Centric Approach Not all who need machine learning are data scientists Could be used to bootstrap a larger effort Perhaps other challenges take precedence

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Example Application Browse and rate movies Using the Movielens 10M dataset [1] Thanks to the fine folks at Grouplens [2] [1] https://movielens.org/ [2] http://grouplens.org/

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Code and Components https://github.com/crcsmnky/movieweb Built using Python, Flask, MongoDB Updated to use machine learning real-time endpoints from each service Will push updated code soon™

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What’s Missing From MovieWeb? How do you know if a user will like a particular movie?

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Steps and Goals Use Movielens training data Contains userId, movieId, rating Train a model Evaluate model Create “recommendation” service Profit!

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AWS

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AWS: Create Datasource ratings.csv S3

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AWS: Create Model

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AWS: Correcting Model Type

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AWS: Schema configuration By default schema read in as: userId (numeric), movieId (numeric), rating (numeric) But I need: userId (numeric), movieId (numeric), rating (categorical)

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AWS: Create Model

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AWS: Evaluate Model 1-Click (patented!)

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AWS: Predictions Generate real-time predictions endpoint Supply userId, movieId Returns predicted rating Upload dataset and run batch predictions One time, but need to generate dataset (about 8 GB) Repeat as-needed (daily? weekly?)

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AWS: Limits

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Azure

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Azure: Create Experiment Create new datasource and upload ratings.csv Then jump into Azure ML Studio...wow! After some trial and error, landed on an approach

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Azure: Studio

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Azure: Training Experiment choose features split 70/30

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Azure: Evaluation Results

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Azure: Convert to Predictive Experiment First, run training experiment Then, you’ve got a predictive experiment

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Azure: Predictions Generate real-time predictions endpoint Supply userId, movieId Returns predicted rating (and probabilities of other labels) Upload dataset and run batch predictions One time, but need to generate dataset Repeat as-needed (daily? weekly?)

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Azure: Limits 20 requests per endpoint (up to 10,000 endpoints) Up to 10 GB for training data No mention of batch limitations or daily limits

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Google Cloud Platform

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Google: Upload Data Create a Cloud Storage bucket and upload ratings.csv ...Then it’s all API-driven But, it does support Predictive Modeling Markup Language for metadata, transformations, etc.

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Google: Train API

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Model training is asynchronous, so you have to check when it’s ready Google: Model Availability

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Google: Real-time Predictions Once model is trained and available, send individual API requests to predict rating Batch predictions available, done by batching API requests

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Google: Real-time Predictions Using real-time predictions API Supply userId, movieId Returns predicted rating (and probabilities of other labels)

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Google: Limits 2,000,000 predictions per day Training data up to 2.5 GB

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Wrap Up

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Takeaways AWS has a basic but powerful interface - supports regression or multiclass classification and easy model evaluation. Azure has crazy robust Studio interface with lots of algorithms and power - including ability to use BYO Python or R code Google has a limited interface and everything is opaque but supports standard PMML for metadata and transformation. Missing quality UI and scoring/eval are all behind the scenes. Marketing would tout this as “simple”

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Takeaways AWS and Azure have support for batch predictions, which would be useful for this application Azure’s algorithm and code support is top-notch. Generates code for C#, Python, and R for batch and streaming endpoints Google requires you to manually batch but you can update your trained model with more ratings come in, reducing re-training time

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But Wait, There’s More I’ve made a huge mistake! My training dataset had userId and movieId - those aren’t nearly enough to predict using multiclass regression Training data should have included userId and movie metadata (like year, genres, title) to generate the best model

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Other Approaches Collaborative Filtering “If you and I like the same thing, I might like other things you like” ...Or something like that Other tools can do this pretty easily with userId and movieId See http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html

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Final Thoughts Garbage In == Garbage Out Well, Garbage Applied == Garbage Out too Can’t just throw algorithms against the wall and see what sticks That’s not data science - your output and user experience will suffer

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Final Thoughts Compare output from each service to see how they performed Didn’t even talk about cost or pricing so YMMV Consider other approaches Not just about finding well-performing algorithms Must also consider what makes sense for your use case and/or application

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Final Thoughts These tools are very powerful but they aren’t a panacea Expertise and an understanding of the underlying analyses is critical to making this useful Be careful going down this road - make sure you’ve understood the data science problem before leveraging

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Thanks! Where can you find me? @crcsmnky http://github.com/crcsmnky [email protected] Questions?