alpine innovation final v1.0
DESCRIPTION
Alpine is constantly innovating, ever since the founding of the company based on in-database analytics that went far beyond traditional, in-memory, code-based desktop applications. This initial innovation built on the work of the MADlib team at Greenplum/Pivotal, ultimately inspired by the work of Joe Hellerstein’s team at UC Berkeley. The team then made all of this functionality available in a simple web interface, which enabled enterprise collaboration and a team-based approach to analytics. Later on, Alpine released its first support for Hadoop, enabling complex analytics on Hadoop without any coding, taking care of all the complexity of MapReduce and Hadoop configuration. Most recently, Alpine has been building new capabilities on top of Spark, to offer Hadoop users a new level of performance and scale.TRANSCRIPT
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Innovation on DB [email protected] [email protected]
Machine Learning Engineering @AlpineDataLabs
August 14, 2014
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Alpine Data Labs• Advanced Analytic Software Company
– Founded in 2011– Agile Advanced Analytics, Collaboration and Management at Enterprise Scale– Partnerships with EMC, Pivotal, MapR, Cloudera, QlikView and Tableau
• 50+ employees, based in San Francisco– Machine Learning, Statistics and Big Data (Stanford, Berkeley, MIT)
• Growing in excess of 200% YOY with a broad international customer base– Financial Services, Online Media, Government, Retail, Manufacturing…
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Advanced Analytics on Big Data
Alpine Data Labs. Confidential and Proprietary.
Timeframe of Relevance
Work independently and re-use data scientist work. Collaborate across functions and teams. Iterate quickly.
Scalable Business Analytics
Allowing the Enterprise to manage “Data as an Asset.”
Scale and guard data practices
Data Science Productivity
Work faster, safer, in a more open manner. Industry leading machine learning algorithms built natively for parallel processing.
ALPINE CHORUS 4.0
ENTERPRISE DATA ENVIRONMENT
Data Scientist
Database Analyst
Data Engineer
Business Analyst
Campaign Manager
Sales Division
CustomerSuccess
Product Manager
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TRADITIONAL DESKTOP
IN-DATABASE METHODS
WEB-BASED AND COLLABORATIVE
SIMPLIFIED CODE-FREE HADOOP & MPP DATABASE
ONGOING INNOVATION
The Path to Innovation
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The Path to Innovation
Iterative algorithms scan through the data each time
1st Iteration
Total
2nd Iteration
With Spark, data is cached in memory after first iteration
Quasi-Newton methods enhance in-memory benefits
921s150mm
rows
97s
1st Iteration
Total
2nd Iteration
1st Iteration
Total
2nd Iteration
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Machine Learning in the Big Data Era
• Hadoop Map Reduce solutions
• MapReduce scales well for batch processing• Lots of machine learning algorithms are iterative by nature• There are lots of tricks people do, like training with subsamples of data, and
then average the models. Why have big data if you’re only approximating.
+ =
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Lightning-fast cluster computing
• Empower users to iterate through the data by utilizing the in-memory cache.
• Logistic regression runs up to 100x faster than Hadoop M/R in memory.
• We’re able to train exact models without doing any approximation.
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Why Alpine supports MLlib?
• MLlib is a Spark subproject providing Machine Learning primitives.
• It’s built on Apache Spark, a fast and general engine for large-scale data processing.
• Shipped with Apache Spark since version 0.8• High quality engineering design and effort• More than 50 contributors since July 2014• Alpine is 100% committed to open source to facilitate industry
adoption that are driven by business needs.
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AutoML
• Success of machine learning crucially relies on human machine learning experts, who select appropriate features, workflows, paradigms, algorithms, and their hyper-parameters.
• Even the hyper-parameters can be chosen by grid search with cross-validation, a problem with more than two parameters becomes very difficult and challenging. It’s a non-convex optimization problem.
• There is a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge.
- AutoML workshop @ ICML’14
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Random Forest• An ensemble learning method for classification & regression that
operates by constructing a multitude of decision trees at training time.
• A “black box” without too much tuning and it can automatically identify the structure, interactions, and relationships in the data.
• A technique to reduce the variance of single decision tree predictions by averaging the predictions of many de-correlated trees.
• De-correlation is achieved through Bagging and / or randomly selecting features per tree node.
NOTE: Most Kaggle competitions have at least one top entry that heavily uses Random Forests.
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Sequoia ForestWhy Sequoia Forest?
MLlib already has a decision tree implementation, but it doesn’t support random features and is not optimized to train on large clusters.
What does Sequoia Forest do?
• Classification and Regression.• Numerical and Categorical Features.
What’s next?
Gradient Boosting
Where can you find?https://github.com/AlpineNow/SparkML2
We’re merging back with MLlib and is licensed under the Apache License.
More info: http://spark-summit.org/2014/talk/sequoia-forest-random-forest-of-humongous-trees.
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Spark-1157: L-BFGS Optimizer
• No, its not a blender!
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What is Spark-1157: L-BFGS Optimizer
• Merged in Spark 1.0• Popular algorithms for parameter estimation in Machine
Learning.• It’s a quasi-Newton Method.• Hessian matrix of second derivatives doesn't need to be
evaluated directly. • Hessian matrix is approximated using gradient evaluations. • It converges a way faster than the default optimizer in Spark,
Gradient Decent.
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SPARK-2934: LogisticRegressionWithLBFGS
• Merged in Spark 1.1 • Using L-BFGS to train Logistic Regression instead of default
Gradient Descent. • Users don't have to construct their objective function for
Logistic Regression, and don't have to implement the whole details.
• Together with SPARK-2979 to minimize the condition number, the convergence rate is further improved.
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0 5 10 15 20 25 30 350.3
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Logistic Regression with a9a Dataset (11M rows, 123 features, 11% non-zero elements)16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster
L-BFGS Dense FeaturesL-BFGS Sparse FeaturesGD Sparse FeaturesGD Dense Features
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sa9a Dataset Benchmark
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a9a Dataset Benchmark
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Logistic Regression with a9a Dataset (11M rows, 123 features, 11% non-zero elements)16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster
L-BFGSGD
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0 5 10 15 20 25 300
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Logistic Regression with rcv1 Dataset (6.8M rows, 677,399 features, 0.15% non-zero elements)16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster
LBFGS Sparse Vector
GD Sparse Vector
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rcv1 Dataset Benchmark
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news20 Dataset Benchmark
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Logistic Regression with news20 Dataset (0.14M rows, 1,355,191 features, 0.034% non-zero elements)16 executors in INTEL Xeon E3-1230v3 32GB Memory * 5 nodes Hadoop 2.0.5 alpha cluster
LBFGS Sparse VectorGD Sparse Vector
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SPARK-2979: Improve the convergence rate by standardizing the training features
Merged in Spark 1.1 Due to the invariance property of MLEs, the scale of your inputs are irrelevant. However, the optimizer will not be happy with poor condition numbers which
can often be improved by scaling. The model is trained in the scaled space, but the coefficients are converted to
original space; as a result, it's transparent to users. Without this, some training datasets mixing the columns with different scales
may not be able to converge. Scikit and glmnet package also standardize the features before training to
improve the convergence. Only enable in Logistic Regression for now.
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SPARK-2272: Transformer
A spark, the soul of a transformer
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SPARK-2272: Transformer Merged in Spark 1.1 MLlib data preprocessing pipeline. StandardScaler
Standardize features by removing the mean and scaling to unit variance. RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear
models typically works better with zero mean and unit variance. Normalizer
Normalizes samples individually to unit L^n norm. Common operation for text classification or clustering for instance. For example, the dot product of two l2-normalized TF-IDF vectors is the cosine
similarity of the vectors.
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StandardScaler
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Normalizer
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Merged in Spark 1.1
Online algorithms for computing the mean, variance, min, and max in a streaming fashion.
Two online summerier can be merged, so we can use one summerier for one block of data in map phase, and merge all of them in reduce phase to obtain the global summarizer.
A numerically stable one-pass algorithm is implemented to avoid catastrophic cancellation in naive implementation. Ref: http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
Optimized for sparse vector, and the time complexity is O(non-zeors) instead of O(numCols) for each sample.
SPARK-1969: Online summarizer
Two-pass algorithm Naive algorithm
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Merged in Spark 1.1
Floating point math is not exact, and most floating-point numbers end up being slightly imprecise due to rounding errors.
Simple values like 0.1 cannot be precisely represented using binary floating point numbers, and the limited precision of floating point numbers means that slight changes in the order of operations or the precision of intermediates can change the result.
That means that comparing two floats to see if they are equal is usually not what we want. As long as this imprecision stays small, it can usually be ignored.
Scala syntax sugar comparators are implemented using implicit conversion allowing developers to write unittest easier.
SPARK-2479: MLlib UnitTests
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SPARK-1892: OWL-QN Optimizerongoing work
It extends L-BFGS to handle L2 and L1 regularizations together
(balanced with alpha as in elastic nets) We fixed couple issues #247 in Breeze's OWLQN
implementation, and this work is based on that. Blocked by SPARK-2505
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SPARK-2505: Weighted Regularizationongoing work
Each components of weights can be penalized differently. We can exclude intercept from regularization in this framework. Decoupling regularization from the raw gradient update which is not
used in other optimization schemes. Allow various update/learning rate schemes (adagrad, normalized
adaptive gradient, etc) to be applied independent of the regularization
Smooth and L1 regularization will be handled differently in optimizer.
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SPARK-2309: Multinomial Logistic Regressionongoing work
For K classes multinomial problem, we can generalize it via K -1 linear models with logist link functions.
As a result, the weights will have dimension of (K-1)(N + 1) where N is number of features.
MLlib interface is designed for one set of paramerters per model, so it requires some interface design changes.
Expected to be merged in next release of MLlib, Spark 1.2
Ref: http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297
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Technology we're using: Scala/Java, Akka, Spray, Hadoop, Spark/SparkSQL, Pig, Sqoop, Javascripts, D3.js etc.
Actively involved in the open source community: almost of all our newly developed algorithms in Spark will be contributed back to MLLib.
Actively developing on application to/from Spark Yarn communication infrastructure (application lifecycle, error reporting, progress monitoring and interactive command etc)
In addition to Spark, we are the maintainer of several open source projects including Chorus, SBT plugin for JUnit test Listener, and Akka-based R engine.
Weekly tech/ML talks. Speakers: David Hall (author of Breeze), Heather Miller (student of Martin Ordersky), H.Y. Li (author of Tachyon), and Jason Lee (student of Trevor Hastie), etc…
Oraginzes the SF Machine Learning meetup (2k+ members). Speakers: Andrew Ng (Stanford), Michael Jorden (Berkely), Xiangrui Meng (Databricks), Sandy Ryza (Cloudera), etc…
We’re open source friendly and tech driven!
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Machine Learning Engineer Data Scientist UI/UX Engineer Platform Engineer Automation Test Engineer
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