Strata San Jose 2016: Scalable Ensemble Learning with H2O

"In addition to the many data engineering initiatives at Starbucks, we are also working on many interesting data science initatives. The business scenarios involved in our deep learning initatives include (but are not limited to) planogram analysis (layout of our stores for efficient partner and customer flow) to predicting product pairings (e.g. purchase a caramel machiato and perhaps you would like caramel brownie) via the product components using graph convolutional networks. For this session, we will be focusing on how we can run distributed Keras (TensorFlow backend) training to perform image analytics. This will be combined with MLflow to showcase the data science lifecycle and how Databricks + MLflow simplifies it. "

Interested in learning how Showtime is leveraging the power of Spark to transform a traditional premium cable network into a data-savvy analytical competitor? The growth in our over-the-top (OTT) streaming subscription business has led to an abundance of user-level data not previously available. To capitalize on this opportunity, we have been building and evolving our unified platform which allows data scientists and business analysts to tap into this rich behavioral data to support our business goals. We will share how our small team of data scientists is creating meaningful features which capture the nuanced relationships between users and content; productionizing machine learning models; and leveraging MLflow to optimize the runtime of our pipelines, track the accuracy of our models, and log the quality of our data over time. From data wrangling and exploration to machine learning and automation, we are augmenting our data supply chain by constantly rolling out new capabilities and analytical products to help the organization better understand our subscribers, our content, and our path forward to a data-driven future. Authors: Josh McNutt, Keria Bermudez-Hernandez

This document discusses challenges in deploying machine learning models for scoring in streaming applications using Apache Spark. It describes how ML Pipelines and Structured Streaming in Spark can be used to build an application that monitors web sessions for bots in real-time. However, there are issues with two-pass transformers and handling invalid data in Spark 2.2. Spark 2.3 includes fixes that allow most transformers and models to work for both batch and streaming scoring, and improves handling of invalid values. The talk provides tips on updating pipelines to work with streaming and testing them.

Deep Reinforcement Learning (DRL) is a thriving area in the current AI battlefield. AlphaGO by DeepMind is a very successful application of DRL which has drawn the attention of the entire world. Besides playing games, DRL also has many practical use in industry, e.g. autonomous driving, chatbots, financial investment, inventory management, and even recommendation systems. Although DRL applications has something in common with supervised Computer Vision or Natural Language Processing tasks, they are unique in many ways. For example, they have to interact (explore) with the environment to obtain training samples along the optimization, and the method to improve the model is usually different from common supervised applications. In this talk we will share our experience of building Deep Reinforcement Learning applications on BigDL/Spark. BigDL is a well-developed deep learning library on Spark which is handy for Big Data users, but it has been mostly used for supervised and unsupervised machine learning. We have made extensions particularly for DRL algorithms (e.g. DQN, PG, TRPO and PPO, etc.), implemented classical DRL algorithms, built applications with them and did performance tuning. We are happy to share what we have learnt during this process. We hope our experience will help our audience learn how to build a RL application on their own for in their production business.

Sparkling Water provides transparent integration of the H2O machine learning platform into the Spark ecosystem. It allows users to use advanced H2O machine learning algorithms like deep learning, gradient boosted machines, and random forests within existing Spark workflows. Sparkling Water excels at tasks that require these advanced algorithms, like complex predictive modeling problems. It also enables loading and parsing data directly into the H2O distributed in-memory framework using the H2OFrame data structure.

My talk at Data Science Labs conference in Odessa. Training a model in Apache Spark while having it automatically available for real-time serving is an essential feature for end-to-end solutions. There is an option to export the model into PMML and then import it into a separated scoring engine. The idea of interoperability is great but it has multiple challenges, such as code duplication, limited extensibility, inconsistency, extra moving parts. In this talk we discussed an alternative solution that does not introduce custom model formats and new standards, not based on export/import workflow and shares Apache Spark API.

This document discusses machine learning pipelines and introduces Evan Sparks' presentation on building image classification pipelines. It provides an overview of feature extraction techniques used in computer vision like normalization, patch extraction, convolution, rectification and pooling. These techniques are used to transform images into feature vectors that can be input to linear classifiers. The document encourages building simple, intermediate and advanced image classification pipelines using these techniques to qualitatively and quantitatively compare their effectiveness.

"GOJEK, the Southeast Asian super-app, has seen an explosive growth in both users and data over the past three years. Today the technology startup uses big data powered machine learning to inform decision-making in its ride-hailing, lifestyle, logistics, food delivery, and payment products. From selecting the right driver to dispatch, to dynamically setting prices, to serving food recommendations, to forecasting real-world events. Hundreds of millions of orders per month, across 18 products, are all driven by machine learning. Building production grade machine learning systems at GOJEK wasn't always easy. Data processing and machine learning pipelines were brittle, long running, and had low reproducibility. Models and experiments were difficult to track, which led to downstream problems in production during serving and model evaluation. In this talk we will cover these and other challenges that we faced while trying to scale end-to-end machine learning systems at GOJEK. We will then introduce MLflow and explore the key features that make it useful as part of an ML platform. Finally, we will show how introducing MLflow into the ML life cycle has helped to solve many of the problems we faced while scaling machine learning at GOJEK. "

Spark has become synonymous with big data processing, however the majority of data scientists still build models using single machine libraries. This talk will explore the multitude of ways Spark can be used to scale machine learning applications. In particular, we will guide you through distributed solutions for training and inference, distributed hyperparameter search, deployment issues, and new features for Machine Learning in Apache Spark 3.0. Niall Turbitt and Holly Smith combine their years of experience working with Spark to summarize best practices for scaling ML solutions.

This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/ndUtKRzVUCo In this presentation, Erin LeDell (Chief Machine Learning Scientist, H2O.ai), will provide an overview of the field of "Automatic Machine Learning" and introduce the new AutoML functionality in H2O. H2O's AutoML provides an easy-to-use interface which automates the process of training a large, comprehensive selection of candidate models and a stacked ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard. Bio: Erin is the Chief Machine Learning Scientist at H2O.ai. Erin has a Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley. Her research focuses on automatic machine learning, ensemble machine learning and statistical computing. She also holds a B.S. and M.A. in Mathematics. Before joining H2O.ai, she was the Principal Data Scientist at Wise.io (acquired by GE Digital in 2016) and Marvin Mobile Security (acquired by Veracode in 2012), and the founder of DataScientific, Inc.

Building a machine learning model is an iterative process. A data scientist will build many tens to hundreds of models before arriving at one that meets some acceptance criteria. However, the current style of model building is ad-hoc and there is no practical way for a data scientist to manage models that are built over time. In addition, there are no means to run complex queries on models and related data. In this talk, we present ModelDB, a novel end-to-end system for managing machine learning (ML) models. Using client libraries, ModelDB automatically tracks and versions ML models in their native environments (e.g. spark.ml, scikit-learn). A common set of abstractions enable ModelDB to capture models and pipelines built across different languages and environments. The structured representation of models and metadata then provides a platform for users to issue complex queries across various modeling artifacts. Our rich web frontend provides a way to query ModelDB at varying levels of granularity. ModelDB has been open-sourced at https://github.com/mitdbg/modeldb.

- The document discusses deep learning frameworks and how to choose one for a given environment. It summarizes the strengths, weaknesses, opportunities and threats of popular frameworks like TensorFlow, Theano, Torch, Caffe, DeepLearning4J and H2O. - It recommends H2O as a good choice for enterprise environments due to its ease of use, scalability on big data, integration with Spark, Java/Scala support and commercial support. DeepLearning4J is also recommended for more advanced deep neural networks and multi-dimensional arrays. - The document proposes using Spark as a middleware to leverage multiple frameworks and avoid vendor lock-in, and describes Agile Lab's recommended stack for enterprises which combines H

This document summarizes a data science summit attended by the author. It includes a brief overview of the author's travel itinerary to and from the event. The main body summarizes various sessions and topics discussed, including machine learning platforms and tools like Dato, IBM System ML, Apache Flink, PredictionIO, and DeepLearning4J. Session topics focused on scalable data processing, stream and batch processing, graph processing, and machine learning algorithms. The document provides links to several of the platforms and tools discussed.

Gurpreet Singh from Microsoft gave a talk on scaling Python for data analysis and machine learning using DASK and Apache Spark. He discussed the challenges of scaling the Python data stack and compared options like DASK, Spark, and Spark MLlib. He provided examples of using DASK and PySpark DataFrames for parallel processing and showed how DASK-ML can be used to parallelize Scikit-Learn models. Distributed deep learning with tools like Project Hydrogen was also covered.

This document discusses platforms for democratizing data science and enabling enterprise grade machine learning applications. It introduces Flock, a platform that aims to automate the machine learning lifecycle including tracking experiments, managing models, and deploying models for production. It demonstrates Flock by instrumenting Python code for a light gradient boosted machine model to track parameters, log models to MLFlow, convert the model to ONNX, optimize it, and deploy it as a REST API. Future work discussed includes improving Flock's data governance, generalizing auto-tracking capabilities, and integrating with other systems like SQL and Spark for end-to-end pipeline provenance.

Tech-Talk at Bay Area Spark Meetup Apache Spark(tm) has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these model to a production environment. How do I embed what I have learned into customer facing data applications. Like all things in engineering, it depends. In this meetup, we will discuss best practices from Databricks on how our customers productionize machine learning models and do a deep dive with actual customer case studies and live demos of a few example architectures and code in Python and Scala. We will also briefly touch on what is coming in Apache Spark 2.X with model serialization and scoring options.

This document provides an overview of scalable machine learning in R and Python using the H2O platform. It introduces H2O.ai, the company and H2O, the open source machine learning platform. Key features of the H2O platform include its distributed algorithms, APIs for R and Python, and interfaces like H2O Flow. The document outlines tutorials for using popular algorithms like deep learning and ensembles in H2O and describes ongoing developments like DeepWater and AutoML.

Ensemble machine learning methods are often used when the true prediction function is not easily approximated by a single algorithm. Practitioners may prefer ensemble algorithms when model performance is valued above other factors such as model complexity and training time. The Super Learner algorithm, also called "stacking", learns the optimal combination of the base learner fits. The latest version of H2O now contains a "Stacked Ensemble" method, which allows the user to stack H2O models into a Super Learner. The Stacked Ensemble method is the the native H2O version of stacking, previously only available in the h2oEnsemble R package, and now enables stacking from all the H2O APIs: Python, R, Scala, etc. Erin is a Statistician and Machine Learning Scientist at H2O.ai. Before joining H2O, she was the Principal Data Scientist at Wise.io (acquired by GE Digital) and Marvin Mobile Security (acquired by Veracode) and the founder of DataScientific, Inc. Erin received her Ph.D. from University of California, Berkeley. Her research focuses on ensemble machine learning, learning from imbalanced binary-outcome data, influence curve based variance estimation and statistical computing.

Ray Peck from H2O.ai talks about the roadmap for the upcoming AutoML product in H2O. - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

Machine Learning for Smarter Apps with Tom Kraljevic - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

- The document describes a presentation on deep learning given by Arno Candel of H2O.ai. - The presentation covered deep learning methods and implementations, results from case studies in Higgs boson classification, handwritten digit recognition, and text classification. - It also demonstrated H2O's scalability and the ability of its deep learning algorithm to achieve state-of-the-art results on benchmark datasets.