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Start Getting Your Feet Wet in Open Source Machine and Deep Learning
- 1. Open Source AI Platform for Business Transformation
- 2. Desmond Chan Senior Director of Marketing, H2O.ai
- 3. Agenda for H2O Introduction Webinar ▪ Company Introduction (5 mins) ▪ H2O Introduction and Demo (35 mins) – Installation of H2O – Flight delay prediction use case • Use case description • Data set description • Data munging • Model creation ▪ Q&A (10 mins)
- 4. H2O AI Platform In-Memory, Distributed Machine Learning with Visual Intelligence H2O AI in Spark with Data Prep and ML Pipelines Operationalize Model Building and Deployment Governance. Best-of-breed GPU Deep Learning with easy API and AutoML TensorFlow, MXNet or Caffe and H2O Deep Water AI For Business Transformation Insights on Text, Images, Transactions, Speech Best Machine Learning Algorithms on Spark Platform to Build and Scale Data Products. Dual licensing (AGPL and Commercial) H2O is the #1 Platform for Open Source AI
- 5. Open Source Drives Community Adoption Companies Using H2O.ai 2014 2015 2016 2017 9173 6427 3810 400 H2O.ai Users 2014 2015 2016 2017 83108 54163 38257 1000 * Data from July of every year, except for 2017 when data from Feb 21st are used.
- 6. H2O Recognized by Press and Customers
- 7. H2O.ai Strongly Positioned in Key Analyst Reports and Press “Overall customer satisfaction is very high.” “H2O is especially suited to IoT edge and device scenarios.” “H2O had the highest reference customer analytics support score of all the vendors.” H2O.ai is a Visionary in the Gartner Magic Quadrant for Data Science Platforms “H2O.ai has significant adoption by large enterprises such as Macy’s, Comcast, and Capital One.” “H2O.ai is best known for developing open source, cluster-distributed ML algorithms at a time (2011) when big data demanded them, but no one else had them.” H2O.ai is a Strong Performer in the Forrester Predictive Analytics & Machine Learning H2O.ai is a Top 10 Hot Artificial Intelligence (AI) Technologies on Forbes H2O.ai named alongside Nvidia, Google, IBM, Intel, Microsoft, SAS, et al as in Top 10 Hot Artificial Intelligence (AI) on Forbes - contributed by Gil Press
- 8. H2O Use Cases – Videos and Talks Auto Insurance UBI Telematics Commercial Insurance Risk Analytics Financial Services Customer Insights Digital Marketing Consumer Behavior Pawan Divarkarla Chief Data Officer “H2O is an enabler in how people are thinking about data.” Conor Jensen Analytics Director “Advanced analytics was one of the key investments we decided to make.” Brendan Herger Data Scientist “H2O is the best solution to to iterate very quickly on large datasets and produce meaning models.” Satya Satyamoorthy Director, Software Dev "I am a big fan of open source. H2O is the best fit in terms of cost as well as ease of use and scalability and usability.” Play Video Play Video Play Video Play Video Progressive Zurich Capital One Nielsen Catalina
- 9. Amy Wang Math Hacker, H2O.ai
- 10. What is H2O? Open%source%in,memory%prediction%engineMath%Platform • Parallelized%and%distributed%algorithms%making%the%most%use%out%of% multithreaded%systems • GLM,%Random%Forest,%GBM,%PCA,%etc. Easy%to%use%and%adoptAPI • Written%in%Java%– perfect%for%Java%Programmers • REST%API%(JSON)%– drives%H2O%from%R,%Python,%Excel,%Tableau More%data?%Or%better%models?%BOTHBig%Data • Use%all%of%your%data%– model%without%down%sampling • Run%a%simple%GLM%or%a%more%complex%GBM%to%find%the%best%fit%for%the%data • More%Data%+%Better%Models%=%Better%Predictions
- 11. Supervised Learning H2O Algorithms Statistical Analysis Ensembles Deep Neural Networks • Generalized Linear Models: Binomial, Gaussian, Gamma, Poisson, and Tweedie • Naive Bayes: Binary Text Classification • Distributed Random Forest: Classification or Regression Models • Gradient Boosting Machine: Ensembles of shallow decision trees with increasing refined approximations • Deep Learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations
- 12. Unsupervised Learning Clustering Dimensionality Reduction Anomaly Detection • K-means: Partition observations into k clusters of the same spatial size. Categorical features are one hot encoded. • Archetypes [GLRM]: Partition observations into k archetypes. • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Model: Approximates data set as a product of two low dimensional factors. Extends PCA to handle sparse data, categorical data, and adds regularization. • Autoencoders [Deep Learning]: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations H2O Algorithms
- 13. Accuracy with Speed and Scale HDFS S3 SQL NoSQL Classification Regression Feature Engineering In-Memory Map Reduce/Fork Join Columnar Compression Deep Learning PCA, GLM, Cox Random Forest / GBM Ensembles Fast Modeling Engine Streaming Nano Fast Java Scoring Engines Matrix Factorization Clustering Munging
- 14. Reading Data into H2O with R STEP 1 R user h2o_df = h2o.importFile(“../data/allyears2k.csv”)
- 15. Reading Data from HDFS into H2O with R H2O H2O H2O data.csv HTTP REST API request to H2O has HDFS path H2O ClusterInitiate distributed ingest HDFS Request data from HDFS STEP 2 2.2 2.3 2.4 R h2o.importFile() 2.1 R function call
- 16. Reading Data from HDFS into H2O with R H2O H2O H2O R HDFS STEP 3 Cluster IP Cluster Port Pointer to Data Return pointer to data in REST API JSON Response HDFS provides data 3.3 3.4 3.1h2o_df object created in R data.csv h2o_df H2O Fram e 3.2 Distributed H2O Frame in DKV H2O Cluster
- 17. Data Munging in R
- 18. Installing in R Installing in Python R> install.packages(“h2o”) Terminal$ pip install h2o
- 19. Demo Time!
- 20. Questions?
- 21. Thanks for joining us!