The Art of Intelligence – A Practical Introduction Machine Learning for Oracle professionals (NLOUG Tech Experience 2017)
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The document provides an introduction to machine learning specifically tailored for Oracle professionals, covering its relevance, key algorithms, and the data science workflow. It highlights the importance of machine learning in analyzing historical data to predict outcomes, discusses various types of learning, and outlines the tools and technologies involved, including Apache Spark and Oracle's offerings. The text emphasizes that machine learning is increasingly feasible and accessible due to advancements in technology and affordable resources.
![AGENDA
• What is Machine Learning?
• Why could it be relevant [to you]?
• What does it entail?
• With which algorithms, tools and technologies?
• Oracle and Machine Learning?
• How do you embark on Machine Learning?](https://image.slidesharecdn.com/theartofmachinelearning-techexperience15june2017-170615141656/85/The-Art-of-Intelligence-A-Practical-Introduction-Machine-Learning-for-Oracle-professionals-NLOUG-Tech-Experience-2017-2-320.jpg)
![MACHINE LEARNING
• Analyze Historical Data (input and result – training set) to discover
Patterns & Models
• Iteratively apply Models to [additional] Input (test set) and compare
model outcome with known actual result to improve the model
• Use Model to predict
outcome for
entirely new data
4](https://image.slidesharecdn.com/theartofmachinelearning-techexperience15june2017-170615141656/85/The-Art-of-Intelligence-A-Practical-Introduction-Machine-Learning-for-Oracle-professionals-NLOUG-Tech-Experience-2017-4-320.jpg)
![OPEN DATA – SOME EXAMPLES
• Kaggle - Data Sets and [Samples of] Data Discovery: www.kaggle.com
• US, EU and UK Government Data: data.gov, open-data.europa.eu and data.gov.uk
• Dutch Government Data: data.overheid.nl (plus CBS, RDW, individual cities, …)
• Open Images Data Set: www.image-net.org
• Open Data From World Bank: data.worldbank.org
• Historic Football Data: api.football-data.org
• Detroit Open Data Portal: data.detroitmi.gov
• Airports, Airlines, Flight Routes: openflights.org
• Open Database – machine counterpart to Wikipedia: www.wikidata.org
• Google Audio Set (manually annotated audio events) - research.google.com/audioset/
• Movielens - Movies, viewers and ratings: files.grouplens.org/datasets/movielens/](https://image.slidesharecdn.com/theartofmachinelearning-techexperience15june2017-170615141656/85/The-Art-of-Intelligence-A-Practical-Introduction-Machine-Learning-for-Oracle-professionals-NLOUG-Tech-Experience-2017-31-320.jpg)
![MANY CLOUD SERVICES AROUND BIG DATA &
[PREDICTIVE] ANALYTICS & MACHINE LEARNING
39](https://image.slidesharecdn.com/theartofmachinelearning-techexperience15june2017-170615141656/85/The-Art-of-Intelligence-A-Practical-Introduction-Machine-Learning-for-Oracle-professionals-NLOUG-Tech-Experience-2017-39-320.jpg)
The Art of Intelligence – A Practical Introduction Machine Learning for Oracle professionals (NLOUG Tech Experience 2017)
- 1. THE ART OF INTELLIGENCE – A PRACTICAL INTRODUCTION MACHINE LEARNING FOR ORACLE PROFESSIONALS Lucas Jellema (CTO AMIS & Oracle ACE Director) 15th June 2017, TechExperience 2017, Amersfoort, The Netherlands
- 2. AGENDA • What is Machine Learning? • Why could it be relevant [to you]? • What does it entail? • With which algorithms, tools and technologies? • Oracle and Machine Learning? • How do you embark on Machine Learning?
- 3. LEARNING • How do we learn? • Try something (else) => get feedback => learn • Eventually: • We get it (understanding) so we can predict the outcome of a certain action in a new situation • Or we have experienced enough situations to predict the outcome in most situations with high confidence • Through interpolation, extrapolation, etc. • We remain clueless 3
- 4. MACHINE LEARNING • Analyze Historical Data (input and result – training set) to discover Patterns & Models • Iteratively apply Models to [additional] Input (test set) and compare model outcome with known actual result to improve the model • Use Model to predict outcome for entirely new data 4
- 5. WHY IS IT RELEVANT (NOW)? • Data • big, fast, open • Machine Learning has become feasible and accessible • Available • Affordable (software & hardware) • Doable (Citizen Data Scientist) • Fast enough • Business Cases & Opportunities => Demands • End users, Consumers, Competitive pressure, Society
- 6. WHY IS IT RELEVANT (NOW)?
- 7. EXAMPLE USE CASES • Speech recognition • Identify churn candidates • Intent & Sentiment analysis on social media • Upsell & Cross Sell • Target Marketing • Customer Service • Chat bots & voice response systems • Predictive Maintenance • Gaming • Captcha • Medical Diagnosis • Anomaly Detection (find the odd one out) • Autonomous Cars • Voter Segment Analysis • Customer Recommendations • Smart Data Capture • Face Detection • Fraud Prevention • (really good) OCR • Traffic light control • Navigation • Should we investigate | do lab test? • Spam filtering • Propose friends | contacts • Troll detection • Auto correct • Photo Tagging and Album organization
- 8. THE DATA SCIENCE WORKFLOW • Set Business Goal – research scope, objectives • Gather data • Prepare data • Cleanse, transform (wrangle), combine (merge, enrich) • Explore data • Model Data • Select model, train model, test model • Present findings and recommend next steps • Apply: • Make use of insights in business decisions • Automate Data Gathering & Preparation, Deploy Model, Embed Model in operational systems
- 9. DATA DISCOVERY 9 A B C D E F G 1104534 ZTR 0.1 anijs 2 36 T 631148 ESE 132 rivier 0 21 S -3 WGN 71 appel 0 1 - 1262300 ZTR 56 zes 2 41 T 315529 HVN 1290 hamer 0 11 - 788914 ASM 676 zwaluw 0 26 T 157762 HVN 9482 wie 0 6 - 946681 DHG 42 rond 1 31 T -31539 WGN 2423 bruin 0 0 - 47338 HVN 54 hamer 0 16 P
- 10. SCATTER PLOT ATTRIBUTE F (Y-AXIS)VS ATTRIBUTE A 10 0 5 10 15 20 25 30 35 40 45 -200000 0 200000 400000 600000 800000 1000000 1200000 1400000 Y-Values Y-Values
- 11. SCATTER PLOT ATTRIBUTE F (Y-AXIS)VS ATTRIBUTE A 11 0 5 10 15 20 25 30 35 40 45 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Age of Lucas Jellema vs Year Y-Values
- 12. DATA DISCOVERY – ATTRIBUTES IDENTIFIED 12 Time City - - #Kids Age Level of Education 1104534 ZTR 0.1 anijs 2 36 T 631148 ESE 132 rivier 0 21 S -3 WGN 71 appel 0 1 - 1262300 ZTR 56 zes 2 41 T 315529 HVN 1290 hamer 0 11 - 788914 ASM 676 zwaluw 0 26 T 157762 HVN 9482 wie 0 6 - 946681 DHG 42 rond 1 31 T -31539 WGN 2423 bruin 0 0 - 47338 HVN 54 hamer 0 16 P
- 13. TYPES OF MACHINE LEARNING • Supervised • Train and test model from known data (both features and target) • Unsupervised • Analyze unlabeled data – see if you can find anything • Semi-Supervised • Interactive flow, for example human identifying clusters • Reinforcement • Continuously improve algorithm (model) as time progresses, based on new experience
- 14. MACHINE LEARNING ALGORITHMS • Clustering • Hierarchical k-means, Orthogonal Partitioning Clustering, Expectation-Maximization • Feature Extraction/Attribute Importance/Principal Component Analysis • Classification • Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, Support Vector Machine • Regression • Multiple Regression, Support Vector Machine, Linear Model, LASSO, Random Forest, Ridgre Regression, Generalized Linear Model, Stepwise Linear Regression • Association & Collaborative Filtering (market basket analysis , apriori) • Neural network and Deep Learning with Deep Neural Network • Can be used for many different use cases
- 15. MODELING PHASE • Select a model to try to create a fit with (predict target well) • Set configuration parameters for model • Divide data in training set and test set • Train model with training set • Evaluate performance of trained model on the test set • Confusion matrix, mean square error, support, lift, false positives, false negatives • Optionally: tweak model parameters, add attributes, feed in more training data, choose different model • Eventually (hopefully): pick model plus parameters plus attributes that will reliably predict the target variable given new data
- 16. OPTICAL DIGIT RECOGNITION Predicted Actual 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 Naïve Bayes Decision Tree
- 17. CLASSIFICATION GONE WRONG • Machine learning applied to millions of drawings on QuickDraw • to classify drawings • For example: drawings of beds • See for example: • https://aiexperiments.withgoogle.com/quick-draw
- 18. MACHINE LEARNING OPERATIONAL SYSTEMS • “We have a model that will choose best chess move based on certain input”
- 19. MACHINE LEARNING OPERATIONAL SYSTEMS • Discovery => Model => Deploy • “We have a model that will predict a class (classification) or value (regression) based on certain input with a meaningful degree of accuracy” – how can we make use of that model?
- 20. DEPLOY MODEL AND EXPOSE • Model is usually created on Big Data in Data Science environment using the Data Scientist’s tools • Model itself is typically fairly small • Model will be applied in operational systems against single data items (not huge collections nor the entire Big Data set) • Running the model online may not require extensive resources • Implementing the model at production run time • Export model (from Data Scientist environment) and import (into production environment) • Reimplement the model in the development technology and deploy (in the regular way) to the production environment • Expose model through API
- 21. DEPLOY MODEL AND EXPOSE REST API
- 22. MODEL MANAGEMENT • Governance (new versions, testing and approval) • A/B testing • Auditing (what did the model decide and why? notifying humans? ) • Evaluation (how well did the model’s output match the reality) to help evolve the model • for example recommendations followed • Monitor self learning models (to detect rogue models)
- 23. DEPLOYMENT CAN ALSO BE: LOAD RESULTS FROM MODEL INTO PRODUCTION
- 24. WHAT TO DO IT WITH? • Mathematics (Statistics) • Gauss (normal distribution) • Bayes’ Theorem • Euclidean Distance • Perceptron • Mean Square Error
- 25. WHAT TO DO IT WITH?
- 26. HOW TO PICK TOOLS FOR THE JOB • What are the jobs? • Gather data • Prepare data • Explore and (hopefully) Discover • Present • Embed & Deploy Model • What are considerations? • Volume • Speed and Time • Skills • Platform
- 27. POPULAR TOOLS
- 28. NOTEBOOK – THE LAB JOURNAL FROM THE DATALAB • Common format for data exploration and presentation • User friendly interface on top of powerful technologies • Most popular implementations • Jupyter (fka IPython) • Apache Zeppelin • Spark Notebook • Beaker • SageMath (SageMathCloud => CoCalc) • Oracle Machine Learning Notebook UI
- 30. OPEN DATA • Governments and NGOs, scientific and even commercial organizations are publishing data • Inviting anyone who wants to join in to help make sense of the data – understand driving factors, identify categories, help predict • Many areas • Economy, health, public safety, sports, traffic & transportation, games, environment, maps, …
- 31. OPEN DATA – SOME EXAMPLES • Kaggle - Data Sets and [Samples of] Data Discovery: www.kaggle.com • US, EU and UK Government Data: data.gov, open-data.europa.eu and data.gov.uk • Dutch Government Data: data.overheid.nl (plus CBS, RDW, individual cities, …) • Open Images Data Set: www.image-net.org • Open Data From World Bank: data.worldbank.org • Historic Football Data: api.football-data.org • Detroit Open Data Portal: data.detroitmi.gov • Airports, Airlines, Flight Routes: openflights.org • Open Database – machine counterpart to Wikipedia: www.wikidata.org • Google Audio Set (manually annotated audio events) - research.google.com/audioset/ • Movielens - Movies, viewers and ratings: files.grouplens.org/datasets/movielens/
- 32. WHAT IS HADOOP? • Big Data means Big Computing and Big Storage • Big requires scalable => horizontal scale out • Moving data is very expensive (network, disk IO) • Rather than move data to processor – move processing to data: distributed processing • Horizontal scale out => Hadoop: distributed data & distributed processing • HDFS – Hadoop Distributed File System • Map Reduce – parallel, distributed processing • Map-Reduce operates on data locally, then persists and aggregates results
- 33. WHAT IS SPARK? • Developing and orchestrating Map-Reduce on Hadoop is not simple • Running jobs can be slow due to frequent disk writing • Spark is for managing and orchestrating distributed processing on a variety of cluster systems • with Hadoop as the most obvious target • through APIs in Java, Python, R, Scala • Spark uses lazy operations and distributed in-memory data structures – offering much better performance • Through Spark – cluster based processing can be used interactively • Spark has additional modules that leverage distributed processing for running prepackaged jobs (SQL, Graph, ML, …)
- 35. EXAMPLE RUNNING AGAINST SPARK • https://github.com/jadianes/spark-movie-lens/blob/master/notebooks/building-recommender.ipynb
- 36. WHAT IS ORACLE DOING AROUND MACHINE LEARNING? • Oracle Advanced Analytics in Oracle Database • Data Mining, Enterprise R • Text (ESA), Spatial, Graph • SQL
- 37. DEMONSTRATION OF ORACLE ADVANCED ANALYTICS • Using Text Mining and Naives Bayes Data Mining Classification • Train model for classifying conference abstracts into tracks • Use model to propose a track for new abstracts • Steps • Gather data • Import, cleanse, enrich, … • Prepare training set and test set • Select and configure model • Combining Text and Mining using Naive Bayes • Train model • Test and apply model
- 38. BIG DATA SQL ORACLE DATABASE AS SINGLE POINT OF ENTRY
- 39. MANY CLOUD SERVICES AROUND BIG DATA & [PREDICTIVE] ANALYTICS & MACHINE LEARNING 39
- 40. WHAT IS ORACLE DOING AROUND MACHINE LEARNING? • Big Data Discovery (fka Endeca) and BDD CS • Big Data Appliance • Data Vizualization Cloud • Analytics Clouds (Sales, Marketing, HCM) on top of SaaS • RTD – Real Time Decisions • DaaS • Oracle Labs (labs.oracle.com) • Machine Learning Research Group (link) • Machine Learning CS – “Oracle Notebook”
- 45. HUMANS LEARNING MACHINE LEARNING: YOUR FIRST STEPS • Jupyter Notebooks and Python – tmpnb.org • HortonWorks Sandbox VM – Hadoop & Spark & Hive, Ambari • DataBricks Cloud Environment with Apache Spark (free trial) • Oracle Big Data Lite – Prebuilt Virtual Machine • Tutorials, Courses (Udacity, Coursera, edX) • Books • Introducing Data Science • Learning Apache Spark 2 • Python Machine Learning
- 46. SUMMARY • Machine Learning by computers helps us(ers) understand historic data and apply that insight to new data • Through smart algorithms, advanced software and cheap and powerful compute and storage resources, Machine Learning is accessible to all • R and Python are most popular technologies for data exploration and ML model discovery • Apache Spark (on Hadoop) is frequently used to powercrunch data (wrangling) and run ML models on Big Data sets • Notebooks are a popular vehicle in the Data Science lab • To explore and report • Oracle researches, applies and exposes ML (Big Data SQL, OAA, OPC) • Getting started on Machine Learning is fun, smart and well supported
- 47. • Blog: technology.amis.nl • Email: [email protected] • : lucasjellema • : lucas-jellema • : www.amis.nl, [email protected] +31 306016000 Edisonbaan 15, Nieuwegein