Operationalizing Machine Learning
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This document presents a comprehensive overview of operationalizing machine learning (ML) in software applications, specifically comparing traditional software approaches to ML solutions. It highlights the advantages of ML, such as generalization and solving complex problems that traditional software cannot, and discusses key aspects of deploying ML models, including data preparation and model selection. The document also outlines the costs associated with custom ML models and references methodologies like CRISP-DM for data science.
Operationalizing Machine Learning
- 1. Operationalizing Machine Learning 5/24/2017 Taylor Howard
- 2. Presenter C. Taylor Howard Data Analytics & Collaboration taylor.howard@agilethought.com
- 3. Agenda 10 Minutes of Content & 5 for Minutes for Questions 3 • Traditional software systems versus ML & AI Solutions Let’s apply some definitions for clarity. Why would we want to invest in ML? • What do I want for lunch? A simple example demonstrating the benefits, challenges, and operational considerations of ML versus traditional software. • Machine Learning in the field What do we need to know about deploying a solution with ML? • Where do I begin and what is this going to cost me? Adding ML can be very cost effective when using trained models, but what about training bespoke models for custom needs?
- 4. Definitions 4 Artificial Intelligence Encompasses all approaches to simulate human intelligence. General AI is the goal. Machine Learning Algorithmic approach to parse data, learn from it, and make predictions. Deep Learning Massive artificial neural networks targeting narrow AI. Source – A great article by Michael Copeland https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
- 5. Why Machine Learning? 5 • Many problems do not require ML. • Where there is overlap, the ML offers generalization. • To the far right are problem domains that traditional software cannot solve, e.g., speech recognition, computer vision, etc. Traditional Software Machine Learning Descriptive Predictive
- 6. Version 3 Provide recommendations based on past order history Version 2 Order history remembered for quick reordering Lunchtime Ordering App Integrating machine learning with a traditional app 6 Version 1 Users can order lunch Cuisine? American Italian Indian French Menu? Hamburger Fries Chicken Application Development Methodology (Scrum) Data Science Methodology (CRISP-DM) =
- 7. Version 3 Provide recommendations based on past order history Lunchtime Ordering App Machine learning versus conventional approaches 7 Non-ML Approach – Considerations Simple list of whatever a user orders the most is what is recommended What was ordered most recently is what is recommended Recommend based on price Mine reviews from Yelp and recommend based on user reviews Recommended based on location ? ML Approach There is no need for us to programmatically try to understand all the relationships between the influencing factors that go into making a lunch determination. We will let Machine Learning determine this for us, but we need to provide the inputs – these are called Features.
- 8. Lunchtime Ordering App Machine learning observations, features & labels 8 Features are an individual property being observed that be believe will have predictive power. External features can have significance The label informs our algorithm of the correct result we seek to predict
- 9. Lunchtime Ordering App Machine learning training & model selection 9 Multiclass neural network Accuracy, long training times Multiclass logistic regression Fast training times, linear model Multiclass decision forest Accuracy, fast training times Multiclass decision jungle Accuracy, small memory footprint A good article on model performance – Accuracy, Precision, Recall https://blogs.msdn.microsoft.com/andreasderuiter/2015/02/09/performance-measures-in-azure-ml-accuracy-precision-recall-and-f1-score/
- 10. Lunchtime Ordering App Deploying our model and predicting where to eat! 10 Multiclass decision forest Accuracy, fast training times Observation Day of Week Time of Day Ordered Recently Distance Vegan Option Calories Cuisine Price Yelp Rating Weather 1 Wednesday 12:30 PM No 0 - 5 Miles Yes Medium American $$ 4 Clear 2 Wednesday 12:30 PM No 15 - 25 Miles Yes High Indian $ 5 Clear 3 Wednesday 12:30 PM No 6 - 10 Miles Yes Medium American $$$ 4 Clear 4 Wednesday 12:30 PM No 0 - 5 Miles Yes Medium Italian $ 4 Clear 5 Wednesday 12:30 PM No 15 - 25 Miles Yes High American $$ 5 Clear 6 Wednesday 12:30 PM Yes 6 - 10 Miles Yes High Italian $$$ 5 Clear 7 Wednesday 12:30 PM No 0 - 5 Miles No Medium American $ 4 Clear 8 Wednesday 12:30 PM No 15 - 25 Miles Yes Low American $$ 4 Clear 1 2 3 Observation Scored Label Scored Probobablites 1 High 0.92 2 High 0.80 3 Medium 0.94 4 Medium 0.70 5 High 0.80 6 Low 0.95 7 Low 0.80 8 High 0.50
- 11. What does this cost? 11 Custom Model - ML • Azure ML • Microsoft R Server • Google TensorFlow • Amazon Machine Learning • Big Data – Spark R 1-4 Months Prebuilt Intelligence APIs Microsoft Azure Cognitive Services Google Cloud Prediction IBM Watson APIs 4-12+ Months Deep Learning • Microsoft Cognitive Toolkit (CNTK) • Google TensorFlow • Custom Algorithm Neural Network 6-18+ Months
- 12. Observation User Age Income Gender Day of Week Time of Day Satisfaction 1 Jeff 44 50 - 75k Male Thursday 11:00 AM High 2 Jeff 44 50 - 75k Male Friday 1:00 PM Low 3 Jeff 44 50 - 75k Male Friday 1:00 PM Medium 4 Tony 43 75 - 100k Male Monday 12:30 PM Medium 5 Tony 43 75 - 100k Male Tuesday 12:30 PM High 6 Tony 43 75 - 100k Male Friday 12:00 PM Low 7 Jill 28 75 - 100k Female Friday 11:30 AM High 8 Jill 28 75 - 100k Female Friday 2:00 PM High … N - Lunchtime Ordering App Bonus section – Unsupervised learning (clustering) 12 Imagine adding demographic features to our data set. The label informs our algorithm of the correct result we seek to predict What type of clusters do we see for users that are highly satisfied? {Female, 24-30, 75-100k} Perhaps an ad campaign?
- 13. Thank You www.agilethought.com linkedin.com/company/AgileThought @AgileThought C. Taylor Howard Director of Data Analytics & Collaboration taylor.howard@agilethought.com www.agilethought.com 727.248.2478 13
- 14. Data Analytics CRISP Methodology 14 • Business Understanding This initial phase focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition, and a preliminary plan designed to achieve the objectives. • Data Understanding The data understanding phase starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information. • Data Preparation The data preparation phase covers all activities to construct the final dataset. This data will be fed into the modeling tools from the initial raw data. Data preparation tasks are likely to be performed multiple times. • Modeling Modeling techniques are selected and applied, and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Stepping back to the data preparation phase is often needed.
- 15. AzureMLAlgorithms 15 Azure ML Cheat Sheet https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet
- 16. Additional and Links • Scrum Software Development https://en.wikipedia.org/wiki/Scrum_(software_development) • CRISP-DM, Cross Industry Standard Process for Data Mining https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining
Editor's Notes
- #15: CRISP?