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AutoML: Helping to Bridge Skills Gap Between Data Enthusiasts & Data Scientists
- 1. AUTOML: HELPING TO BRIDGE SKILLS GAP BETWEEN DATA ENTHUSIASTS & DATA SCIENTISTS By Josh Janzen – Data Scientist
- 2. AUTOML: AGENDA What is ML What is AutoML Animated Visualizations of ML vs AutoML AutoML code demos on Titanic Dataset Comparison of AutoML tools available
- 3. WHERE I LIVE IN TERMS OF DATA SCIENCE TOOLS
- 4. THE EMERGING FIELD OF DATA SCIENCE “The more I learn, the more I realize how much I don’t know.”
- 5. EXAMPLE OF ML (MACHINE LEARNING) 1. Gather historical data (years of weather, were you cold, hot, comfortable, activity levels, did you wear a coat) 2. Apply algorithm to learn relationships (learn impact of weather, activity, coat, to determine were you comfortable) 3. Predict on new data/future (is it a good idea to where a coat today?) NO. 95.2% chance of being comfortable by not wearing a coat Example: should I wear a coat today? ML finds relationships in large datasets to help us understand patterns and make predictions what is ML video
- 6. 1. There is a place for business analysis, and a place for ML. They are very different 2. ML is another tool to help drive value from data, especially with large, complex datasets 3. Both ML & Analysis need an understanding of the business to be successful 4. ML can’t solve every data problem 5. ML is a vast and growing field ML != Analysis ML is another tool to help further drive value from data. WHAT IS ML (MACHINE LEARNING)
- 7. WHAT IT ISN’TWHAT IT IS AUTOML • “Tools to make Data Scientists more efficient” • “..Data Science democratization” • AutoML makes ML available to the Data Enthusiasts • Simplifies the Machine Learning model building process by applying Computer Science and Statistical techniques to find an optimal model in an efficient amount of time. • The silver bullet to make all ML better • A proven to produce better results than a very experienced ML Data Scientist • Simpler and easier to use (at least not yet) • Another data buzz word like “big data”
- 8. WHAT IS A DATA SCIENCE ENTHUSIAST • The software developer who wants to try ML • The college student with aspirations to be a DS • The mid-level MGR looking to up their game • The analyst looking to differentiate their skills • Do not need advanced MATH & STATS skills Estimated from multiple sources including https://www.kdnuggets.com/2018/09/how-many-data-scientists-are- there.html
- 9. ML VS. AUTOML > > Prediction Score: 0.752 ML without AutoML Credit: Josh Janzen Data Scientist Visualize and Structure the Dataset Import Data Work w/Business to Identify Opportunity
- 10. ML VS. AUTOML > > Prediction Score: 0.752 ML without AutoML Credit: Josh Janzen Data Scientist Visualize and Structure the Dataset Import Data - Missing values - Outlier handling - Checking variable types Preprocessing - Feature selection - Feature transformation Feature Engineering - Split data train, valid, test - Import ML libraries - Try various algorithm(s) - Score models Partition Data & Model Selection - Evaluate model - Tune hyper parameters Model Tuning - Save best model - Run to make predictions Predict on New Data >> Prediction Score: 0.752 ML with AutoML Credit: Josh Janzen Data Scientist Visualize and Structure the Dataset Import Data Work w/Business to Identify Opportunity Work w/Business to Identify Opportunity SME
- 11. ML VS. AUTOML > > Prediction Score: 0.752 ML without AutoML Credit: Josh Janzen Data Scientist Visualize and Structure the Dataset Import Data - Missing values - Outlier handling - Checking variable types Preprocessing - Feature selection - Feature transformation Feature Engineering - Split data train, valid, test - Import ML libraries - Try various algorithm(s) - Score models Partition Data & Model Selection - Evaluate model - Tune hyper parameters Model Tuning - Save best model - Run to make predictions Predict on New Data >> Prediction Score: 0.752 ML with AutoML Credit: Josh Janzen Data Scientist Visualize and Structure the Dataset Import Data Work w/Business to Identify Opportunity Work w/Business to Identify Opportunity
- 12. ML VS. AUTOML > >> > Prediction Score: 0.752 - Missing values - Outlier handling - Checking variable types Preprocessing - Feature selection - Feature transformation Feature Engineering - Split data train, valid, test - Import ML libraries - Try various algorithm(s) - Score models Partition Data & Model Selection - Evaluate model - Tune hyper parameters Model Tuning - Save best model - Run to make predictions Predict on New Data ML without AutoML Credit: Josh Janzen Data Scientist Visualize and Structure the Dataset Import Data Prediction Score: 0.752 ML with AutoML Credit: Josh Janzen Data Scientist Visualize and Structure the Dataset Import Data - Automatically build and evaluate 100s of models - Review performance and variable importance Work w/Business to Identify Opportunity Work w/Business to Identify Opportunity
- 13. DEMO WITH TITANIC DATASET • Ipython notebook: https://github.com/donnemartin/data-science- ipython- notebooks/blob/master/kaggle/titanic.ipynb • Run MLBox from command line • Demo AutoML App • Show Azure AutoML tool
- 17. AZURE ML RESULTS PROS: • No code to write • Lots of investment from Microsoft in this space CONS: • Slower than expected, took about 20 min • No easy way to create new predictions • Process not polished, easy to use as expected
- 18. NEXT FRONTIER: AUTO FEATURE ENGINEERING Source: https://towardsdatascience.com/feature-engineering-what-powers-machine-learning- 93ab191bcc2d
- 19. THE END Questions? Deck will be posted on my blog www.JoshJanzen.com