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Operationalizing Data Science St. Louis Big Data IDEA
- 1. • Please mute your phone and turn off your video. There are over eighty people who don’t want to see or hear you chewing. • If you have any suggestions for future topics that you would like this group to cover, please send them to Scott Shaw using Webex’s chat feature. • We will send out the presentation deck after the meeting. Look for an announcement in the Meetup link for this meeting. • If you have questions during the presentation, also send them to Scott Shaw using Webex’s chat feature. We will get to as many questions as we can. Before we begin…
- 2. Operationalizing Data Science Adam Doyle Daugherty Business Solutions April 1, 2020
- 3. Thanks for coming out everyone!
- 4. April Fools!
- 5. Operationalizing Data Science Adam Doyle Daugherty Business Solutions April 1, 2020
- 6. Begin with the end in mind. Pause in the middle to make sure that you can get to where you are going. • What is the business intention that you are trying to achieve? • Minimize Cost • Maximize Return • Minimize Risk • Realize Opportunity • Engage Stakeholders • POC vs Production ready and valued product
- 7. Identify your thesis. Goal vs. intention SMART goal Refine to question that can be answered with data science Data science – predict, explain, evaluate Decision science – combination of data science and data engineering
- 8. Acquire data. Third-Party Data Internal API Streaming General – Amount, Access, Quality, Labeled? Third Party o Assess Data Quality (Value Range, Adherence, Representative) o Data Format (Automatic vs hand-generated, Similar data from different partners are vastly different) o Governed (Use appropriate – avoid reidentification, TTL, Contractuals, Track access, renewals) Internal API (Data size limits, unreliability, costs) Streaming (CDC, Device Data, Standardized?)
- 9. Explore the data. Data Exploration Statistical Relationships and Correlations Profiling Textual – Word, Stop Words, Bigram, Trigram Clustering Check in with SME
- 10. Every block of stone has a statue inside it, and it is the task of the sculptor to discover it. Cleanse data. Data profiling Deduplication Outliers Filter Imputation Source Corrections Data shaping Sort Project Enrichment
- 11. Create the model and features. Type of Models (Supervised, Unsupervised, Reinforcement Learning, Neural Networks) Feature Engineering (Transformations and Aggregations) Encode Indicator Variables Binning/Bucketing Sparse Classes Interaction Features Extract Elements (eg. Time) Normalization Feature Selection Testing your features Testing your model Check in with SME
- 12. Check in with Business Does what you’ve created address the concerns of the business?
- 13. Batch vs. Real-time? Batch Training vs Real-time for - Training - Evaluation
- 14. Evaluate the model. Accuracy Precision Recall MSE Alignment to Business
- 15. Deploy the model. Automation Scaling SLAs Versioning Data Pipelines Ongoing Data Acquisition Ongoing Data Cleaning Ongoing Feature Encoding Integration in application
- 16. Monitor the model. Drift Degrading the model Predictions and their effects
- 17. Optimize the model. Feature Optimization Retraining Remodeling
- 18. Conclusion What does it mean to be done? Explanation as a Result
- 19. Questions?
- 20. • https://www.dataengineeringpodcast.com/ • https://dataengweekly.com/ • https://www.logicalclocks.com/blog/feature-store-the-missing-data- layer-in-ml-pipelines • https://www.imperva.com/blog/deployment-isnt-the-final-step- monitoring-machine-learning-models-in-production/ Links
Editor's Notes
- Welcome. Introduction.
- Welcome. Introduction.
- What is the business intention that you are trying to achieve? Minimize Cost Maximize Return Minimize Risk Realize Opportunity Engage Stakeholders POC vs Production ready and valued product
- Decision science SMART goal Goal vs. intention Refine to question Data science – predict, explain, evaluate
- General – Amount, Access, Quality, Labeled? Third Party o Assess Data Quality (Value Range, Adherence, Representative) o Data Format (Automatic vs hand-generated, Similar data from different partners are vastly different) o Governed (Use appropriate – avoid reidentification, TTL, Contractuals, Track access, renewals) Internal API (Data size limits, unreliability, costs) Streaming (CDC, Device Data, Standardized?)
- Data Exploration Statistical Relationships and Correlations Profiling Textual – Word, Stop Words, Bigram, Trigram Clustering Check in with SME
- Data profiling Deduplication Outliers Filter Imputation Source Corrections Data shaping Sort Project Enrichment
- Type of Models (Supervised, Unsupervised, Reinforcement Learning, Neural Networks) Feature Engineering (Transformations and Aggregations) Encode Indicator Variables Binning/Bucketing Sparse Classes Interaction Features Extract Elements (eg. Time) Normalization Feature Selection Testing your features Testing your model Check in with SME
- Check in with Business Does what you’ve created address the concerns of the business?
- Batch Training vs Real-time Training Batch Evaluation vs Real-time Evaluation
- Truth Matrix Mean Square Error Evaluation time
- Automation Scaling SLAs Versioning Data Pipelines Ongoing Data Acquisition Ongoing Data Cleaning Ongoing Feature Encoding Integration in application
- Drift Degrading the model Predictions and their effects
- Feature Optimization Retraining Remodeling
- What does it mean to be done? Explanation as a Result