Managing Enterprise Data Science 201904
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This presentation anchors best practices for Enterprise Data Science based on Microsoft's "Team Data Science Process". The talk includes introducing the concepts, describing some real-world advice for project planning, and discusses typical titles of professionals who make enterprise data science successful. These techniques also apply for AI (artificial intelligence), deep learning, machine learning, and advanced analytics.
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Managing Enterprise Data Science 201904
- 1. Managing Enterprise Data Science • Mark Tabladillo Ph.D. • Cloud Solution Architect • Microsoft • April 12, 2019 This Photo by Unknown Author is licensed under CC BY-SA-NC
- 4. Domain specific pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Familiar Data Science tools To simplify model development CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge Azure Notebooks JupyterVisual Studio Code Command line
- 7. Outline Examine Each Phase of Team Data Science Process Examine Project Management Examine Project Personas
- 8. TDSP Phases
- 9. Data Science Lifecycle •Primary phases: Lifecycle
- 10. Data Science Lifecycle •Primary phases: Lifecycle
- 11. Business Understanding Study AI Applications Assess AI Maturity Identify Use Cases and Personas
- 12. Data Acquisition and Understanding Identify Possible Data Sources Identify Subject Matter Experts Evaluate ETL Technologies
- 18. Time Science One Envisioning Scoping Science Two Science Three Science Four
- 19. TDSP Personas
- 20. Team Data Science Process – Personas Persona Needs Account Executive • Effective planning and expectation setting with CxO level, directors, technical leaders • Integrating TDSP into a solutions conversation Program Manager/Engagement Manager • Resource Planning and Assignment • Project Progress Tracking • Communicating an effective narrative throughout the project lifecycle Solution Architect • Architecture Development • Technology Selection • Central role of Data as a TDSP process driver Data Scientist/Data Engineer • Understanding the major categories of the TDSP • Understanding the latitude for choosing technologies • Understanding the ability to generate and manage tasks under the major TDSP categories Application Developer • Integrating data science into a new or preexisting application • Understanding the two types of developer roles • Those directly participating in the TDSP iterations • Those just providing input and consuming output DevOps • Understanding how TDSP may be invoked in a production environment, either on a planned or unscheduled basis • How to work effectively with data scientists • Optimizing and improving monitoring of production models
- 21. Resources • Team Data Science Process https://docs.microsoft.com/en- us/azure/machine-learning/team-data-science-process/ • Azure DevOps https://docs.microsoft.com/en- us/azure/devops/?view=azure-devops • AI Business School https://www.microsoft.com/en-us/ai/ai-business-school
- 22. Summary Examine Each Phase of Team Data Science Process Examine Project Management Examine Project Personas
- 23. Connect with Mark Tabladillo Linked In Twitter @marktabnet