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1 Battle Camp S1: Reality Rivalries with Dana Moon & QT 1:00:36
Power BI & More: Is your Power Platform data ready for Data Science/Machine Learning?
Manage episode 248013320 series 2582622
In this episode (brought to you by mscrm-addons.com), Matt Lamb, Data Science and Commercial Analytics Lead at eLogic, rejoins the podcast to discuss what we need from our Dynamics 365 implementation when stepping into Machine Learning and AI. What do we need from our Dynamics 365 data in terms quantity and completeness to get effective results? What are the ways to deal with incomplete and what consequences does it have on your Machine Learning results when you make even simple updates to your business processes. In order to create a record set to use as a base for Machine Learning, you may not need as many records as you think, but need to strike the right balance of quantity and quality.
In this episode we discuss:
o How many records are really needed for effective machine learning?
o What structure and maturity level of data is needed?
o Supervised vs. Unsupervised Learning
o How many people does Matt’s dog need to meet?
o What happens with your algorithms when you make changes to your business process?
o Tips to make your data scientist happy
Got questions or suggestions for future episode? Email voice@crm.audio.
This episode is a production of Dynamic Podcasts LLC.
23 פרקים
Manage episode 248013320 series 2582622
In this episode (brought to you by mscrm-addons.com), Matt Lamb, Data Science and Commercial Analytics Lead at eLogic, rejoins the podcast to discuss what we need from our Dynamics 365 implementation when stepping into Machine Learning and AI. What do we need from our Dynamics 365 data in terms quantity and completeness to get effective results? What are the ways to deal with incomplete and what consequences does it have on your Machine Learning results when you make even simple updates to your business processes. In order to create a record set to use as a base for Machine Learning, you may not need as many records as you think, but need to strike the right balance of quantity and quality.
In this episode we discuss:
o How many records are really needed for effective machine learning?
o What structure and maturity level of data is needed?
o Supervised vs. Unsupervised Learning
o How many people does Matt’s dog need to meet?
o What happens with your algorithms when you make changes to your business process?
o Tips to make your data scientist happy
Got questions or suggestions for future episode? Email voice@crm.audio.
This episode is a production of Dynamic Podcasts LLC.
23 פרקים
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1 Customer Insights with Matt Wittemann 41:42

1 Matt and Ulrik make unsupervised product recommendation engines 51:28

1 Predictions and Analytics on Field Service 35:42

1 Embedded experience and Power BI April 2019 Update 31:41

1 Power BI & More: Is your Power Platform data ready for Data Science/Machine Learning? 32:51


1 Dynamics 365 interactive charts and dashboards 31:41

1 Security and User Adoption Reporting 25:38

1 Introducing Dataflows with Scott Sewell 37:38

1 What's new in the August 2018 update 36:23




1 Analyzing sales and business process data 30:25

1 Questions from the world tour and CDS for Analytics 33:57

1 Enhanced D365 hyperlinks and hidden entities 33:00


1 What's new with the Dynamics 365 Data Export Service 41:41


1 CRM Power BI Viewer with Trond Aarskog 28:57


1 Getting Dynamics 365 data into Power BI 20:00

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