32 分鐘
Power BI & More: Is your Power Platform data ready for Data Science/Machine Learning? Power BI & More
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- 科技
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.
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.
32 分鐘