Row 8890
Content Data
This page contains data entry 8890 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
I think these are roles that are still being defined. Maybe will fade in and out of existence. Every organization that wants to use ML effectively: has to figure out how to move data in such a way that someone can do research without affecting operations (not easy if all of the data is in operational data stores), has to have clear goals about what their ML will achieve and why it isn't possible with non-ML-driven software, and has to plan for deployment and post-deployment operations of models.
Most companies have planned for zero out of three of these and very few have planned for all three.
| Field | Value |
|---|---|
| text | I think these are roles that are still being defined. Maybe will fade in and out of existence. Every organization that wants to use ML effectively: has to figure out how to move data in such a way that someone can do research without affecting operations (not easy if all of the data is in operational data stores), has to have clear goals about what their ML will achieve and why it isn't possible with non-ML-driven software, and has to plan for deployment and post-deployment operations of models.… |
| label | r/datascience |
| dataType | comment |
| communityName | r/datascience |
| datetime | 2024-05-20 |
| username_encoded | Z0FBQUFBQm5Lakw0cUZDZ29WQW1fVVRHVFFaQVZSd3YyTzFlaXF1WklpNHJ1bE4waGJoX09uN09udjc0cF80VmdTSkxxQ0NaaElONDFnRmNTeE5majhybnpBeHVQamxITVE9PQ== |
| url_encoded | Z0FBQUFBQm5Lak9IRktLV19iTVEyUGJZZWZwWVhLT1JFWmlteU1vd1hrWC1yVUIxV3R0OF9QVDg5bWViUDV6N240QWZQbXpCNkE0Q1k3NUFVM0dYNkRGeGhzOXBQeVo1MktiTXZ1X1BEMktuaWZJZGFQWC1TNjJacS1KYVNzU3VkNjdqUVNNZzRnMzllWnk1bmZ4Nktvb1VmLTFjT1NZQkN3Z05HUERDYUFGb0ppaEFGaWFRV0puWU9ONWxSY2Jhc1lvc0t3SVl3b3V0NXpDSllBbUJhcllFcDI3MEk3dzlPZz09 |
Raw Record
{
"text": "I think these are roles that are still being defined. Maybe will fade in and out of existence. Every organization that wants to use ML effectively: has to figure out how to move data in such a way that someone can do research without affecting operations (not easy if all of the data is in operational data stores), has to have clear goals about what their ML will achieve and why it isn't possible with non-ML-driven software, and has to plan for deployment and post-deployment operations of models. \n\nMost companies have planned for zero out of three of these and very few have planned for all three.",
"label": "r/datascience",
"dataType": "comment",
"communityName": "r/datascience",
"datetime": "2024-05-20",
"username_encoded": "Z0FBQUFBQm5Lakw0cUZDZ29WQW1fVVRHVFFaQVZSd3YyTzFlaXF1WklpNHJ1bE4waGJoX09uN09udjc0cF80VmdTSkxxQ0NaaElONDFnRmNTeE5majhybnpBeHVQamxITVE9PQ==",
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}
Entry Information
- Entry ID: 8890
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000