|
RAR-files |
| linkedin.learning.mlops.essentials.model.development.and.integration-xqzt.rar |
50,000,000 |
6016A1D8 |
| linkedin.learning.mlops.essentials.model.development.and.integration-xqzt.r00 |
50,000,000 |
5C8B611D |
| linkedin.learning.mlops.essentials.model.development.and.integration-xqzt.r01 |
50,000,000 |
8C202D43 |
| linkedin.learning.mlops.essentials.model.development.and.integration-xqzt.r02 |
17,215,650 |
F46DBB18 |
|
| Total size: |
167,215,650 |
|
|
| Archived
files |
|
01.01-getting_started_with_mlops.mkv
[4035429368128f35]
|
6,358,136 |
9743E61B |
|
01.02-scope_and_prerequisites.mkv
[4e32aec3022587e]
|
6,428,066 |
D9ED6540 |
|
02.01-machine_learning_life_cycle.mkv
[4f0967c66cadcb87]
|
3,964,534 |
C933984B |
|
02.02-unique_challenges_with_ml.mkv
[59397050c467d1b8]
|
6,314,182 |
F3D06D9F |
|
02.03-what_is_devops.mkv
[59c0703faa31b87f]
|
3,513,503 |
E01C343B |
|
02.04-what_is_mlops.mkv
[a0e4428ff51fcf6f]
|
7,311,468 |
DBA9E881 |
|
02.05-principles_of_mlops.mkv
[2878822b6e32641]
|
4,339,017 |
49150D69 |
|
02.06-when_to_start_mlops.mkv
[f368bd613c789255]
|
6,486,880 |
824927EE |
|
03.01-selecting_ml_projects.mkv
[af7b5ad3de2fc093]
|
4,279,130 |
4E1ED6BB |
|
03.02-creating_requirements.mkv
[551eed82d921f007]
|
6,975,139 |
912A3335 |
|
03.03-designing_the_ml_workflow.mkv
[ffcd6e3f3623902f]
|
4,965,436 |
3EC5FB62 |
|
03.04-assembling_the_team.mkv
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|
5,217,332 |
73A0E7F3 |
|
03.05-choosing_tools_and_technologies.mkv
[4caf7e46344cc41d]
|
3,964,049 |
D2AC936B |
|
04.01-managed_data_pipelines.mkv
[3aba48a0b103fc3e]
|
7,511,635 |
8FCD8449 |
|
04.02-automated_data_validation.mkv
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|
3,929,445 |
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|
04.03-managed_feature_stores.mkv
[fdd6c964e84b8105]
|
4,677,987 |
AFF57509 |
|
04.04-data_versioning.mkv
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4,548,295 |
0CF8AEC7 |
|
04.05-data_governance.mkv
[a0064c3a1fc45bff]
|
6,250,596 |
BDCAEAFF |
|
04.06-tools_and_technologies_for_data_processing.mkv
[2b12109e4dc56a61]
|
2,102,050 |
9AAE20DE |
|
05.01-managed_training_pipelines.mkv
[23af27150c21e59b]
|
6,687,437 |
D1473AAF |
|
05.02-creating_data_labels.mkv
[cc95f96876831788]
|
6,301,099 |
5C337F17 |
|
05.03-experiment_tracking.mkv
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|
5,347,283 |
CEF5E9BF |
|
05.04-automl.mkv
[e805693a5bb2dfb9]
|
7,399,921 |
F1405D92 |
|
05.05-tools_and_technologies_for_training.mkv
[988b1d7c64a059d8]
|
1,509,666 |
8050EDA1 |
|
06.01-model_versioning.mkv
[b33dc59529b18e12]
|
2,430,803 |
4B0AFF57 |
|
06.02-model_registry.mkv
[715e86de82635ad9]
|
4,081,176 |
8FED2557 |
|
06.03-benchmarking_models.mkv
[a1fcce79f7f6fffa]
|
6,454,393 |
6D353580 |
|
06.04-model_life_cycle_management.mkv
[42d0714e431f142c]
|
5,143,100 |
A61A52BD |
|
06.05-tools_and_technologies_for_model_management.mkv
[9e13f10d2f3f0024]
|
2,186,675 |
4D679635 |
|
07.01-solution_integration_pipelines.mkv
[74588b908732efda]
|
3,520,066 |
DE1299D2 |
|
07.02-notebook_to_software.mkv
[3f116489ee428d97]
|
7,154,064 |
E298B6EB |
|
07.03-solution_integration_patterns.mkv
[a8ad74564cd2768c]
|
6,504,716 |
F1801CB9 |
|
07.04-best_practices_for_solution_integration.mkv
[e28ae8b10baaccbc]
|
1,944,604 |
081BA586 |
|
08.01-continuing_on_with_mlops.mkv
[d2aa4901681b4111]
|
1,411,000 |
A1AAB7F4 |
|
| Total size: |
167,212,883 |
|
|
