Oreilly - Machine Learning Engineering in Action, Video Edition

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[ FreeCourseWeb.com ] Oreilly - Machine Learning Engineering in Action, Video Edition
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    • Appendix_A._Analyzing_decision-tree_complexity.mp4 (20.0 MB)
    • Appendix_A._Big_O(no)_and_how_to_think_about_runtime_performance.mp4 (22.2 MB)
    • Appendix_A._Complexity_by_example.mp4 (34.2 MB)
    • Appendix_A._General_algorithmic_complexity_for_ML.mp4 (18.2 MB)
    • Appendix_B._Containers_to_deal_with_dependency_hell.mp4 (8.7 MB)
    • Appendix_B._Setting_up_a_development_environment (1).mp4 (7.5 MB)
    • Appendix_B._Setting_up_a_development_environment.mp4 (7.1 MB)
    • Bonus Resources.txt (0.4 KB)
    • Chapter_1._Summary.mp4 (2.1 MB)
    • Chapter_1._The_core_tenets_of_ML_engineering.mp4 (61.8 MB)
    • Chapter_1._The_goals_of_ML_engineering.mp4 (6.5 MB)
    • Chapter_1._What_is_a_machine_learning_engineer.mp4 (24.8 MB)
    • Chapter_1.__Hello,_World!__and_printing.mp4.jpg (93.3 KB)
    • Chapter_10._1Naming,_structure,_and_code_architecture.mp4 (26.3 MB)
    • Chapter_10._Blind_to_issues_Eating_exceptions_and_other_bad_practices.mp4 (21.3 MB)
    • Chapter_10._Excessively_nested_logic.mp4 (31.6 MB)
    • Chapter_10._Standards_of_coding_and_creating_maintainable_ML_code.mp4 (12.1 MB)
    • Chapter_10._Summary.mp4 (4.9 MB)
    • Chapter_10._Tuple_unpacking_and_maintainable_alternatives.mp4 (14.2 MB)
    • Chapter_10._Use_of_global_mutable_objects.mp4 (21.7 MB)
    • Chapter_11._Leveraging_AB_testing_for_attribution_calculations.mp4 (58.6 MB)
    • Chapter_11._Model_measurement_and_why_it_s_so_important.mp4 (52.8 MB)
    • Chapter_11._Summary.mp4 (1.4 MB)
    • Chapter_12._Holding_on_to_your_gains_by_watching_for_drift.mp4 (67.8 MB)
    • Chapter_12._Responding_to_drift.mp4 (25.9 MB)
    • Chapter_12._Summary.mp4 (1.3 MB)
    • Chapter_13._Do_you_really_want_to_be_the_canary_Alpha_testing_and_the_dangers_of_the_open_source_coal_mine.mp4 (19.4 MB)
    • Chapter_13._ML_development_hubris.mp4 (54.8 MB)
    • Chapter_13._Premature_generalization,_premature_optimization,_and_other_bad_ways_to_show_how_smart_you_are.mp4 (50.7 MB)
    • Chapter_13._Summary.mp4 (5.0 MB)
    • Chapter_13._Technology-driven_development_vs._solution-driven_development.mp4 (12.8 MB)
    • Chapter_13._Unintentional_obfuscation_Could_you_read_this_if_you_didn_t_write_it.mp4 (74.9 MB)
    • Chapter_14._Avoiding_cargo_cult_ML_behavior.mp4 (28.0 MB)
    • Chapter_14._Keeping_things_as_simple_as_possible.mp4 (20.9 MB)
    • Chapter_14._Monitoring_everything_else_in_the_model_life_cycle.mp4 (16.4 MB)
    • Chapter_14._Monitoring_your_features.mp4 (21.6 MB)
    • Chapter_14._Summary.mp4 (6.2 MB)
    • Chapter_14._Writing_production_code.mp4 (72.1 MB)
    • Chapter_14.__Wireframing_ML_projects.mp4 (27.3 MB)
    • Chapter_15._End_user_vs._internal_use_testing.mp4 (29.3 MB)
    • Chapter_15._Fallbacks_and_cold_starts.mp4 (36.2 MB)
    • Chapter_15._Model_interpretability.mp4 (41.7 MB)
    • Chapter_15._Quality_and_acceptance_testing.mp4 (41.0 MB)
    • Chapter_15._Summary.mp4 (4.2 MB)
    • Chapter_16._Feature_stores.mp4 (33.4 MB)
    • Chapter_16._Prediction_serving_architecture.mp4 (79.7 MB)
    • Chapter_16._Production_infrastructure.mp4 (34.7 MB)
    • Chapter_16._Summary.mp4 (2.5 MB)
    • Chapter_2._Co-opting_principles_of_Agile_software_engineering.mp4 (17.5 MB)
    • Chapter_2._Summary.mp4 (3.4 MB)
    • Chapter_2._The_foundation_of_ML_engineering.mp4 (3.9 MB)
    • Chapter_2._Your_data_science_could_use_some_engineering.mp4 (10.8 MB)
    • Chapter_2.__A_foundation_of_simplicity.mp4 (11.5 MB)
    • Chapter_3._Before_you_model_Planning_and_scoping_a_project.mp4 (110.6 MB)
    • Chapter_3._Summary.mp4 (1.3 MB)
    • Chapter_3.__Experimental_scoping_Setting_expectations_and_boundaries.mp4 (80.9 MB)
    • Chapter_4._Before_you_model_Communication_and_logistics_of_projects.mp4 (131.7 MB)
    • Chapter_4._Don_t_waste_our_time_Meeting_with_cross-functional_teams.mp4 (52.7 MB)
    • Chapter_4._Planning_for_business_rules_chaos.mp4 (19.9 MB)
    • Chapter_4._Setting_limits_on_your_experimentation.mp4 (47.5 MB)
    • Chapter_4._Summary.mp4 (4.5 MB)
    • Chapter_4._Talking_about_results.mp4 (16.9 MB)
    • Chapter_5._Experimentation_in_action_Planning_and_researching_an_ML_project.mp4 (73.2 MB)
    • Chapter_5._Performing_experimental_prep_work.mp4 (76.3 MB)
    • Chapter_5._Summary.mp4 (1.7 MB)
    • Chapter_6._Experimentation_in_action_Testing_and_evaluating_a_project.mp4 (130.2 MB)
    • Chapter_6._Summary.mp4 (1.3 MB)
    • Chapter_6._Whittling_down_the_possibilities.mp4 (34.9 MB)
    • Chapter_7._Choosing_the_right_tech_for_the_platform_and_the_team.mp4 (53.6 MB)
    • Chapter_7._Experimentation_in_action_Moving_from_prototype_to_MVP.mp4 (64.3 MB)
    • Chapter_7._Summary.mp4 (1.7 MB)
    • Chapter_8._Experimentation_in_action_Finalizing_an_MVP_with_MLflow_and_runtime_optimization.mp4 (46.1 MB)
    • Chapter_8._Scalability_and_concurrency.mp4 (18.8 MB)
    • Chapter_8._Summary.mp4 (1.6 MB)
    • Chapter_9._Debugging_walls_of_text.mp4 (10.8 MB)
    • Chapter_9._Designing_modular_ML_code.mp4 (15.4 MB)
    • Chapter_9._Modularity_for_ML_Writing_testable_and_legible_code.mp4 (47.9 MB)
    • Chapter_9._Summary.mp4 (2.6 MB)
    • Chapter_9._Using_test-driven_development_for_ML.mp4 (19.7 MB)
    • Part_1._An_introduction_to_machine_learning_engineering.mp4 (4.7 MB)
    • Part_2._Preparing_for_production_Creating_maintainable_ML.mp4 (4.0 MB)
    • Part_3._Developing_production_machine_learning_code.mp4 (2.3 MB)

Description

Machine Learning Engineering in Action, Video Edition

https://FreeCourseWeb.com

Released 4/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 14h 54m | Size: 2.34 GB

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from In Machine Learning Engineering in Action, you will learn

Evaluating data science problems to find the most effective solution
Scoping a machine learning project for usage expectations and budget
Process techniques that minimize wasted effort and speed up production
Assessing a project using standardized prototyping work and statistical validation
Choosing the right technologies and tools for your project
Making your codebase more understandable, maintainable, and testable
Automating your troubleshooting and logging practices

Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you’ll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks.



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Oreilly - Machine Learning Engineering in Action, Video Edition

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2.3 GB
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Oreilly - Machine Learning Engineering in Action, Video Edition

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