|
RAR-files |
| rebar-production.machine.learning.systems.rar |
50,000,000 |
5B9BFD8F |
| rebar-production.machine.learning.systems.r00 |
50,000,000 |
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| rebar-production.machine.learning.systems.r01 |
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| rebar-production.machine.learning.systems.r02 |
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| rebar-production.machine.learning.systems.r03 |
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| rebar-production.machine.learning.systems.r04 |
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| rebar-production.machine.learning.systems.r05 |
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| rebar-production.machine.learning.systems.r06 |
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| rebar-production.machine.learning.systems.r07 |
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| rebar-production.machine.learning.systems.r08 |
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| rebar-production.machine.learning.systems.r09 |
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| rebar-production.machine.learning.systems.r10 |
50,000,000 |
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| rebar-production.machine.learning.systems.r11 |
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| rebar-production.machine.learning.systems.r12 |
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| rebar-production.machine.learning.systems.r13 |
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| rebar-production.machine.learning.systems.r14 |
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| rebar-production.machine.learning.systems.r15 |
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| rebar-production.machine.learning.systems.r16 |
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| rebar-production.machine.learning.systems.r17 |
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| rebar-production.machine.learning.systems.r18 |
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| rebar-production.machine.learning.systems.r19 |
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| rebar-production.machine.learning.systems.r20 |
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| Total size: |
1,056,102,499 |
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|
| Archived
files |
|
01 - Welcome to the course\01 - Course Introduction.mp4
[c77572a3a486ffb8]
|
25,262,520 |
320DB73B |
|
01 - Welcome to the course\02 - Getting started with GCP and Qwiklabs.mp4
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|
9,984,917 |
C6EA6EE3 |
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02 - Architecting Production ML Systems\03 - Introduction.mp4
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|
13,066,091 |
E0A42B60 |
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02 - Architecting Production ML Systems\04 - The Components of an ML System.mp4
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5,122,125 |
352F3C47 |
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02 - Architecting Production ML Systems\05 - The Components of an ML System -Data Analysis and Validation.mp4
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|
41,850,733 |
8B266B6E |
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02 - Architecting Production ML Systems\06 - The Components of an ML System -Data Transformation + Trainer.mp4
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4,137,001 |
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02 - Architecting Production ML Systems\07 - The Components of an ML System -Tuner + Model Evaluation and Validation.mp4
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13,567,823 |
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02 - Architecting Production ML Systems\08 - The Components of an ML System -Serving.mp4
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|
8,047,244 |
7392DFB6 |
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02 - Architecting Production ML Systems\09 - The Components of an ML System -Orchestration + Workflow.mp4
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26,440,056 |
B7D60BD6 |
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02 - Architecting Production ML Systems\10 - The Components of an ML System -Integrated Frontend + Storage.mp4
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6,223,967 |
1FCC95D1 |
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02 - Architecting Production ML Systems\11 - Training Design Decisions.mp4
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23,685,939 |
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02 - Architecting Production ML Systems\12 - Serving Design Decisions.mp4
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35,193,270 |
209CEEFC |
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02 - Architecting Production ML Systems\13 - Lab Intro -Serving on Cloud AI Platform.mp4
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15,253,026 |
D2D7C3F0 |
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02 - Architecting Production ML Systems\14 - Serving on Cloud AI Platform.mp4
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236,088 |
3DA81ABB |
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02 - Architecting Production ML Systems\15 - Lab Solution -Serving on Cloud AI Platform.mp4
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11,375,508 |
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02 - Architecting Production ML Systems\16 - Designing from Scratch.mp4
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|
22,417,001 |
A9A0FED3 |
|
03 - Ingesting data for Cloud-based analytics and ML\17 - Introduction.mp4
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|
24,934,927 |
CA206729 |
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03 - Ingesting data for Cloud-based analytics and ML\18 - Data On-Premise.mp4
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20,342,287 |
061FE508 |
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03 - Ingesting data for Cloud-based analytics and ML\19 - Large Datasets.mp4
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37,311,023 |
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03 - Ingesting data for Cloud-based analytics and ML\20 - Data on Other Clouds.mp4
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17,700,228 |
63E4275E |
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03 - Ingesting data for Cloud-based analytics and ML\21 - Existing Databases.mp4
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11,833,622 |
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03 - Ingesting data for Cloud-based analytics and ML\22 - Demo -Load data into BigQuery.mp4
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18,595,479 |
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03 - Ingesting data for Cloud-based analytics and ML\23 - Demo -Automatic ETL Pipelines into GCP.mp4
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7,903,424 |
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04 - Designing Adaptable ML systems\24 - Introduction.mp4
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|
43,226,131 |
C975B7E3 |
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04 - Designing Adaptable ML systems\25 - Adapting to Data.mp4
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22,632,777 |
E96BF6C8 |
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04 - Designing Adaptable ML systems\26 - Changing Distributions.mp4
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38,809,722 |
D65D99CC |
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04 - Designing Adaptable ML systems\27 - Exercise -Adapting to Data.mp4
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14,932,303 |
2A340C66 |
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04 - Designing Adaptable ML systems\28 - Right and Wrong Decisions.mp4
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39,595,305 |
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04 - Designing Adaptable ML systems\29 - System Failure.mp4
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|
18,484,471 |
86E66AEB |
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04 - Designing Adaptable ML systems\30 - Mitigating Training-Serving Skew through Design.mp4
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|
13,396,113 |
4EEF5876 |
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04 - Designing Adaptable ML systems\31 - Lab Intro -Serving ML Predictions in batch and real-time.mp4
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12,330,941 |
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04 - Designing Adaptable ML systems\32 - Serving ML Predictions in batch and real-time.mp4
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|
247,658 |
4FF994E3 |
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04 - Designing Adaptable ML systems\33 - Lab Solution -Serving ML Predictions in batch and real-time.mp4
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|
32,273,065 |
438BBD9A |
|
04 - Designing Adaptable ML systems\34 - Debugging a Production Model.mp4
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|
20,879,630 |
D0675F6B |
|
04 - Designing Adaptable ML systems\35 - Summary.mp4
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|
13,242,710 |
6FD74FCF |
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05 - Designing High-performance ML systems\36 - Introduction.mp4
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|
8,841,185 |
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05 - Designing High-performance ML systems\37 - Training.mp4
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28,674,997 |
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05 - Designing High-performance ML systems\38 - Predictions.mp4
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|
23,547,376 |
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05 - Designing High-performance ML systems\39 - Why distributed training.mp4
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25,653,805 |
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05 - Designing High-performance ML systems\40 - Distributed training architectures.mp4
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22,093,327 |
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05 - Designing High-performance ML systems\41 - Faster input pipelines.mp4
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|
10,758,552 |
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05 - Designing High-performance ML systems\42 - Native TensorFlow Operations.mp4
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|
5,595,769 |
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05 - Designing High-performance ML systems\43 - TensorFlow Records.mp4
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3,171,771 |
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05 - Designing High-performance ML systems\44 - Parallel pipelines.mp4
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|
9,454,447 |
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05 - Designing High-performance ML systems\45 - Data parallelism with All Reduce.mp4
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|
19,355,391 |
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05 - Designing High-performance ML systems\46 - Parameter Server Approach.mp4
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11,959,047 |
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05 - Designing High-performance ML systems\47 - Inference.mp4
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|
17,331,161 |
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06 - Hybrid ML systems\48 - Introduction.mp4
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|
27,303,863 |
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06 - Hybrid ML systems\49 - Machine Learning on Hybrid Cloud.mp4
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|
17,655,367 |
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06 - Hybrid ML systems\50 - KubeFlow.mp4
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|
18,161,791 |
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06 - Hybrid ML systems\51 - Demo -KubeFlow.mp4
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|
52,160,648 |
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06 - Hybrid ML systems\52 - Kubeflow - End to End.mp4
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06 - Hybrid ML systems\53 - Embedded Models.mp4
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06 - Hybrid ML systems\54 - TensorFlow Lite.mp4
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06 - Hybrid ML systems\55 - Optimizing for Mobile.mp4
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06 - Hybrid ML systems\56 - Summary.mp4
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|
07 - Course Summary\57 - Summary.mp4
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|
01 - Welcome to the course |
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02 - Architecting Production ML Systems |
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03 - Ingesting data for Cloud-based analytics and ML |
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04 - Designing Adaptable ML systems |
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05 - Designing High-performance ML systems |
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06 - Hybrid ML systems |
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07 - Course Summary |
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| Total size: |
1,056,092,798 |
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