Download Free Download : Oreilly Machine Learning Engineering in Action
mp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:2.33 GB
Files Included :
Appendix A Analyzing decision-tree complexity.mp4 (20.02 MB)
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Appendix A Big O
and how to think about runtime performance.mp4 (22.16 MB)
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Appendix A Complexity by example.mp4 (34.17 MB)
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Appendix A General algorithmic complexity for ML.mp4 (18.24 MB)
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Appendix B Containers to deal with dependency hell.mp4 (8.68 MB)
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Appendix B Setting up a development environment (1).mp4 (7.53 MB)
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Appendix B Setting up a development environment.mp4 (7.12 MB)
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Chapter 1 Summary.mp4 (2.05 MB)
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Chapter 1 The core tenets of ML engineering.mp4 (61.81 MB)
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Chapter 1 The goals of ML engineering.mp4 (6.48 MB)
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Chapter 1 What is a machine learning engineer.mp4 (24.84 MB)
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Chapter 10 1Naming, structure, and code architecture.mp4 (26.26 MB)
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Chapter 10 Blind to issues Eating exceptions and other bad practices.mp4 (21.27 MB)
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Chapter 10 Excessively nested logic.mp4 (31.57 MB)
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Chapter 10 Standards of coding and creating maintainable ML code.mp4 (12.07 MB)
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Chapter 10 Summary.mp4 (4.88 MB)
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Chapter 10 Tuple unpacking and maintainable alternatives.mp4 (14.22 MB)
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Chapter 10 Use of global mutable objects.mp4 (21.67 MB)
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Chapter 11 Leveraging AB testing for attribution calculations.mp4 (58.58 MB)
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Chapter 11 Model measurement and why it s so important.mp4 (52.83 MB)
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Chapter 11 Summary.mp4 (1.4 MB)
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Chapter 12 Holding on to your gains by watching for drift.mp4 (67.83 MB)
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Chapter 12 Responding to drift.mp4 (25.89 MB)
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Chapter 12 Summary.mp4 (1.27 MB)
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Chapter 13 Do you really want to be the canary Alpha testing and the dangers of the open source coal mine.mp4 (19.43 MB)
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Chapter 13 ML development hubris.mp4 (54.84 MB)
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Chapter 13 Premature generalization, premature optimization, and other bad ways to show how smart you are.mp4 (50.71 MB)
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Chapter 13 Summary.mp4 (5 MB)
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Chapter 13 Technology-driven development vs solution-driven development.mp4 (12.83 MB)
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Chapter 13 Unintentional obfuscation Could you read this if you didn t write it.mp4 (74.92 MB)
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Chapter 14 Avoiding cargo cult ML behavior.mp4 (27.97 MB)
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Chapter 14 Keeping things as simple as possible.mp4 (20.94 MB)
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Chapter 14 Monitoring everything else in the model life cycle.mp4 (16.36 MB)
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Chapter 14 Monitoring your features.mp4 (21.64 MB)
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Chapter 14 Summary.mp4 (6.25 MB)
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Chapter 14 Writing production code.mp4 (72.11 MB)
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Chapter 14 Wireframing ML projects.mp4 (27.3 MB)
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Chapter 15 End user vs internal use testing.mp4 (29.31 MB)
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Chapter 15 Fallbacks and cold starts.mp4 (36.2 MB)
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Chapter 15 Model interpretability.mp4 (41.72 MB)
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Chapter 15 Quality and acceptance testing.mp4 (41.04 MB)
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Chapter 15 Summary.mp4 (4.2 MB)
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Chapter 16 Feature stores.mp4 (33.37 MB)
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Chapter 16 Prediction serving architecture.mp4 (79.68 MB)
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Chapter 16 Production infrastructure.mp4 (34.73 MB)
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Chapter 16 Summary.mp4 (2.5 MB)
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Chapter 2 Co-opting principles of Agile software engineering.mp4 (17.5 MB)
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Chapter 2 Summary.mp4 (3.36 MB)
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Chapter 2 The foundation of ML engineering.mp4 (3.94 MB)
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Chapter 2 Your data science could use some engineering.mp4 (10.83 MB)
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Chapter 2 A foundation of simplicity.mp4 (11.55 MB)
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Chapter 3 Before you model Planning and scoping a project.mp4 (110.62 MB)
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Chapter 3 Summary.mp4 (1.31 MB)
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Chapter 3 Experimental scoping Setting expectations and boundaries.mp4 (80.88 MB)
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Chapter 4 Before you model Communication and logistics of projects.mp4 (131.66 MB)
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Chapter 4 Don t waste our time Meeting with cross-functional teams.mp4 (52.7 MB)
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Chapter 4 Planning for business rules chaos.mp4 (19.9 MB)
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Chapter 4 Setting limits on your experimentation.mp4 (47.48 MB)
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Chapter 4 Summary.mp4 (4.47 MB)
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Chapter 4 Talking about results.mp4 (16.9 MB)
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Chapter 5 Experimentation in action Planning and researching an ML project.mp4 (73.23 MB)
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Chapter 5 Performing experimental prep work.mp4 (76.32 MB)
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Chapter 5 Summary.mp4 (1.73 MB)
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Chapter 6 Experimentation in action Testing and evaluating a project.mp4 (130.17 MB)
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Chapter 6 Summary.mp4 (1.35 MB)
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Chapter 6 Whittling down the possibilities.mp4 (34.89 MB)
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Chapter 7 Choosing the right tech for the platform and the team.mp4 (53.63 MB)
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Chapter 7 Experimentation in action Moving from prototype to MVP.mp4 (64.26 MB)
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Chapter 7 Summary.mp4 (1.69 MB)
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Chapter 8 Experimentation in action Finalizing an MVP with MLflow and runtime optimization.mp4 (46.1 MB)
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Chapter 8 Scalability and concurrency.mp4 (18.82 MB)
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Chapter 8 Summary.mp4 (1.64 MB)
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Chapter 9 Debugging walls of text.mp4 (10.81 MB)
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Chapter 9 Designing modular ML code.mp4 (15.41 MB)
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Chapter 9 Modularity for ML Writing testable and legible code.mp4 (47.85 MB)
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Chapter 9 Summary.mp4 (2.6 MB)
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Chapter 9 Using test-driven development for ML.mp4 (19.69 MB)
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Part 1 An introduction to machine learning engineering.mp4 (4.72 MB)
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Part 2 Preparing for production Creating maintainable ML.mp4 (3.96 MB)
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Part 3 Developing production machine learning code.mp4 (2.3 MB)
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Appendix A Big O
and how to think about runtime performance.mp4 (22.16 MB)MP4
Appendix A Complexity by example.mp4 (34.17 MB)
MP4
Appendix A General algorithmic complexity for ML.mp4 (18.24 MB)
MP4
Appendix B Containers to deal with dependency hell.mp4 (8.68 MB)
MP4
Appendix B Setting up a development environment (1).mp4 (7.53 MB)
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Appendix B Setting up a development environment.mp4 (7.12 MB)
MP4
Chapter 1 Summary.mp4 (2.05 MB)
MP4
Chapter 1 The core tenets of ML engineering.mp4 (61.81 MB)
MP4
Chapter 1 The goals of ML engineering.mp4 (6.48 MB)
MP4
Chapter 1 What is a machine learning engineer.mp4 (24.84 MB)
MP4
Chapter 10 1Naming, structure, and code architecture.mp4 (26.26 MB)
MP4
Chapter 10 Blind to issues Eating exceptions and other bad practices.mp4 (21.27 MB)
MP4
Chapter 10 Excessively nested logic.mp4 (31.57 MB)
MP4
Chapter 10 Standards of coding and creating maintainable ML code.mp4 (12.07 MB)
MP4
Chapter 10 Summary.mp4 (4.88 MB)
MP4
Chapter 10 Tuple unpacking and maintainable alternatives.mp4 (14.22 MB)
MP4
Chapter 10 Use of global mutable objects.mp4 (21.67 MB)
MP4
Chapter 11 Leveraging AB testing for attribution calculations.mp4 (58.58 MB)
MP4
Chapter 11 Model measurement and why it s so important.mp4 (52.83 MB)
MP4
Chapter 11 Summary.mp4 (1.4 MB)
MP4
Chapter 12 Holding on to your gains by watching for drift.mp4 (67.83 MB)
MP4
Chapter 12 Responding to drift.mp4 (25.89 MB)
MP4
Chapter 12 Summary.mp4 (1.27 MB)
MP4
Chapter 13 Do you really want to be the canary Alpha testing and the dangers of the open source coal mine.mp4 (19.43 MB)
MP4
Chapter 13 ML development hubris.mp4 (54.84 MB)
MP4
Chapter 13 Premature generalization, premature optimization, and other bad ways to show how smart you are.mp4 (50.71 MB)
MP4
Chapter 13 Summary.mp4 (5 MB)
MP4
Chapter 13 Technology-driven development vs solution-driven development.mp4 (12.83 MB)
MP4
Chapter 13 Unintentional obfuscation Could you read this if you didn t write it.mp4 (74.92 MB)
MP4
Chapter 14 Avoiding cargo cult ML behavior.mp4 (27.97 MB)
MP4
Chapter 14 Keeping things as simple as possible.mp4 (20.94 MB)
MP4
Chapter 14 Monitoring everything else in the model life cycle.mp4 (16.36 MB)
MP4
Chapter 14 Monitoring your features.mp4 (21.64 MB)
MP4
Chapter 14 Summary.mp4 (6.25 MB)
MP4
Chapter 14 Writing production code.mp4 (72.11 MB)
MP4
Chapter 14 Wireframing ML projects.mp4 (27.3 MB)
MP4
Chapter 15 End user vs internal use testing.mp4 (29.31 MB)
MP4
Chapter 15 Fallbacks and cold starts.mp4 (36.2 MB)
MP4
Chapter 15 Model interpretability.mp4 (41.72 MB)
MP4
Chapter 15 Quality and acceptance testing.mp4 (41.04 MB)
MP4
Chapter 15 Summary.mp4 (4.2 MB)
MP4
Chapter 16 Feature stores.mp4 (33.37 MB)
MP4
Chapter 16 Prediction serving architecture.mp4 (79.68 MB)
MP4
Chapter 16 Production infrastructure.mp4 (34.73 MB)
MP4
Chapter 16 Summary.mp4 (2.5 MB)
MP4
Chapter 2 Co-opting principles of Agile software engineering.mp4 (17.5 MB)
MP4
Chapter 2 Summary.mp4 (3.36 MB)
MP4
Chapter 2 The foundation of ML engineering.mp4 (3.94 MB)
MP4
Chapter 2 Your data science could use some engineering.mp4 (10.83 MB)
MP4
Chapter 2 A foundation of simplicity.mp4 (11.55 MB)
MP4
Chapter 3 Before you model Planning and scoping a project.mp4 (110.62 MB)
MP4
Chapter 3 Summary.mp4 (1.31 MB)
MP4
Chapter 3 Experimental scoping Setting expectations and boundaries.mp4 (80.88 MB)
MP4
Chapter 4 Before you model Communication and logistics of projects.mp4 (131.66 MB)
MP4
Chapter 4 Don t waste our time Meeting with cross-functional teams.mp4 (52.7 MB)
MP4
Chapter 4 Planning for business rules chaos.mp4 (19.9 MB)
MP4
Chapter 4 Setting limits on your experimentation.mp4 (47.48 MB)
MP4
Chapter 4 Summary.mp4 (4.47 MB)
MP4
Chapter 4 Talking about results.mp4 (16.9 MB)
MP4
Chapter 5 Experimentation in action Planning and researching an ML project.mp4 (73.23 MB)
MP4
Chapter 5 Performing experimental prep work.mp4 (76.32 MB)
MP4
Chapter 5 Summary.mp4 (1.73 MB)
MP4
Chapter 6 Experimentation in action Testing and evaluating a project.mp4 (130.17 MB)
MP4
Chapter 6 Summary.mp4 (1.35 MB)
MP4
Chapter 6 Whittling down the possibilities.mp4 (34.89 MB)
MP4
Chapter 7 Choosing the right tech for the platform and the team.mp4 (53.63 MB)
MP4
Chapter 7 Experimentation in action Moving from prototype to MVP.mp4 (64.26 MB)
MP4
Chapter 7 Summary.mp4 (1.69 MB)
MP4
Chapter 8 Experimentation in action Finalizing an MVP with MLflow and runtime optimization.mp4 (46.1 MB)
MP4
Chapter 8 Scalability and concurrency.mp4 (18.82 MB)
MP4
Chapter 8 Summary.mp4 (1.64 MB)
MP4
Chapter 9 Debugging walls of text.mp4 (10.81 MB)
MP4
Chapter 9 Designing modular ML code.mp4 (15.41 MB)
MP4
Chapter 9 Modularity for ML Writing testable and legible code.mp4 (47.85 MB)
MP4
Chapter 9 Summary.mp4 (2.6 MB)
MP4
Chapter 9 Using test-driven development for ML.mp4 (19.69 MB)
MP4
Part 1 An introduction to machine learning engineering.mp4 (4.72 MB)
MP4
Part 2 Preparing for production Creating maintainable ML.mp4 (3.96 MB)
MP4
Part 3 Developing production machine learning code.mp4 (2.3 MB)
MP4
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um Code Inhalt zu sehen!