Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

Contents

Foreword

Preface

About the Authors

I: Principles of Framing

1 The Role of the Data Scientist

1.1 Introduction

1.2 The Role of the Data Scientist

1.2.1 Company Size

1.2.2 Team Context

1.2.3 Ladders and Career Development

1.2.4 Importance

1.2.5 The Work Breakdown

1.3 Conclusion

2 Project Workflow

2.1 Introduction

2.2 The Data Team Context

2.2.1 Embedding vs. Pooling Resources

2.2.2 Research

2.2.3 Prototyping

2.2.4 A Combined Workflow

2.3 Agile Development and the Product Focus

2.3.1 The 12 Principles

2.4 Conclusion

3 Quantifying Error

3.1 Introduction

3.2 Quantifying Error in Measured Values

3.3 Sampling Error

3.4 Error Propagation

3.5 Conclusion

4 Data Encoding and Preprocessing

4.1 Introduction

4.2 Simple Text Preprocessing

4.2.1 Tokenization ...