| Titre : |
Machine learning design patterns : solutions to common challenges in data preparation, model building, and MLOps |
| Type de document : |
texte imprimé |
| Auteurs : |
Valliappa Lakshmanan, Auteur ; Sara Robinson, Auteur ; Michael Munn, Auteur |
| Editeur : |
Sebastopol [Etats-Unis] : O'Reilly Media |
| Année de publication : |
2021 |
| Importance : |
XIV, 390 p. |
| Présentation : |
ill. |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-1-09-811578-4 |
| Note générale : |
Index |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Machine learning
Computer programming
Big data
Apprentissage automatique
Design patterns |
| Index. décimale : |
004.8 Intelligence artificielle |
| Résumé : |
n this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. |
| Note de contenu : |
Summary :
1. The need for machine learning design patterns.
2. Data representation design patterns.
3. Problem representation design patterns.
4. Model training patterns.
5. Design patterns for resilient serving.
6. Reproducibility design patterns.
7. Responsible AI.
8. Connected patterns. |
Machine learning design patterns : solutions to common challenges in data preparation, model building, and MLOps [texte imprimé] / Valliappa Lakshmanan, Auteur ; Sara Robinson, Auteur ; Michael Munn, Auteur . - Sebastopol [Etats-Unis] : O'Reilly Media, 2021 . - XIV, 390 p. : ill. ; 24 cm. ISBN : 978-1-09-811578-4 Index Langues : Anglais ( eng)
| Mots-clés : |
Machine learning
Computer programming
Big data
Apprentissage automatique
Design patterns |
| Index. décimale : |
004.8 Intelligence artificielle |
| Résumé : |
n this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. |
| Note de contenu : |
Summary :
1. The need for machine learning design patterns.
2. Data representation design patterns.
3. Problem representation design patterns.
4. Model training patterns.
5. Design patterns for resilient serving.
6. Reproducibility design patterns.
7. Responsible AI.
8. Connected patterns. |
|  |
Ajouter le résultat dans votre panier Faire une suggestion Affiner la rechercheMachine learning design patterns (2021)
