<p><span>Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle</span></p><h4><span>Key Features</span></h4><ul><li><span><span>Gain practical knowledge of managing ML workloads on AWS using Amazon SageMaker,
Operationalizing Machine Learning Pipelines: Building Reusable and Reproducible Machine Learning Pipelines Using MLOps
โ Scribed by Vishwajyoti Pandey; Shaleen Bengani
- Publisher
- BPB Publications
- Year
- 2022
- Tongue
- English
- Category
- Library
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
<div><p>Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you ho
<div><p>Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you ho
<div><p>Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you ho
Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put