• Books
  • Databases
  • Amazon Redshift
  • Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands
Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands
A

Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands

Amazon Redshift
Amazon Redshift ML serverless computing machine learning SQL data analysis predictive analytics AWS
Author
Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi
Publisher
Packt Publishing
Year
2023
Language
English
38
Rate this book

To read the book, log in / register

Preview fragment

Similar books

Applied Machine Learning and High-Performance Computing on AWS

Applied Machine Learning and High-Performance Computing on AWS

Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter

Azure Machine Learning

Azure Machine Learning

Jeff Barnes

Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions. Second Edition

Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions. Second Edition

Shruti Worlikar, Harshida Patel, Anusha Challa

Serverless Development on AWS: Building Enterprise-Scale Serverless Solutions

Serverless Development on AWS: Building Enterprise-Scale Serverless Solutions

Sheen Brisals, Luke Hedger

Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform

Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform

Pramod Singh

Practical Machine Learning on Databricks

Practical Machine Learning on Databricks

Debu Sinha

AI-Assisted Programming for Web and Machine Learning: Improve your development workflow with ChatGPT and GitHub Copilot

AI-Assisted Programming for Web and Machine Learning: Improve your development workflow with ChatGPT and GitHub Copilot

Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe Mutlu, Ajit Jaokar

Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems

Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems

Benjamin Tan Wei Hao, Shanoop Padmanabhan, Varun Mallya

Data Without Labels: Practical Unsupervised Machine Learning

Data Without Labels: Practical Unsupervised Machine Learning

Vaibhav Verdhan

Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python

Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python

Pramod Singh, Avinash Manure