Practical Machine Learning on Databricks : Seamlessly transition ML models and MLOps on Databricks

Practical Machine Learning on Databricks : Seamlessly transition ML models and MLOps on Databricks

Enregistré dans:

Détails bibliographiques
Auteur principal:	Sinha, Debu. (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	Birmingham : Packt Publishing.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

Databricks ML in Action : Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment
par: Rivera, Stephanie.

Databricks Lakehouse Platform Cookbook : 100+ recipes for building a scalable and secure Databricks Lakehouse
par: Dennis, Alan L..

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

Data Engineering with Databricks Cookbook : Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake
par: Chadha, Pulkit.

Practical Machine Learning
par: Gollapudi, Sunila.
Publié: (2016)

Mastering Databricks Lakehouse Platform : Perform Data Warehousing, Data Engineering, Machine Learning, DevOps, and BI into a Single Platform (English Edition)
par: Lad, Sagar.
Publié: (2022)

Machine Learning Engineering with Python : Manage the lifecycle of machine learning models using MLOps with practical examples
par: Mcmahon, Andrew P..

The Machine Learning Solutions Architect Handbook : Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI
par: Ping, David.

Business Intelligence with Databricks SQL : Concepts, tools, and techniques for scaling business intelligence on the data lakehouse
par: Gupta, Vihag.
Publié: (2022)

Optimizing Databricks Workloads : Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads
par: Kala, Anirudh.
Publié: (2021)

Practical Machine Learning with R : Define, build, and evaluate machine learning models for real-world applications
par: Jeyaraman, Brindha Priyadarshini.
Publié: (2019)

Distributed Data Systems with Azure Databricks : Create, deploy, and manage enterprise data pipelines
par: Palacio, Alan Bernardo.
Publié: (2021)

Azure Databricks Cookbook : Accelerate and scale real-time analytics solutions using the Apache Spark-based analytics service
par: Raj, Phani.

Querying Databricks with Spark SQL : Leverage SQL to query and analyze Big Data for insights (English Edition)
par: Aspin, Adam.

Practical Machine Learning with Spark : Uncover Apache Spark's Scalable Performance with High-Quality Algorithms Across NLP, Computer Vision and ML
par: Gupta, Gourav.

Practical machine learning in R
par: Nwanganga, Fred (19..-....).

Practical Machine Learning Cookbook
par: Tripathi, Atul.
Publié: (2017)

Practical Full Stack Machine Learning : A Guide to Build Reliable, Reusable, and Production-Ready Full Stack ML Solutions
par: Kumar, Alok.

Machine Learning in Production : Master the art of delivering robust Machine Learning solutions with MLOps
par: Pote, Suhas.

Operationalizing Machine Learning Pipelines : Building Reusable and Reproducible Machine Learning Pipelines Using MLOps
par: Pandey, Vishwajyoti.
Publié: (2022)

Cloud Native AI and Machine Learning on AWS : Use SageMaker for building ML models, automate MLOps, and take advantage of numerous AWS AI services
par: Rangarajan, Premkumar.

Databricks Certified Associate Developer for Apache Spark Using Python : The ultimate guide to getting certified in Apache Spark using practical examples with Python
par: Shah, Saba.

Machine Learning Engineering on AWS : Build, scale, and secure machine learning systems and MLOps pipelines in production
par: Lat, Joshua Arvin.
Publié: (2022)

Hands-On Machine Learning with ML.NET : Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#
par: Capellman, Jarred.
Publié: (2020)

Continuous Machine Learning with Kubeflow : Performing Reliable MLOps with Capabilities of TFX, Sagemaker and Kubernetes
par: Choudhury, Aniruddha.

Applied Machine Learning Explainability Techniques : Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
par: Bhattacharya, Aditya.
Publié: (2022)

Practical machine learning for data analysis using Python
par: Subasi, Abdulhamit (19..-....).
Publié: (2020)

Azure Machine Learning Engineering : Deploy, fine-tune, and optimize ML models using Microsoft Azure
par: Fakhraee, Sina.

Machine learning with Core ML : an iOS developer's guide to implementing machine learning in mobile apps
par: Newnham, Joshua.
Publié: (2018)

Machine Learning with BigQuery ML : Create, execute, and improve machine learning models in BigQuery using standard SQL queries
par: Marrandino, Alessandro.
Publié: (2021)

Microservices for Machine Learning : Design, implement, and manage high-performance ML systems with microservices
par: Ranjan, Rohit.

Learn Unity ML-Agents - fundamentals of Unity Machine Learning : ncorporate new powerful ML algorithms such as Deep Reinforcement Learning for games
par: Lanham, Micheal.
Publié: (2018)

Mastering Azure Machine Learning : Perform large-scale end-to-end advanced machine learning on the cloud with Microsoft Azure ML
par: Körner, Christoph.
Publié: (2020)

Hands-on automated machine learning : a beginner's guide to building automated machine learning systems using AutoML and Python
par: Das, Sibanjan.
Publié: (2018)

Feature Store for Machine Learning : Curate, discover, share and serve ML features at scale
par: Kumar, Jayanth.
Publié: (2022)

Machine Learning Infrastructure and Best Practices for Software Engineers : Take your machine learning software from a prototype to a fully fledged software system
par: Staron, Miroslaw.

Machine Learning with Qlik Sense : Utilize different machine learning models in practical use cases by leveraging Qlik Sense
par: Ranta, Hannu.

Fun with Machine Learning : Simplify the Data Science process by automating repetitive and complex tasks using AutoML
par: Liborious, Arockia.

Machine learning for Mobile : practical guide to building intelligent mobile applications powered by machine learning
par: Gopalakrishnan, Revathi.
Publié: (2018)

Pragmatic Machine Learning with Python : Learn How to Deploy Machine Learning Models in Production
par: Nag, Avishek.

Université catholique de l'Ouest - Bibliothèques - 3 place André Leroy - BP10808 - 49008 ANGERS Cedex 1