Beschreibung Machine Learning for Business: Using Amazon SageMaker and Jupyter. Summary Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen Think about the benefits of forecasting tedious business processes and back-office tasks Envision quickly gauging customer sentiment from social media content (even large volumes of it). Consider the competitive advantage of making decisions when you know the most likely future events Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Machine learning can deliver huge benefits for everyday business tasks. With some guidance, you can get those big wins yourself without complex math or highly paid consultants! If you can crunch numbers in Excel, you can use modern ML services to efficiently direct marketing dollars, identify and keep your best customers, and optimize back office processes. This book shows you how. About the book Machine Learning for Business teaches business-oriented machine learning techniques you can do yourself. Concentrating on practical topics like customer retention, forecasting, and back office processes, you’ll work through six projects that help you form an ML-for-business mindset. To guarantee your success, you’ll use the Amazon SageMaker ML service, which makes it a snap to turn your questions into results. What's inside Identifying tasks suited to machine learning Automating back office processes Using open source and cloud-based tools Relevant case studies About the reader For technically inclined business professionals or business application developers. About the author Doug Hudgeon and Richard Nichol specialize in maximizing the value of business data through AI and machine learning for companies of any size. Table of Contents: PART 1 MACHINE LEARNING FOR BUSINESS 1 ¦ How machine learning applies to your business PART 2 SIX SCENARIOS: MACHINE LEARNING FOR BUSINESS 2 ¦ Should you send a purchase order to a technical approver? 3 ¦ Should you call a customer because they are at risk of churning? 4 ¦ Should an incident be escalated to your support team? 5 ¦ Should you question an invoice sent by a supplier? 6 ¦ Forecasting your company’s monthly power usage 7 ¦ Improving your company’s monthly power usage forecast PART 3 MOVING MACHINE LEARNING INTO PRODUCTION 8 ¦ Serving predictions over the web 9 ¦ Case studies
Machine Learning for Business: Using Sagemaker and ~ Machine Learning for Business: Using Sagemaker and Jupyter / Doug Hudgeon, Richard Nichol / ISBN: 9781617295836 / Kostenloser Versand für alle Bücher mit Versand und Verkauf duch .
Machine Learning for Business: Using SageMaker and ~ Machine Learning for Business: Using SageMaker and Jupyter` / Doug Hudgeon, Richard Nichol / download / B–OK. Download books for free. Find books
Manning / Machine Learning for Business ~ Machine Learning for Business teaches business-oriented machine learning techniques you can do yourself. Concentrating on practical topics like customer retention, forecasting, and back office processes, you’ll work through six projects that help you form an ML-for-business mindset. To guarantee your success, you’ll use the SageMaker ML service, which makes it a snap to turn your .
Machine Learning for Business: Using SageMaker and ~ Machine Learning for Business: Using SageMaker and Jupyter. Author: Doug Hudgeon, Richard Nichol. Edition: 1 edition. Categories: Machine Theory / Business & Money / Business Mathematics. Data: 2020-02-04. ISBN: 1617295833. ISBN-13: 9781617295836. Language: English. Pages: 300. Format: PDF. Book Description Imagine predicting which customers are thinking about switching to a competitor .
Machine Learning for Business: Using SageMaker and ~ Machine Learning for Business teaches business-oriented machine learning techniques you can do yourself. Concentrating on practical topics
SageMaker: Simplifying Machine Learning Application ~ Machine learning is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market. This course will teach you, an application developer, how to use SageMaker to simplify the integration of Machine Learning into your applications. Key topics include: an overview of Machine Learning and problems .
Production Jupyter Notebook on SageMaker — Getting ~ You can potentially found a machine learning, data centric startup today. In this article you will learn how to initialize a Jupyter Notebook on SageMaker. First of all, you will need an .
Deploying reinforcement learning in production using Ray ~ Reinforcement learning (RL) is used to automate decision-making in a variety of domains, including games, autoscaling, finance, robotics, recommendations, and supply chain.Launched at AWS re:Invent 2018, SageMaker RL helps you quickly build, train, and deploy policies learned by RL. Ray is an open-source distributed execution framework that makes it easy to scale your Python applications.
Understanding SageMaker notebook instance ~ An SageMaker notebook instance provides a Jupyter notebook app through a fully managed machine learning (ML) EC2 instance. SageMaker Jupyter notebooks are used to perform advanced data exploration, create training jobs, deploy models to SageMaker hosting, and test or validate your models. The notebook instance has a variety of networking configurations […]
How to Build, Train, and Deploy a Machine Learning Model ~ In this tutorial, you will learn how to use SageMaker to build, train, and deploy a machine learning (ML) model. We will use the popular XGBoost ML algorithm for this exercise. SageMaker is a modular, fully managed machine learning service that enables developers and data scientists to build, train, and deploy ML models at scale.
Real-time data labeling pipeline for ML workflows using ~ High-quality machine learning (ML) models depend on accurately labeled, high-quality training, validation, and test data. As ML and deep learning models are increasingly integrated into production environments, it’s becoming more important than ever to have customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data.
Using R with SageMaker / AWS Machine Learning Blog ~ This blog post describes how to train, deploy, and retrieve predictions from a machine learning (ML) model using SageMaker and R. The model predicts abalone age as measured by the number of rings in the shell. The reticulate package will be used as an R interface to SageMaker Python SDK to make API calls to […]
ElectrifAi Offers New Machine Learning Models for ~ ElectrifAi's models for SageMaker available in AWS Marketplace are orchestrated together in a use-case to solve real business problems. The ML models in AWS Marketplace are available worldwide.
SageMaker ~ Using SageMaker, you can deploy your trained machine learning models to Inf1 instances, built using the AWS Inferentia chip, to provide high performance and low cost inference. Using Inf1 instances, you can run large scale machine learning inference applications like image recognition, speech recognition, natural language processing, personalization, and fraud detection. With .
How to Decide Between SageMaker and Microsoft Azure ~ So, this week, I am taking a look at SageMaker (SageMaker) and how it compares to Studio. What I found when I looked at SageMak e r in comparison to Studio is a significantly different approach to model building. The vendors of each tool would both claim to offer a fully managed service that covers the entire machine learning workflow to build, train, and deploy machine learning models .
Mastering Machine Learning on AWS: Advanced - ~ Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow / Mengle, Dr. Saket S.R., Gurmendez, Maximo / ISBN: 9781789349795 / Kostenloser Versand für alle Bücher mit Versand und Verkauf duch .
Machine Learning in the AWS Cloud (eBook, PDF) von ~ Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Web Services. Part One introduces readers .
Example Notebooks - SageMaker ~ The example notebooks contain code that shows how to apply machine learning solutions by using SageMaker. Notebook instances use the nbexamples Jupyter extension, which enables you to view a read-only version of an example notebook or create a copy of it so that you can modify and run it. For more information about the
Developing NER models with SageMaker Ground Truth ~ SageMaker Ground Truth enables you to efficiently and accurately label the datasets required to train machine learning systems. Ground Truth provides built-in labeling workflows that take human labelers step-by-step through tasks and provide tools to efficiently and accurately build the annotated NER datasets required to train machine learning (ML) systems.
SageMaker: A Hands-On Introduction – BMC Blogs ~ SageMaker is a managed machine learning service (MLaaS). SageMaker lets you quickly build and train machine learning models and deploy them directly into a hosted environment. In this blog post, we’ll cover how to get started and run SageMaker with examples. One thing you will find with most of the examples written by for .
Identifying worker labeling efficiency using ~ This post is targeted at anyone interested in learning how to perform a Text Classification labeling job with SageMaker using the Mechanical Turk (MTurk) worker type, and subsequently verifying the quality of the labels. The post walks you a tutorial in which you perform each step of the process, so no prior knowledge in ML/AI or SageMaker is required.
Linear learner algorithm - SageMaker ~ Linear models are supervised learning algorithms used for solving either classification or regression problems. For input, you give the model labeled examples ( x , y ). x is a high-dimensional vector and y is a numeric label. For binary classification problems, the label must be either 0 or 1. For multiclass classification problems, the labels must be from 0 to
Neuerscheinungen: Die beliebtesten Neuheiten in ~ Neuerscheinungen: Die beliebtesten Neuheiten in Künstliche Intelligenz. Zum Hauptinhalt wechseln Prime entdecken DE Hallo! Anmelden .
R User Guide to SageMaker - SageMaker ~ Example Notebooks. Prerequisites. Getting Started with R on SageMaker: This sample notebook describes how you can develop R scripts using SageMaker‘s R kernel.In this notebook you set up your SageMaker environment and permissions, download the abalone dataset from the UCI Machine Learning Repository, do some basic processing and visualization on the data, then save the data as .csv .
Access Notebook Instances - SageMaker ~ AWS Documentation SageMaker Developer Guide. Access Notebook Instances. To access your SageMaker notebook instances, choose one of the following options: Use the console. Choose Notebook instances. The console displays a list of notebook instances in your account. To open a notebook instance with a standard Jupyter interface, choose Open Jupyter for that instance. To open a .