Products related to Scale:
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MLOps Engineering at Scale
Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers. Cloud Native Machine Learning helps you bridge that gap by using the pre-built services provided by cloud platforms like Azure and AWS to assemble your ML system’s infrastructure.Following a real-world use case for calculating taxi fares, you’ll learn how to get a serverless ML pipeline up and running using AWS services.Clear and detailed tutorials show you how to develop reliable, flexible, and scalable machine learning systems without time-consuming management tasks or the costly overheads of physical hardware. about the technologyYour new machine learning model is ready to put into production, and suddenly all your time is taken up by setting up your server infrastructure.Serverless machine learning offers a productivity-boosting alternative.It eliminates the time-consuming operations tasks from your machine learning lifecycle, letting out-of-the-box cloud services take over launching, running, and managing your ML systems.With the serverless capabilities of major cloud vendors handling your infrastructure, you’re free to focus on tuning and improving your models. about the book Cloud Native Machine Learning is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers.You’ll start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled.Next, you’ll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training.Finally, you’ll tune and engineer your serverless machine learning pipeline for scalability, elasticity, and ease of monitoring with the built-in notification tools of your cloud platform.When you’re done, you’ll have the tools to easily bridge the gap between ML models and a fully functioning production system. what's inside Extracting, transforming, and loading datasetsQuerying datasets with SQLUnderstanding automatic differentiation in PyTorchDeploying trained models and pipelines as a service endpointMonitoring and managing your pipeline’s life cycleMeasuring performance improvements about the readerFor data professionals with intermediate Python skills and basic familiarity with machine learning.No cloud experience required. about the author Carl Osipov has spent over 15 years working on big data processing and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms.While at IBM, Carl helped IBM Software Group to shape its strategy around the use of Docker and other container-based technologies for serverless computing using IBM Cloud and Amazon Web Services.At Google, Carl learned from the world’s foremost experts in machine learning and also helped manage the company’s efforts to democratize artificial intelligence.You can learn more about Carl from his blog Clouds With Carl.
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Scale Development : Theory and Applications
Scale Development: Theory and Applications, by Robert F.DeVellis and new co-author Carolyn T. Thorpe, demystifies measurement by emphasizing a logical rather than strictly mathematical understanding of concepts.The Fifth Edition includes a new chapter that lays out the key concepts that distinguish indices from scales, contrasts various types of indices, suggests approaches for developing them, reviews validity and reliability issues, and discusses in broad terms some analytic approaches.All chapters have been updated, and the book strikes a balance between including relevant topics and highlighting recent developments in measurement while retaining an accessible, user-friendly approach to the material covered.
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Science, Technology And Innovation Indicators : Lessons from the Development Experience in Africa
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Workplace Research : Conducting small-scale research in organizations
Undertaking small-scale applied workplace-based research presents a unique set of challenges including how to conduct good research in time pressured situations and how to thrive in unfamiliar work environments.This book will help you to meet these challenges by setting out a step-by-step guide to planning, conducting and delivering top quality small-scale work-based research projects. In addition this book will help you to adapt quickly to your new surroundings.It will provide a strong strategic skill set that will enable you to navigate the workplace and ensure that your research findings inform organizational decision making.It tackles the challenge of conducting rigorous credible research FOR and WITHIN organizations and provides tangible advice for those looking to complete their research in very short time frames and with limited resources. Written by established academics, with significant experience coordinating industry/government placements and internships, the book will help you to bridge the gap between academic expectations and the requirements of the organisation, including the need to Develop a ‘pitch’ as well as a proposal;Produce powerful visualizations as well as logical narrative;Present practicable recommendations as well as findings/conclusions;Develop persuasive deliverables as well as traditional research reports;Create presentations that persuade as well as inform. Packed full of real-life examples, actionable advice and sensitive pedagogy this book will get you through your placement with confidence and style.
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How does determining the scale factor and scale work?
Determining the scale factor involves comparing the measurements of corresponding sides of two similar figures. The scale factor is the ratio of the lengths of the sides of the two figures. To find the scale factor, you can divide the length of a side of the larger figure by the length of the corresponding side of the smaller figure. The scale is the ratio of the dimensions of the actual object to the dimensions of the model or drawing. It is used to accurately represent the size of an object in a smaller or larger form.
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How does one determine the scale factor and scale?
To determine the scale factor, one must compare the measurements of corresponding sides of two similar figures. The scale factor is calculated by dividing the length of a side in the larger figure by the length of the corresponding side in the smaller figure. The scale is then determined by using the scale factor to create a ratio between the measurements of the figures. This ratio can be used to accurately resize or recreate the figures at a different scale.
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What is the difference between interval scale and ratio scale?
The main difference between interval scale and ratio scale is the presence of a true zero point. Ratio scale has a true zero point, meaning that a value of zero indicates the absence of the quantity being measured. Interval scale, on the other hand, does not have a true zero point, and a value of zero does not necessarily indicate the absence of the quantity being measured. Additionally, ratio scale allows for meaningful ratios to be calculated, while interval scale does not.
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Can a regular scale be used as a kitchen scale?
Yes, a regular scale can be used as a kitchen scale as long as it has the capacity to measure in smaller increments typically used in cooking, such as grams or ounces. However, kitchen scales are specifically designed for food measurements and often come with features like tare function, which allows you to zero out the weight of the container. So while a regular scale can be used in a pinch, a kitchen scale may be more convenient and accurate for cooking and baking purposes.
Similar search terms for Scale:
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Understanding Case Study Research : Small-scale Research with Meaning
Written in an accessible and jargon-free style, this book provides a comprehensive, student-friendly guide to the nature and use of case study research. Whether as part of a more substantial study or as the foundation for a self-contained smaller project, case studies provide viable and valuable alternatives to conducting large-scale research.Grounded in both theory and practice, this book sets out not only the key debates and ethical issues surrounding case study research, but also focuses specifically on the work of others and how you can understand, use, and write about secondary data as the basis for your own research project.With tips, examples, and extensive discussion of real-world case studies from a variety of social science and other disciplines, Tight illustrates the kinds of research to which case studies can be applied.Topics include: Types of case studiesAdvantages and disadvantages to using case studiesThe meaning and value of case study research The use of case studies in different disciplines and research designs Whether you want to know how to access and use the case studies of others or understand the methods behind conducting your own case study research, this book will take you through every step of the process!
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Data Management in Large-Scale Education Research
Research data management is becoming more complicated.Researchers are collecting more data, using more complex technologies, all the while increasing the visibility of our work with the push for data sharing and open science practices.Ad hoc data management practices may have worked for us in the past, but now others need to understand our processes as well, requiring researchers to be more thoughtful in planning their data management routines. This book is for anyone involved in a research study involving original data collection.While the book focuses on quantitative data, typically collected from human participants, many of the practices covered can apply to other types of data as well.The book contains foundational context, instructions, and practical examples to help researchers in the field of education begin to understand how to create data management workflows for large-scale, typically federally funded, research studies.The book starts by describing the research life cycle and how data management fits within this larger picture.The remaining chapters are then organized by each phase of the life cycle, with examples of best practices provided for each phase.Finally, considerations on whether the reader should implement, and how to integrate those practices into a workflow, are discussed. Key Features:Provides a holistic approach to the research life cycle, showing how project management and data management processes work in parallel and collaborativelyCan be read in its entirety, or referenced as needed throughout the life cycleIncludes relatable examples specific to education researchIncludes a discussion on how to organize and document data in preparation for data sharing requirementsContains links to example documents as well as templates to help readers implement practices
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Exploring Rating Scale Functioning for Survey Research
Items with ordered response categories are common in survey research, such as when respondents are asked how much they agree with certain statements.But how large are the differences between categories of response, and how well do they distinguish between respondents?This volume is the first to introduce the evaluation of rating scales to an audience of survey researchers.Evaluating Rating Scale Functioning for Survey Research provides researchers with an overview of rating scale analysis along with practical guidance on how to conduct such analyses with their own survey data.Author Stefanie A. Wind presents three categories of methods: Rasch models; non-Rasch Item Response Theory (IRT) models; and non-parametric models, together with practical examples.Tutorials, datasets, and software code (R and Facets) to accompany the book are available on the book’s website.
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Digital Pocket Scale 100G-0.01G Mini Scale Electronic Gauge Scale Electronic Scale Table Scale
Digital Pocket Scale 100G-0.01G Mini Scale Electronic Gauge Scale Electronic Scale Table Scale
Price: 3.99 € | Shipping*: 1.99 €
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What does the bathroom scale or the kitchen scale show?
The bathroom scale shows a person's weight, typically in pounds or kilograms. It is used to measure and track changes in weight over time. On the other hand, the kitchen scale shows the weight of food or ingredients, often in grams or ounces. It is used to measure precise amounts of ingredients for cooking and baking. Both scales are important tools for monitoring and managing weight and portion sizes.
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What is the major difference between N scale and Z scale?
The major difference between N scale and Z scale model trains is their size. N scale trains are larger than Z scale trains, with N scale trains typically being 1:160 scale and Z scale trains being 1:220 scale. This means that Z scale trains are smaller and more compact, allowing for more intricate layouts in a smaller space. Additionally, N scale trains are more popular and have a wider variety of available models and accessories compared to Z scale.
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Do a gold scale and a kitchen scale weigh the same?
No, a gold scale and a kitchen scale do not weigh the same. A gold scale is typically more precise and accurate in measuring very small weights, such as those of precious metals like gold. On the other hand, a kitchen scale is designed for measuring larger quantities of ingredients for cooking and baking. The two scales are calibrated differently to suit their specific purposes.
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Does anyone know how to use the prism scale triangular scale?
The prism scale triangular scale is a specialized tool used in drafting and design to accurately measure and scale drawings. To use it, align the desired scale on the tool with the drawing and read the measurements accordingly. The triangular shape allows for easy maneuvering and precise measurements. If you are unfamiliar with how to use it, it may be helpful to refer to the scale's instructions or seek guidance from someone experienced in drafting.
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