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MLOps Challenges in Industry 4.0
An important part of the Industry 4.0 vision is the use of machine learning (ML) techniques to create novel capabilities and flexibility in...
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The Future of MLOps
As AI/ML matures and businesses increasingly rely on them for business competitive advantages, such as enhancing customer experience and driving...
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Toward a safe MLOps process for the continuous development and safety assurance of ML-based systems in the railway domain
Traditional automation technologies alone are not sufficient to enable driverless operation of trains (called Grade of Automation (GoA) 4) on...
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Operationalizing Machine Learning Using Requirements-Grounded MLOps
[Context & Motivation] Machine learning (ML) use has increased significantly, [Question/Problem] however, organizations still struggle with...
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Foundations for MLOps Systems
In this chapter, we will discuss foundations for MLOps systems by breaking down the topic into fundamental building blocks that you will apply in...
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Infrastructure for MLOps
This chapter is about infrastructure. You might think of buildings and roads when you hear the word infrastructure, but in MLOps, infrastructure...
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An Analysis of the Barriers Preventing the Implementation of MLOps
The rapid improvements in machine learning (ML) and the increasing importance of ML models in numerous industries have resulted in the emergence of...
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MLOps with Ray Best Practices and Strategies for Adopting Machine Learning Operations
Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and...
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Introduction to MLOps
Machine learning (ML) has proven to be a very powerful tool to learn and extract patterns from data. The ability to generate, store, and process a...
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Introducing MLOps
As data scientists we enjoy getting to see the impact of our models in the real world, but if we can’t get that model into production, then the data...
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MLOps Lifecycle Toolkit A Software Engineering Roadmap for Designing, Deploying, and Scaling Stochastic Systems
This book is aimed at practitioners of data science, with consideration for bespoke problems, standards, and tech stacks between industries. It will...
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MLOps Adoption Strategies and Case Studies
There is no doubt that we are in the heady days of AI/ML operationalization. According to a Gartner article about the “IT Budgets Are Growing. Here’s...
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Applications of MLOps in the Cognitive Cloud Continuum
Background. Since the rise of Machine Learning, the automation of software development has been a desired feature. MLOps is targeted to have the same...
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Towards Regulatory-Compliant MLOps: Oravizio’s Journey from a Machine Learning Experiment to a Deployed Certified Medical Product
Agile software development embraces change and manifests working software over comprehensive documentation and responding to change over following a...
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What Is MLOps?
In this chapter, we will cover the concepts behind the term “MLOps” and go over what it is, why it’s useful, and how it’s implemented.
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Development of MLOps Platform Based on Power Source Analysis for Considering Manufacturing Environment Changes in Real-Time Processes
Smart factories have led to the introduction of automated facilities in manufacturing lines and the increase in productivity using semi-automatic...
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Agility in Software 2.0 – Notebook Interfaces and MLOps with Buttresses and Rebars
Artificial intelligence through machine learning is increasingly used in the digital society. Solutions based on machine learning bring both great...
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Beginning MLOps with MLFlow Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure
Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud,...
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How far are we with automated machine learning? characterization and challenges of AutoML toolkits
Automated Machine Learning aka AutoML toolkits are low/no-code software that aim to democratize ML system application development by ensuring rapid...
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Unlabeled learning algorithms and operations: overview and future trends in defense sector
In the defense sector, artificial intelligence (AI) and machine learning (ML) have been used to analyse and decipher massive volumes of data, namely...
