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AI in DevOps: Streamlining Software Deployment and Operations

Unite.AI

As emerging DevOps trends redefine software development, companies leverage advanced capabilities to speed up their AI adoption. That’s why, you need to embrace the dynamic duo of AI and DevOps to stay competitive and stay relevant. How does DevOps expedite AI? How will DevOps culture boost AI performance?

DevOps 310
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Mastering MLOps : The Ultimate Guide to Become a MLOps Engineer in 2024

Unite.AI

MLOps, or Machine Learning Operations, is a multidisciplinary field that combines the principles of ML, software engineering, and DevOps practices to streamline the deployment, monitoring, and maintenance of ML models in production environments. ML Operations : Deploy and maintain ML models using established DevOps practices.

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AI and the Gig Economy: Opportunity or Threat?

Unite.AI

However, AI tools are not just for creatives but also for more complex fields, such as DevOps, finance, and even legal. Tools that handle analytics, coding assistance, and even financial modelling are turning such complex duties into more simple tasks for those willing to learn.

AI Tools 124
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

The use of multiple external cloud providers complicated DevOps, support, and budgeting. Operational consolidation and reliability Post-migration, our DevOps and SRE teams see 20% less maintenance burden and overheads. These operational inefficiencies meant that we had to revisit our solution architecture.

DevOps 84
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Breaking Down the O’Reilly 2024 Tech Trends Report

Unite.AI

Application security topics rose by 42%, and DevSecOps – which integrates security practices within the DevOps process – experienced a 30% growth in usage. As technology continues to advance rapidly, professionals seek to enhance their qualifications and stay abreast of the latest trends and best practices.

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How MLOps is Transforming AI Deployment and Management in the Real World

Mlearning.ai

It handles everything from initial creation of the model to successful deployment and continuous learning. Extension Of Devops MLOps is an extension of DevOps. DevOps aims to streamline the development and operation of software applications, while MLOps focuses on the machine learning lifecycle.

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Introducing the Amazon Comprehend flywheel for MLOps

AWS Machine Learning Blog

MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the ML development lifecycle. Learn more about Simplify continuous learning of Amazon Comprehend custom models using Comprehend flywheel.