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Machine Learning

jfrog

MLOps Your Way with the JFrog Platform

Jan 23, 2025 By Bill Manning In JFrog
Just like in traditional software development, creating AI applications isn’t a one size fits all approach. However, many of the challenges and concerns facing AI/ML development teams share common threads – difficulties getting models to production, tangled infrastructure, data quality, security issues, and so on.
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civo

Beginner's guide to getting started in machine learning

Jan 20, 2025 By Barry Ugochukwu In Civo
Machine learning (ML) has shifted from being a niche research field to a powerhouse behind many technologies we use daily. From personalized recommendations on streaming platforms to chatbots and image recognition, ML’s influence is everywhere. But what exactly is machine learning, and why should you invest time in learning about it? This blog will walk you through ML’s fundamentals, explain what you need to know, and outline a practical step-by-step plan to start your ML journey.
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opsmatters

Your 2025 Tech Toolkit: How to Keep Your Business on Top

Jan 16, 2025 By OpsMatters In OpsMatters
Technology is advancing faster than ever, redefining the way businesses operate, interact with customers, and remain competitive. In 2025, embracing innovation isn't just an advantage-it's a necessity for survival and growth. Studies show that businesses leveraging advanced technologies like AI, IoT, and real-time data are better equipped to streamline operations and enhance customer satisfaction. From automating workflows to creating personalized experiences, the right tech tools unlock endless opportunities.
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jfrog

Breaking Silos: Unifying DevOps and MLOps into a Cohesive Software Supply Chain - Part 3

Jan 15, 2025 By Yuval Fernbach In JFrog
The synergy between DevOps and MLOps is more crucial now than ever. However, merging these two paradigms into a coherent software supply chain poses a unique set of challenges that can leave teams feeling overwhelmed. From the complexities of managing model dependencies to adapting conventional CI/CD tools for advanced machine learning (ML) workflows, the path to integration isn’t without its twists and turns.
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Komodor

Why Kubernetes Is Becoming the Platform of Choice for Running AI/MLOps Workloads

Jan 14, 2025 By Mickael Alliel, DevOps Tech Lead In Komodor
Artificial intelligence (AI) and machine learning operations (MLOps) have become crucial across a wide swath of industries, with the two technologies working in tandem to provide value. AI enables data-driven insights and automation, while MLOps ensures efficient management of AI models throughout their lifecycle. With AI’s growing complexity and scale, organizations need robust infrastructure to manage intensive computational tasks, giving rise to platforms like Kubernetes.
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2bcloud

Solving E-Commerce's Cold Start Problem with Azure ML

Jan 2, 2025 By Mark Valman In 2bcloud
Imagine visiting an e-commerce site that instantly understands your preferences, offering tailored product recommendations from the first click. For our client, this vision was about creating a seamless, engaging experience for new users by providing immediate, personalized suggestions. Using Azure ML Studio, we turned this vision into reality by solving key challenges like the “cold start problem” and building a robust recommendation system. Here’s how we made it happen.
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jfrog

Breaking Silos: Unifying DevOps and MLOps into a Cohesive Software Supply Chain - Part 2

Dec 19, 2024 By Yuval Fernbach In JFrog
In this blog series, we will explore the importance of merging DevOps best practices with MLOps to bridge this gap, enhance an enterprise’s competitive edge, and improve decision-making through data-driven insights. Part one discussed the challenges of separate DevOps and MLOps pipelines and outlined a case for integration.
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jfrog

Breaking Silos: Unifying DevOps and MLOps into a Cohesive Software Supply Chain - Part 1

Dec 4, 2024 By Yuval Fernbach In JFrog
As businesses realized the potential of artificial intelligence (AI), the race began to incorporate machine learning operations (MLOps) into their commercial strategies. But the integration of machine learning (ML) into the real world proved challenging, and the vast gap between development and deployment was made clear. In fact, research from Gartner tells us 85% of AI and ML fail to reach production.
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datadog

Monitor AWS Trainium and AWS Inferentia with Datadog for holistic visibility into ML infrastructure

Dec 3, 2024 By Anjali Thatte In Datadog
AWS Inferentia and AWS Trainium are purpose-built AI chips that—with the AWS Neuron SDK—are used to build and deploy generative AI models. As models increasingly require a larger number of accelerated compute instances, observability plays a critical role in ML operations, empowering users to improve performance, diagnose and fix failures, and optimize resource utilization.
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