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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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canonical

Charmed Kubeflow vs Kubeflow

Nov 1, 2024 By Andreea Munteanu In Canonical
Kubeflow is an open source MLOps platform that is designed to enable organizations to scale their ML initiatives and automate their workloads. It is a cloud-native solution that helps developers run the entire machine learning lifecycle within a single solution on Kubernetes. It can be used to develop, optimize and deploy models. This blog will walk you through the benefits of using an official distribution of the Kubeflow project.
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jfrog

swampUP Recap: "EveryOps" is Trending as a Software Development Requirement

Oct 23, 2024 By The JFrog Team In JFrog
swampUP 2024, the annual JFrog DevOps Conference, was unique in it’s addressing not only more familiar DevOps and DevSecOps issues, but adding specific operational challenges, stemming from the explosive growth of GenAI and the resulting need for specialized capabilities for handling AI models and datasets, while supporting new personae such as AI/ML engineers, data scientists and MLOps professionals.
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opsmatters

Accelerating Edge AI: Infineon Introduces Development Kit for ML Innovations

Oct 23, 2024 By OpsMatters In OpsMatters
The PSoC 6 AI Evaluation Kit is purpose-built for developers who need to bring AI capabilities to the edge, where real-time decision-making and energy efficiency are crucial. Unlike traditional cloud-based systems, where data must be transmitted to remote servers for processing, the PSoC 6 solution enables inference to occur directly at the data source-right at the sensor. This architecture provides numerous advantages, including.
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jfrog

Feature Store Benefits: The Advantages of Feature Stores in Machine Learning Development

Oct 22, 2024 By Pavel Klushin In JFrog
Feature stores are rapidly growing in popularity as organizations look to improve their machine learning productivity and operations (MLOps). With the advancements in MLOps, feature stores are becoming an essential component of the machine learning infrastructure, helping organizations to improve the performance and ability to explain their models, and accelerate the integration of new models into the production.
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opsmatters

How to Choose Your Machine Learning Specialization as a Student

Oct 17, 2024 By OpsMatters In OpsMatters
Specialization helps unlock new abilities to elevate your career in one of the most dynamic and fast-evolving niches. Machine learning is a relevant niche across all industries, from healthcare to finance, so there are various paths to explore. However, deciding what to settle for can become confusing with the many options. It's like when you want to pay for essay services; there are numerous essay writing websites, which make it challenging to determine which one suits your needs, and thus, you have to be careful with your selection to pick the right one.
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