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Machine Learning

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

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.

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

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.

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

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.

Charmed Kubeflow vs Kubeflow

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.

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

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.

Accelerating Edge AI: Infineon Introduces Development Kit for ML Innovations

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.

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

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.

How to Choose Your Machine Learning Specialization as a Student

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.

Azure Machine Learning Pricing - 2024 Guide to ML Costs

Undoubtedly, AI is our future—which means it’s past time to integrate machine learning models into your FinOps multi-cloud tech stack. AI turns simple tasks into something that can be executed at the click of a button. With well-trained models, FinOps, MSPs, and Enterprises can automate cost detection, forecasting, and anomaly identification, streamlining complex financial operations without increasing their workforce. The good news?