Although listening to music has its moments, repeating the same songs over and over again can get tedious. With so many great podcasts out there though, thereโs no reason why you canโt sub out your regular playlist for something a little more educational and insightful while you hit the gym or drive to work. Podcasts โฆ
AI/ML development is getting a lot of attention as organizations rush to bring AI services into their business applications. While emerging MLOps practices are designed to make developing AI applications easier, the complexity and fragmentation of available MLOps tools often complicates the work of Data Scientists and ML Engineers, and lessens trust in whatโs being โฆ
Just like in traditional software development, creating AI applications isnโt a one size fits all approach. However, many of the challenges and concerns facing AI/ML development teams share common threads โ difficulties getting models to production, tangled infrastructure, data quality, security issues, and so on. Regardless of how you build it, to accelerate production-ready AI, โฆ
In essence, a feature store is a dedicated repository where features are methodically stored and arranged, primarily for training models by data scientists and facilitating predictions in applications equipped with trained models. It stands as a pivotal gathering point, where one can formulate or modify collections of features drawn from a variety of data sources. โฆ
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 โฆ
At swampUP 2024 in Austin just a few days ago, we explored the EveryOps Matters approach with the crowd of developers, driven by a consolidated view from their companiesโ boardrooms and 2024 CIO surveys. The message was clear: โEveryOpsโ isnโt just a strategy or tech trend โ itโs a fundamental, ongoing mindset shift that must โฆ
Note: This blog post was previously published on Hackeroon We surveyed 1,200+ technology professionals from around the globe, including 300+ VP and C-level executives, on their AI/ML usage and software supply chain security efforts. Upon analysis, a surprising gap emerged between what executives believe is happening and what developers and engineers report is happening. Hereโs โฆ
Generative AI and Large Language Models (LLMs) are the new revolution of Artificial Intelligence, bringing the world capabilities that we could only dream about less than two years ago. Unlike previous milestones, such as Deep Learning, in the current AI revolution, everything is happening faster than ever before. Many feel that the train is about โฆ
Welcome to our hands-on guide where we dive into the world of Large Language Models (LLMs) and their synergy with Vector Databases. LLMs have been a game-changer in the tech world, driving innovation in application development. However, their full potential is often untapped when used in isolation. This is where Vector Databases step in, enhancing โฆ
With Gartner estimating that over 90% of newly created business software applications will contain ML models or services by 2027, it is evident that the open source ML revolution is well underway. By adopting the right MLOps processes and leveraging the lessons learned from the DevOps revolution, organizations can navigate the open source and proprietary โฆ
