Douglas Turner’s Post

https://lnkd.in/gGbYcYNM You can test drive TPU v6e, which is Google's latest generation AI accelerator for your LLM. This document is a good starting point if you need the granular control, scalability, resilience, portability, and cost-effectiveness of managed Kubernetes when you deploy and serve your AI/ML workloads #tpu #vllm #oss #google #gke #genAI

Interested in AI from an engineers point of view? Take a look at this : https://lnkd.in/dVqswNhe Just completed this comprehensive course by Intel, offering essential insights into Machine Learning Operations. It's a great resource for anyone keen on enhancing their AI skills. Best part? The course is free! #MLOps #MachineLearning #IntelCertifiedDeveloper

University of California, Berkeley is pushing the boundaries of AI with SqueezeLLM, optimizing large language model inference to be more efficient and accessible. Using the SYCLomatic tool to migrate code from CUDA to SYCL, Intel oneAPI Base Toolkit, and Intel Tiber AI Cloud, they've significantly reduced the computational load, enabling faster and more cost-effective AI solutions. This collaboration showcases how innovation in AI model optimization can drive real-world impact. Learn more: https://intel.ly/4fpV5me #oneAPI #AI #SYCL

Google Cloud's Computer Vision is your digital eye on the world! It recognizes objects, reads text, and even detects faces faster than you can say "cheese!" Perfect for turning images into actionable insights, it’s like having a super-smart assistant that sees everything—minus the sunglasses and cool demeanor! #MachineLearning #GoogleCloud #ContinousLearning

By fine-tuning large language models (LLMs), Life Science organisations can unlock new levels of efficiency, collaboration, and innovation, accelerating development cycle times and improving patient outcomes. The OCI Generative AI Playground offers a user-friendly interface, allowing researchers to adapt pre-trained LLMs to their proprietary internal data, unlocking invaluable insights without the resource-intensive process of training a model from scratch. The ability to efficiently adapt language models will be crucial for streamlining clinical data flow automation and data-driven decision making as the industry moves towards metadata-driven automation. This approach has significant applicability in the world of clinical trials and research. Read more about fine-tuning LLMs here: https://lnkd.in/eJDPGuPU

Generally, a set of frames represents a video structure, clips, or scenes. A segmentation process including breaking down a video sequence into its main components should pre-processed video analysis-based classification methods. Recently, Deep Learning Models-based video analysis and classification approaches have been grown-up and developed to be more concise and convenient for modern technologies such as big data, cloud computing, video surveillance, and video summarization systems. This paper focuses on the knowledge related to deep learning-based methods to achieve object detection and tracking along with video sequences. Our revision presents and discusses various studies of video classification tasks. Further, the fundamental purpose of this research is to look at which of these techniques affected mainly the performance of video classification tasks and the main parameters required to design and implement an efficient video classification system with relative challenges. A comparison study is performed on various types of video classification models to highlight the strong points of each model with a comprehensive analysis of its performance evaluation based on accuracy metrics.

Imagine debugging an elusive issue that only occurs under specific conditions. In the past, this could take days of combing through logs and code. But now, with AI-driven observability tools, patterns and anomalies are identified in real-time. Cloud platforms store and analyse immense amounts of data, spotlighting issues instantly. This evolution in debugging isn't just a time-saver; it's a game-changer for software reliability. #webevolution #webdeveloper #ai

Develop and run your own AI projects without the heavy investment in hardware! With the Private #AIServer you'll have a secure, flexible, and #unmanaged server solution tailored to your business needs. 🚀   👉🏼 https://lnkd.in/e9tvTttq   With no restrictions, you have the freedom to scale and choose resources for your AI Server as your projects demands. ⚙️   Whether you're working on AI applications, deep or machine learning, training large language models, finetuning or even image generation – the possibilities are limitless with your choice of an A100-, H100- or Finetuning-Server.    Within a dedicated Cloud environment, we provide complete control and high security for your AI infrastructure. 🛡️   Dive into the world of AI with Cloudiax and say goodbye to high upfront costs – our powerful technology is available at a flat rate with no usage-based or hidden costs. 📈   #AIforBusiness #AIsolution #GPUserver #CloudProvider #PrivateCloud

As discussed at AI Field Day, Solidigm's #storage system addresses the diverse and intense demands of #AI workloads, ensuring GPUs remain constantly fed with data. Recognizing the variety in I/O patterns and the substantial data processing required for machine learning, Solidigm's SSDs offer superior performance for sequential and random read/write activities, consuming less power and space in #datacenters. Additionally, their Cloud Storage Acceleration Layer (CSAL) software enhances the durability and efficiency of SSDs by optimizing write patterns, making it an open-source boon for those seeking to streamline AI-based operations. Read more in this Gestalt IT article by Sulagna Saha. #AIFD4

UBITECH introduces a scalable and fully generalizable AI Theoretical Framework. The framework is an AI-verified software solution and benchmarking tool that enables the measurement, storage, and visualization of energy consumption and hardware resource utilization during AI models' preparation and training phase at edge and cloud contexts. The framework leverages containerization to encapsulate AI model training. It deploys it within a Kubernetes cluster and benchmarks several widely used AI algorithms across different data modalities, including tabular, image, and time-series data.