UbiOps - powerful AI model serving & orchestration’s Post

Ryan Farris, VP of product at Qumulo, joins Alan Shimel for a discussion focusing on the trade-offs cloud architects were required to build around using traditional file systems when constructing AI infrastructure. They also discuss how Azure Native Qumulo resolves those trade-offs, decreasing GPU time and significantly lowering costs without sacrificing performance. Watch the full interview here: https://lnkd.in/eqFXuuDz #aiworkloads #cloudstorage #storage #storageperformance #IT #Tech #CloudNative #GPU #AI #AIOps

Excited to be part of the "Hybrid AI + FINOPS Strategies" panel in NYC on September 11th! 🚀 I’ll be joining industry leaders to discuss how AI is reshaping cloud platforms and share insights on optimizing costs while scaling AI-driven applications. Whether you're into tech innovation or looking to sharpen your AI + FINOPS game, this event is not to be missed. See you there! 💡 Register here: https://lnkd.in/eKifzA-t Adam de Delva <> Chris Lawlor <> Matt Biringer #AI #Cloud #FINOPS #AIConsulting #TechInnovation #AWS

[New on our blog] Best Tools For ML Model Serving by Axel Mendoza TL;DR → Model serving is an essential step in building machine-learning products. It comprises packaging models, building APIs, monitoring performance, and scaling to adjust to incoming requests. → The choice of a model-serving tool depends on specific project and team needs, such as frameworks and infrastructure compatibility, ease of use, inference optimization features, monitoring capabilities, and required deployment strategies. → Broadly speaking, model-serving tools can be divided into two categories: model-serving runtimes that package ML models into inference-optimized containers and model-serving platforms that focus on deploying and scaling these models. → Various tools exist on the market today, each with specific strengths and weaknesses. BentoML, TensorFlow Serving, TorchServe, Nvidia Triton, and Titan Takeoff are leaders in the model-serving runtime category. When it comes to model-serving platforms, KServe, Seldon Core, Bento Cloud, and cloud providers’ integrated solutions are the top contenders. — (link to the full article in the comments) #ML #MLOps #ModelServing

Amazon Web Services' (AWS) new feature to fine-tune in-house AI models and their recent enhancements to the Amazon Bedrock platform are indicative of the growing enterprise demand for tailor-made AI solutions. This lines up with AWS's enhanced AI and ML capabilities, courtesy of the Titan Image Generator, Llama 3 models, Claude 2.1, and other bedrock features. Strengthening the Kubernetes ecosystem, CloudBolt Software & StormForge’s alliance combines the best of both platforms, optimizing IT operations, and potentially bringing down costs. Additionally, NVIDIA's acquisition of Run:ai connects Kubernetes orchestration with NVIDIA's DGX Cloud. Both moves focus on optimizing resources and easing complexities. Trader beware though, as while solutions like these offer immediate benefits, the long-term implications of lock-in scenarios need careful examination. Public cloud advancements also mark the need for businesses to better understand & manage their digital assets, which is where Consult IP excels. Our tailored services allow efficient IT procurement, secure cloud transitions, and cost-effective solutions. We handle the complexities so you don't have to. #AWS #Kubernetes #AI #CloudAdvancements #ConsultIP

Nuro and Google team up to share how you can supercharge your AI journey with Google Cloud's AI Hypercomputer. Discover the secrets behind taking AI from concept to real-world impact. #AI #GoogleCloud #Nuro #Innovation #googlecloudnext #next24 #nextlasvegas

SkyPilot - The Cloud Agnostic Framework for Running LLMs, AI, and Batch Jobs 🚀 I'm excited to share SkyPilot - a powerful framework that allows you to run large language models (LLMs), AI workloads, and batch jobs seamlessly across any cloud provider. With SkyPilot, you can unlock maximum cost savings, highest GPU availability, and managed execution for your compute intensive workloads. Key features of SkyPilot: ✨ Abstracts away cloud infrastructure burdens - Launch jobs & auto-scaling clusters on AWS, GCP, Azure, and more - Easy integration with object stores like S3, GCS, R2 - Queue and run many jobs with automatic management ⚡ Maximizes GPU availability - Provision across all zones, regions and clouds you have access to - Automatic failover for high availability 💰 Cuts your cloud costs - Managed Spot: 3-6x savings using spot VMs with auto-recovery - Optimizer: 2x savings by auto-picking cheapest VM/zone/region/cloud - Autostop: Hands-free cleanup of idle clusters SkyPilot supports existing GPU, TPU and CPU workloads with no code changes required. It integrates seamlessly with LLM frameworks like Llama, Vicuna, Mistral, GPT-J and more. Check out the tutorials and runnable examples: https://lnkd.in/drmnE_ZT Join the growing community leveraging SkyPilot to unlock the true potential of LLMs and AI on the cloud! Let me know if you have any questions. #LLMs #Technology #artificialintelligence #datascience #machinelearning

Grafana Cloud & Generative AI Lunch & Learn Understand how Grafana Cloud is harnessing the power of generative AI and machine learning to deliver a better observability experience to you. Watch our 1-hour session to see all the ways in which machine learning can take the guesswork out of site reliability engineering, how AI can simplify incident handling, and to get a glimpse of the future of observability in a post-AI world. Register Now! https://lnkd.in/gmj4kwiQ

Want to learn about how you can leverage AlloyDB Omni to create rich new generative AI experiences that run on cloud and on premises? Join us Google Cloud NEXT '24. Gurmeet G. and I will be talking about how AlloyDB Omni can help you build Generative AI applications that meet your compliance needs and run in connected and disconnected environments in just a few steps. Hear from Sharanya Arcot Desai, Director of AI and Technical Fellow at NeuroPace, on how Neuropace was able to use AlloyDB Omni as a vector database that runs on premises and meets their compliance requirements. #Google #GenAI #databases #cloudconference #Googlecloud #AlloyDB

𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐢𝐧𝐠 𝐭𝐡𝐞 𝐌𝐋𝐎𝐩𝐬 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 🚀 In our quest for MLOps success, it's essential to continually refine our approach. Here are further strategies to enhance your MLOps practices: 🔹 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: Implement automated pipelines to streamline model training, testing, and deployment processes. 🔹 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐚𝐧𝐝 𝐌𝐚𝐢𝐧𝐭𝐚𝐢𝐧: Use robust monitoring tools to track model performance and ensure reliability over time. 🔹 𝐒𝐜𝐚𝐥𝐞 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭𝐥𝐲: Leverage cloud infrastructure to scale your ML operations as needed, ensuring flexibility and cost-effectiveness. ✨ How are you optimizing your MLOps processes? Share your tips and experiences below! ✨ #MLOps #MachineLearning #DataScience #AI #Innovation

Want to optimize your GKE autoscaling for cost efficiency? 🚀 With Google Kubernetes Engine (GKE), you can unlock the power of autoscaling and only pay for what you need. Autoscaling can save costs by ensuring that workloads start and stop based on demand. To achieve this, you can make the most of the four scalability dimensions, including horizontal and vertical scaling. Additionally, you can learn about key features such as the Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), Cluster Autoscaler, and Node auto-provisioning. Work together with OneSight to fine-tune GKE autoscaling for an efficient and cost-effective Kubernetes journey. . . . . #onesight #GKE #Ks8 #kubernetes #Autoscaling #ScalabilitySolutions #CloudOptimization #ContainerOrchestration #CloudComputing #FutureTech #azure #ai #CloudTechnology #HorizontalScaling #VerticalScaling #HPA #ClusterAutoscaler #technology #itservice #technology #CloudServices #Cloudsecurity #cloudstorage #digitalservices #aks #usa