How Palo Alto Networks uses BigQuery ML to automate resource classification
Palo Alto Networks used BigQuery ML to automatically identify and label the owner, environment, and cost center for each of its cloud projects.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Machine Learning
Open Source Fractional GPUs for Everyone, Now Available from ClearML
If you’ve been following our news, you know we just announced free fractional GPU capabilities for open source users, enabling multi-tenancy for NVIDIA GPUs and allowing users to optimize their GPU utilization to support multiple AI workloads as part of our open source and free tier offering.
Why You Need GPU as a Service for GenAI
GPU as a Service (GPUaaS) serves as a cost-effective solution for organizations who need more GPUs for their ML and gen AI operations. By optimizing the use of existing resources, GPUaaS allows organizations to build and deploy their applications, without waiting for new hardware. In this blog post, we explain how GPUaaS as a service works, how it can close the GPU shortage gap, when to use GPUaaS and how it fits with gen AI.
The State of AI Infrastructure at Scale 2024
In our latest research, conducted this year with AIIA and FuriosaAI, we wanted to know more about global AI Infrastructure plans, including respondents’: 1) Compute infrastructure growth plans 2) Current scheduling and compute solutions experience, and 3) Model and AI framework use and plans for 2024. Read on to dive into key findings! Download the survey report now →
Automate building ML apps with Databricks, AutoML and Fivetran
Fivetran and Databricks bring you machine learning, simplified.
Gen AI for Customer Service Demo
Iguazio would like to introduce two practical demonstrations showcasing our call center analysis tool and our innovative GenAI assistant. These demos illustrate how our GenAI assistant supports call center agents with real-time advice and recommendations during customer calls. This technology aims to improve customer interactions and boost call center efficiency. We're eager to share how our solutions can transform call center operations.
Best 10 Free Datasets for Manufacturing [UPDATED]
The manufacturing industry can benefit from AI, data and machine learning to advance manufacturing quality and productivity, minimize waste and reduce costs. With ML, manufacturers can modernize their businesses through use cases like forecasting demand, optimizing scheduling, preventing malfunctioning and managing quality. These all significantly contribute to bottom line improvement.
Implementing a Gen AI Smart Call Center Analysis App - MLOps Live #26 with McKinsey
Many enterprises operate expansive call centers, employing thousands of representatives who provide support and consult with clients, often spanning various time zones and languages. However, the successful implementation of a gen AI-driven smart call center analysis applications presents unique challenges such as data privacy controls, potential biases, AI hallucinations, language translation and more.
Technical Deep-dive - Unlock the Power of Data with AI, Machine Learning & Automation - Part 2
We delve into Generative AI capabilities, seamless application automation integration, and robust machine learning using AutoML. The webinar aims to unravel the behind-the-scenes magic that powers the application. Attendees can anticipate gaining valuable insights into the methodologies and technologies that contribute to enhanced predictability and data-driven decision-making.
Implementing Gen AI for Financial Services
Gen AI is quickly reshaping industries, and the pace of innovation is incredible to witness. The introduction of ChatGPT, Microsoft Copilot, Midjourney, Stable Diffusion and many more incredible tools have opened up new possibilities we couldn’t have imagined 18 months ago. While building gen AI application pilots is fairly straightforward, scaling them to production-ready, customer-facing implementations is a novel challenge for enterprises, and especially for the financial services sector.