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Machine Learning

Predict Known Categorical Outcomes with Snowflake Cortex ML Classification, Now in Public Preview

Today, enterprises are focused on enhancing decision-making with the power of AI and machine learning (ML). But the complexity of ML models and data science techniques often leaves behind organizations without data scientists or with limited data science resources. And for those organizations with strong data analyst resources, complex ML models and frameworks may seem overwhelming, potentially preventing them from driving faster, higher-quality insights.

LLM Validation & Evaluation MLOps Live #27 with Tasq.ai

In this session, Yaron Haviv, CTO Iguazio was joined by Ehud Barnea, PHD, Head of AI at Tasq.ai and Guy Lecker ML Engineering Team Lead, Iguazio to discuss how to validate, evaluate and fine tune an LLM effectively. They shared firsthand tips of how to solve the production hurdle of LLM evaluation, improving LLM performance, eliminating risks, along with a live demo of a fashion chatbot that leverages fine-tuning to significantly improve the model responses.

Qlik AutoML Update - March 2024

Automated free text feature engineering uses sophisticated algorithms under the hood to allows far better prediction from free text fields. This complements the date feature engineering capability we released last year, which automatically parses dates into usable features. Organizations now have role-based access control for AutoML users. We’ve added two new user roles to support AutoML – experiment contributors and deployment contributors, which can be assigned to specific users or groups. With this, you can now control and limit access to AutoML to the right types of users.

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 →

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.