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Machine Learning

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.

Best 13 Free Financial Datasets for Machine Learning [Updated]

Financial services companies are leveraging data and machine learning to mitigate risks like fraud and cyber threats and to provide a modern customer experience. By following these measures, they are able to comply with regulations, optimize their trading and answer their customers’ needs. In today’s competitive digital world, these changes are essential for ensuring their relevance and efficiency.

Nuclio Demo

Nuclio is a high-performance serverless framework focused on data, I/O, and compute intensive workloads. It is well integrated with popular data science tools, such as Jupyter and Kubeflow; supports a variety of data and streaming sources; and supports execution over CPUs and GPUs. The Nuclio project began in 2017 and is constantly and rapidly evolving; many start-ups and enterprises are now using Nuclio in production. In this video, Tomer takes you through a quick demo of Nuclio, triggering functions both from the UI and the CLI.

LLMOps vs. MLOps: Understanding the Differences

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. But a successful deployment of LLMs has to go beyond prototyping, which is where LLMOps comes into play. LLMOps is MLOps for LLMs. It’s about ensuring rapid, streamlined, automated and ethical deployment of LLMs to production. This blog post delves into the concepts of LLMOps and MLOps, explaining how and when to use each one.

The Rise of ML-Centric Technology Consulting in 2024 and Beyond

Businesses globally are witnessing the transformational impact of applied AI and machine learning (ML) capabilities during this blossoming chapter of the Information Age. Therefore, the demand for niche ML consulting services will continue its robust growth trajectory as we enter the year 2024. An increasing number of enterprises are partnering with ML specialists and boutique tech consultants to craft their AI-driven future.