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Machine Learning

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.

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.

GenAI for Financial Services - MLOps Live #25 with McKinsey

Generative AI has sparked the imagination with the explosion of tools like ChatGPT, CodePilot and others, highlighting the importance of LLMs as the basis for modern AI applications. However, implementing GenAI in the enterprise is challenging, and it becomes even more difficult for banks, insurance companies, and other financial services companies. Many Financial Service companies are struggling and end up missing out on the great value of GenAI and the competitive edge it can provide.

Unlock the Power of Your Marketing Data with Snowflake Connector for Google Analytics

Imagine seamlessly integrating your Google Analytics data with Snowflake, allowing you to combine it effortlessly with other key sources like CRM, ERP, social media metrics, email campaign data, and whatever data sources compose the full scope of your data estate. The good news is that it’s possible with the native Snowflake Connector for Google Analytics, now available in public preview.

Easily Train, Manage, and Deploy Your AI Models With Scalable and Optimized Access to Your Company's AI Compute. Anywhere.

Now you can create and manage your control plane on-prem or on-cloud, regardless of where your data and compute are. We recently announced extensive new orchestration,scheduling, and compute management capabilities for optimizing control of enterprise AI & ML. Machine learning and DevOps practitioners can now fully utilize GPUs for maximal usage with minimal costs.