Systems | Development | Analytics | API | Testing

Choosing the Right-Sized LLM for Quality and Flexibility: Optimizing Your AI Toolkit

LLMs are the foundation of gen AI applications. To effectively operationalize and de-risk LLMs and ensure they bring business value, organizations need to consider not just the model itself, but the supporting infrastructure, including GPUs and operational frameworks. By optimizing them to your use case, you can ensure you are using an LLM that is the right fit to your needs.

MLRun v1.7 Launched - Solidifying Generative AI Implementation and LLM Monitoring

As the open-source maintainers of MLRun, we’re proud to announce the release of MLRun v1.7. MLRun is an open-source AI orchestration tool that accelerates the deployment of gen AI applications, with features such as LLM monitoring, fine-tuning, data management, guardrails and more. We provide ready-made scenarios that can be easily implemented by teams in organizations.

Gen AI for Marketing - From Hype to Implementation

Gen AI has the potential to bring immense value for marketing use cases, from content creation to hyper-personalization to product insights, and many more. But if you’re struggling to scale and operationalize gen AI, you’re not alone. That’s where most enterprises struggle. To date, many companies are still in the excitement and exploitation phase of gen AI. Few have a number of initial pilots deployed and even fewer have simultaneous pilots and are building differentiating use cases.

Gen AI for Marketing - From Hype to Implementation - MLOps Live #32 with McKinsey and Iguazio

In this MLOps Live session we were joined by Eli Stein, Partner and Modern Marketing Capabilities Leader at McKinsey, to delve into how data scientists can leverage generative AI to support the company’s marketing strategy. We showcased a live demo of a customer-facing AI agent developed for a jewelry retailer, which can be used as a marketing tool to offer personalized product recommendations and purchasing information and support. Following the demo, we held an interactive discussion and Q&A session. Enjoy!

Implementing Gen AI in Regulated Sectors: Finance, Telecom, and More

If 2023 was the year of gen experimentation, 2024 is the year of gen AI implementation. As companies embark on their implementation journey, they need to deal with a host of challenges, like performance, GPU efficiency and LLM risks. These challenges are exacerbated in highly-regulated industries, such as financial services and telecommunication, adding further implementation complexities. Below, we discuss these challenges and present some best practices and solutions to take into consideration.