Companies want to train and use large language models (LLMs) with their own proprietary data. Open source generative models such as Meta’s Llama 2 are pivotal in making that possible. The next hurdle is finding a platform to harness the power of LLMs. Snowflake lets you apply near-magical generative AI transformations to your data all in Python, with the protection of its out-of-the-box governance and security features.
BigQuery ML inference engine lets you run inference over custom models, remote models, and pretrained models within your machine learning workflow.
Generative AI (GenAI) has the potential to transform enterprise product operations, and as a Chief Product Officer (CPO), it’s essential to understand how to leverage generative AI to drive success within your product organization. This article serves as a comprehensive guide for how CPOs can use GenAI in product strategy, design, and innovation – generating new product ideas, creating unique designs, and exploring different variations and options.
Machine learning watching generative artificial intelligence (AI) take off feels a little bit like an American Girl doll envying the Barbie movie excitement from afar. What is she, chopped liver? But we can’t forget about machine learning, because it’s the giant that generative AI is standing on. How? Well, machine learning is how generative AI learns. Generative AI takes machine learning a step further by leveraging those learnings to produce something new.
