According to DeepLearning.AI, a new blog shares a practical playbook to rapidly build GenAI prototypes using Streamlit and Snowflake based on lessons from a course taught by Chanin Nantasenamat. According to DeepLearning.AI, the announcement focuses on developer speed and a Streamlit-Snowflake workflow without disclosing product changes, pricing, or user metrics. According to DeepLearning.AI, the post is an educational resource and does not reference cryptocurrencies or blockchain, so there are no token-specific disclosures in this update. According to DeepLearning.AI, the announcement was posted on September 25, 2025 with a link directing readers to the full blog.

According to @DeepLearningAI, the organization partnered with Snowflake to launch the course Fast Prototyping of GenAI Apps with Streamlit, taught by Chanin Nantasenamat, showing how a few lines of Python can become working GenAI app prototypes that run inside Streamlit and Snowflake; source: DeepLearning.AI on X, Sep 16, 2025. According to @DeepLearningAI, the announcement highlights rapid feedback and iteration toward production within Snowflake and Streamlit, providing a concrete event traders can log when tracking Snowflake’s GenAI developer enablement for SNOW monitoring; source: DeepLearning.AI on X, Sep 16, 2025.

According to @DeepLearningAI, GenAI with Streamlit enables developers to start from intent and generate working app code in seconds, replacing hours of setup previously required for data or AI prototypes; source: https://twitter.com/DeepLearningAI/status/1963954016100004104. According to @DeepLearningAI, the post promotes the course Fast Prototyping of GenAI Apps with Streamlit aimed at helping learners prototype faster, test more ideas, and move from concept to app; source: https://www.deeplearning.ai/courses/fast-prototyping-of-genai-apps-with-streamlit and https://twitter.com/DeepLearningAI/status/1963954016100004104. According to @DeepLearningAI, the announcement explicitly tags Snowflake and thedataprof, highlighting relevance to data-centric development audiences; source: https://twitter.com/DeepLearningAI/status/1963954016100004104. According to @DeepLearningAI, no specific cryptocurrencies, tickers, or market metrics are cited in the announcement, so any trading impact would stem from faster in-house tool development rather than direct asset news; source: https://twitter.com/DeepLearningAI/status/1963954016100004104 and https://www.deeplearning.ai/courses/fast-prototyping-of-genai-apps-with-streamlit.

According to @DeepLearningAI, a new course titled Fast Prototyping of GenAI Apps with Streamlit, built in partnership with Snowflake, is now live to enable rapid prototyping of generative AI applications on Streamlit; source: @DeepLearningAI on X, Aug 12, 2025. The announcement stresses that months-long planning cycles no longer fit today’s fast-moving software landscape as new capabilities emerge weekly, underscoring a need for faster iteration; source: @DeepLearningAI on X, Aug 12, 2025. The post does not mention cryptocurrency, blockchain integrations, or token use, and no direct crypto market impact was disclosed; source: @DeepLearningAI on X, Aug 12, 2025.