According to @DeepLearningAI, the AI Developer Conference has published its full agenda and speaker lineup featuring experts from Google, AWS, Vercel, Mistral, Neo4j, Arm, and SAP; source: DeepLearning.AI on X, Oct 23, 2025; agenda: hubs.la/Q03PWRbj0. According to @DeepLearningAI, key sessions include Andrew Ng on the current state of AI development, Miriam Vogel on responsible AI and governance, Kay Zhu on scaling Super Agents, Malte Ubl and Fabian Hedin on AI-driven software systems, and João Moura with Hatice Ozen on advancing agentic architectures; source: DeepLearning.AI on X, Oct 23, 2025. According to @DeepLearningAI, the demo area will showcase the latest AI tools and applications from Databricks, Snowflake, LandingAI, Prolific, and Redis; source: DeepLearning.AI on X, Oct 23, 2025. According to @DeepLearningAI, these agenda items and demos are explicitly highlighted in the official program, creating clear event-driven watch points for traders tracking AI infrastructure and agentic AI tooling; source: DeepLearning.AI on X, Oct 23, 2025; agenda: hubs.la/Q03PWRbj0.

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 @AndrewYNg, DeepLearning.AI launched a short course, Building and Evaluating Data Agents, created with Snowflake and taught by @datta_cs and @_jreini, focused on embedding comprehensive evaluation into LLM data agents. Source: Andrew Ng on X, Sep 24, 2025. The course teaches the Goal-Plan-Action framework with runtime evaluations to catch failures mid-execution, OpenTelemetry-based tracing and evaluation to pinpoint where agents fail and systematically improve performance, and LangGraph-based orchestration across web search, SQL, and document retrieval for step-by-step visibility—capabilities directly applicable when building data agents used in analytics and trading pipelines. Source: Andrew Ng on X, Sep 24, 2025.

According to @DeepLearningAI, the organization announced a free short course titled Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta and Josh Reini. Source: DeepLearning.AI on X, Sep 24, 2025, https://bit.ly/424ndqQ According to @DeepLearningAI, the course teaches learners to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes final results. Source: DeepLearning.AI on X, Sep 24, 2025, https://bit.ly/424ndqQ According to @DeepLearningAI, the announcement notes that most data agents struggle with reliability or cannot handle multi-step reasoning, and the curriculum focuses on evaluation and tracing for such agent workflows. Source: DeepLearning.AI on X, Sep 24, 2025, https://bit.ly/424ndqQ According to @DeepLearningAI, the course defines a data agent as a system that extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings, with free enrollment available via the provided link. Source: DeepLearning.AI on X, Sep 24, 2025, https://bit.ly/424ndqQ

According to @DeepLearningAI, Andrew Ng and DeepLearning.AI will host the second AI Developer Conference in New York City on November 14, 2025, featuring technical teams from Anthropic, AWS, Snowflake, Arm, Neo4j, and Coderabbit; source: DeepLearning.AI on X, 2025-09-19 https://twitter.com/DeepLearningAI/status/1969144469032231222. The organizer provides a live list of partners and speakers at hubs.la/Q03K75qR0 and notes that more will be announced; source: DeepLearning.AI on X and hubs.la/Q03K75qR0. The announcement highlights limited regular-priced tickets and an impending move to the next pricing tier, indicating a time-sensitive registration window; source: DeepLearning.AI on X, 2025-09-19 https://twitter.com/DeepLearningAI/status/1969144469032231222. The post does not reference cryptocurrencies, blockchain, or tokens, so no direct crypto market impact is indicated by the source; source: DeepLearning.AI on X, 2025-09-19 https://twitter.com/DeepLearningAI/status/1969144469032231222.

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 @stocktalkweekly, Bank of America upgraded Snowflake (SNOW) to Buy from Neutral and raised its price objective to 240 dollars, applying a 53x CY26 free cash flow valuation with a 1.9x growth adjustment versus its prior 220-dollar target based on 48x and 1.7x, while noting three proprietary data sources as part of its rationale (source: @stocktalkweekly).

According to @StockMKTNewz, Jim Cramer expressed a positive outlook on Snowflake (SNOW), stating that he likes the stock and suggests investors can buy more. This direct endorsement from a high-profile market commentator may influence short-term trading sentiment around SNOW, with increased buying interest potentially impacting both stock and related AI or data analytics crypto assets through market correlation. Source: @StockMKTNewz.