I’m exploring how organizations can practically adopt OpenAI models for production use cases such as analytics, automation, customer support, and decision-making.

With rapid changes in model capabilities, costs, governance, and integration patterns, what are the recommended best practices for:

• Choosing the right OpenAI model for different use cases

• Ensuring data privacy and responsible AI usage

• Integrating OpenAI with existing data and BI systems

• Scaling from experimentation to production

Looking for perspectives from teams that have already implemented OpenAI in real-world workflows, along with lessons learned and pitfalls to avoid.

While technical skills are essential, many data professionals stand out because of less obvious abilities like problem-solving, communication, domain knowledge, or strategic thinking.

Reflecting on underrated skills helps members identify the traits that differentiate exceptional practitioners from average ones.

These discussions can guide personal growth, inspire others to cultivate overlooked abilities, and highlight the human factors that make data & AI work impactful.

I came across a fun project using OpenAI Codex CLI where you simply provide a screenshot of a portfolio website, and Codex helps generate the HTML/CSS files to recreate it. It even lets you approve each step, making it beginner-friendly and customizable. Has anyone tried using Codex (or any other AI tools) to build websites from images? Would love to hear your experience!

Exploring how GPT-4 and other OpenAI APIs could help automate mundane tasks like report generation, cleaning, summarization, etc. Any cool examples of how you’ve used them in your data workflow?