What I Do During A Data Science Project To Deliver Success

from blog Eugene Yan, | ↗ original
It's not enough to have a good strategy and plan. Execution is just as important.
This is a short summary. ↗ Open original to view full content

Related

Lessons learned as a software developer turned project manager
Karim Jedda | original ↗
Data Science Project Quick-Start
Eugene Yan | original ↗
Growing and Running Your Data Science Team
Eugene Yan | original ↗
Why You Need to Follow Up After Your Data Science Project
Eugene Yan | original ↗
What I Do Before a Data Science Project to Ensure Success
Eugene Yan | original ↗
What I Love about Scrum for Data Science
Eugene Yan | original ↗
How to Give a Kick-Ass Data Science Talk
Eugene Yan | original ↗
Data Science and Agile (Frameworks for Effectiveness)
Eugene Yan | original ↗
Data Science and Agile (What Works, and What Doesn't)
Eugene Yan | original ↗
One way to help a data science team innovate successfully
Eugene Yan | original ↗

More from Eugene Yan

39 lessons from Industry ML Conferences in 2024
3 Nov 2024 | original ↗

ML systems, production & scaling, execution & collaboration, building for users, conference etiquette.

AlignEval: Building an App to Make Evals Easy, Fun, and Automated
27 Oct 2024 | original ↗

Look at and label your data, build and evaluate your LLM-evaluator, and optimize it against your labels.

Hackathon Judge - Weights & Biases LLM-Evaluator Hackathon
22 Sept 2024 | original ↗

Being a human judge at the Weights & Biases LLM-as-a-Judge Hackathon

Building the Same App Using Various Web Frameworks
8 Sept 2024 | original ↗

FastAPI, FastHTML, Next.js, SvelteKit, and thoughts on how coding assistants influence builders' choices.

Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge)
18 Aug 2024 | original ↗

Use cases, techniques, alignment, finetuning, and critiques against LLM-evaluators.