We've built something ✨awesome✨to get you started with building ML products 🤩 🥁 Introducing: 🤗Tasks 🥁 This is our recent open-source initiative, each page contains use cases, tutorials, models and more to get you started with ML! hf.co/tasks

Thinking about creating an open source library to hide all the heavy boilerplate code that AWS SageMaker requires to write... #mlops #aws #sagemaker #ml #datascience

Please Amazon Web Services, build a better UI for experiments tracking in SageMaker #mlops #aws #sagemaker

What's the cheapest way of doing big data in the cloud if you just need it a couple of times every week? #bigdata #data #datascience #spark #serverless

Standardize ML systems deployments is so difficult because it doesn't make sense. Each use case is a different. You're basically automating a scientific process. You can standardize scientific methods but not implementations. #mlops #datascience #MachineLearning

Estos son mi 🔝20 startups candidatas a ser #Unicornio en los próximos años, con la colaboración de 30 VC’s & BA’s👇 Factorial #Paack @Typeform @Wallapop RITMO #Clarity @GetCapchase @Seedtag @RedPoints @Bit2me @Housfy_es @TradeinnSL Trucksters … Foto: Expansión Start Up

A lot of machine learning research has detached itself from solving real problems, and created their own "benchmark-islands". How does this happen? And why are researchers not escaping this pattern? A thread 🧵

Abrimos varias posiciones para el equipo de DataCrew de idealista: Data Scientist (lnkd.in/eiQHVe8t) y Data Engineer (lnkd.in/eicN6g-A)

Ok be honest, who the hell has time to do fairness & ethics evaluations on their ML models? I’m always rallying to even get precision metrics! #DataScience #MachineLearning

Wow there are very mediocre ml medium posts out there

What's the best cost-effective approach to tackle a business use case in a domain you've never studied in depth? #DataScience #MachineLearning #DeepLearning

It's so hard to stick to a small proof-of-concept when you know that 1 month more of work will make a huge improvement... #DataScience #MachineLearning

All the hype about machine learning for big applications like self-driving cars, playing Go, passing programming tests. I'm more exited by the "small" ML successes that solve concrete problems. Hidden in organizations, no PR. Just a data scientist (team) doing the real work.

Christoph Molnar 🦋 christophmolnar.bsky.social Solving narrow problems in constrained parameter spaces is where machine learning really shines!

Error Analysis, 101 The best way to improve your models is to see what they're doing wrong. This is called Error Analysis. Done correctly, this gives you a clear path towards improving your models. Ignore it, and you'll just bump around in the dark. Here are 5 principles🧵

¡Gracias a Prodigioso Volcán y Carmen Torrijos por la invitación! Lo he pasado muy bien hablando de #NLP y #AI y conociendo más de como se usa en Salud y Educación

Wondering about a practical applications of ML? 💥 María Hernández & María Hernández walks us through how @BBVA_AIFactory is helping Financial Advisors get Answers that Make Sense and Looks Coherent. #jotb22 #datascience

It was a great experience presenting the work we do at @BBVA_AIFactory yesterday at @jotb22 !! Fantastic organisers, amazing speakers and the best speaker mate María Hernández 😉! Thanks also to Manoella Nolasco and Alexandra M Sims💡 for taking care of us all along the way!

I started establishing “Anti-Goals” for all new projects. Everything changed. Here’s how it works: