#productionml — Public Fediverse posts

Как мы адаптировали LLM для русского языка

Как мы потратили 2 месяца на адаптацию Qwen3-0.6B для русского языка. Написали систему с нуля на основе 8 научных статей из arXiv. Исправили 6 критических багов (от NaN в fp16 до архитектурных проблем). Получили +35% training speed и +60% inference speed . В этой статье - честный рассказ о том, что не работает из коробки, какие грабли ждут в production, и как мы их обошли. Мы - это я и мой друг =)

https://habr.com/ru/articles/964510/

#nlp #llm #machinelearning #RussianNLP #tokenization #pytorch #deeplearning #ProductionML #mawo

🔖 The Top 5 Papers About MLOps You Should Know

5️⃣ Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development By Aspen Hopkins, Serena Booth

#mlops #productionml #devops #data #datascience #readinglist #mlopsengineer

🔖 The Top 5 Papers About MLOps You Should Know (Part 2)

3️⃣ Machine Learning: The High-Interest Credit Card of Technical Debt by D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov,
Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young

4️⃣ The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction By Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, D. Sculley

#mlops #productionml #devops #data #datascience #readinglist #mlopsengineer

🔖 The Top 5 Papers About MLOps You Should Know (Part 1)

1️⃣ Operationalizing Machine Learning: An Interview Study By
Shreya Shankar, Rolando Garcia, Joseph M. Hellerstein, Aditya G. Parameswaran

2️⃣ Socio-Technical Anti-Patterns in Building ML-Enabled Software by Alina Mailach, Nortbert Siegmund

#mlops #productionml #devops #data #datascience #readinglist #mlopsengineer

📝 What kind of MLOps team are you? [Part 3/3]
#mlops #productionml #dataops #mlsystems

In early starts-ups & even at the Small/Med Size business, teams are often a combination of the different modes & that's totally fine!

You don't always need a specialized team!

💡What's important to recognize is to know this framework exists for organziational alignment, as well as to know when teams can be spun out.

📝 What kind of MLOps team are you? [Part2/3]
#mlops #productionml #dataops #mlsystems

🔍 Zeroing in on the ones that oftentimes constitute the ML Org or the Data org:

⛑ Enabling teams - Help the DS & Product folks get those models out the door using the internal plateforms & capabilities provided by the CST

⚙️ Complicated Subsystem team - Focused on maintaining & expanding the extremely technical solution they own

👷🏻‍♀️The Platform Team - Owns unified & integrated experience.

📝 What kind of MLOps team are you? [Part1/3]

🗺️ In the world of "team Topologies" there are 4 types of teams.

🌊 Stream-aligned teams (ST) ---------> Data science & Product (for example)

⛑ Enabling teams (ET) ---------> ML Engineering

⚙️ Complicated Subsystem team (CST) ---------> The Kubernetes Team, the GCP team, the Terraform team, the Redis team, etc

👷🏻‍♀️The Platform Team (PT) ---------> The ML Platform Team, The Data Platform Team, etc

#mlops #productionml #dataops #mlsystems

🧠 Everyone else: <LLM Experts, producing multi-modal Gen AI systems. >

🤓 Me: <Still troubleshooting that lambda function to calculate Euclidean distance of lat/long columns in Polars Dataframe for a sample project in Colab. > 😅

-----
#datascience #mlops #productionml #ai #mlengineer

The tools we have today are better than the ones we had before and this is especially true in the #mlops world. We have more options than ever before (cc: MAD Turck Landscape) but confusion is just as high as it ever was.

#mlops #productionml #mlengineering #oss #devtools #python

👉🏻 Online Inference =/= Streaming

We're all aware of this right? That they're not the same thing?

#mlops #mlengineering #datascience #dataengineering #productionml #mlsystems #systemdesign

👉🏻 It is a truth universally acknowledged, that a data scientist in possession of a trained model, must be in want of a reliable means of productionization and deployment.

👣 And the journey of a thousand pipelines starts with...
knowing how to appropriately package your models from the get-go. 📦

This blog post is for you: https://medium.com/kitchen-sink-data-science/software-fundamentals-for-machine-learning-series-understanding-the-why-of-vms-containers-89621cf66d23?source=friends_link&sk=4b4a9f37c1f609db2addbeb6fa219fb8&utm_content=buffer8c348&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer

#mlops #mleng #productionml #datascience #productdatascience

👉🏻 It is a truth universally acknowledged, that a data scientist in possession of a trained model, must be in want of a reliable means of productionization and deployment.

👣 And the journey of a thousand pipelines starts with...
knowing how to appropriately package your models from the get-go. 📦

This blog post is for you: https://medium.com/kitchen-sink-data-science/software-fundamentals-for-machine-learning-series-understanding-the-why-of-vms-containers-89621cf66d23?source=friends_link&sk=4b4a9f37c1f609db2addbeb6fa219fb8&utm_content=buffer8c348&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer

#mlops #mleng #productionml #datascience #productdatascience

👉🏻 It is a truth universally acknowledged, that a data scientist in possession of a trained model, must be in want of a reliable means of productionization and deployment.

👣 And the journey of a thousand pipelines starts with...
knowing how to appropriately package your models from the get-go. 📦

This blog post is for you: https://medium.com/kitchen-sink-data-science/software-fundamentals-for-machine-learning-series-understanding-the-why-of-vms-containers-89621cf66d23?source=friends_link&sk=4b4a9f37c1f609db2addbeb6fa219fb8&utm_content=buffer8c348&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer

#mlops #mleng #productionml #datascience #productdatascience

👉🏻 It is a truth universally acknowledged, that a data scientist in possession of a trained model, must be in want of a reliable means of productionization and deployment.

👣 And the journey of a thousand pipelines starts with...
knowing how to appropriately package your models from the get-go. 📦

This blog post is for you: https://medium.com/kitchen-sink-data-science/software-fundamentals-for-machine-learning-series-understanding-the-why-of-vms-containers-89621cf66d23?source=friends_link&sk=4b4a9f37c1f609db2addbeb6fa219fb8&utm_content=buffer8c348&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer

#mlops #mleng #productionml #datascience #productdatascience

👉🏻 It is a truth universally acknowledged, that a data scientist in possession of a trained model, must be in want of a reliable means of productionization and deployment.

👣 And the journey of a thousand pipelines starts with...
knowing how to appropriately package your models from the get-go. 📦

This blog post is for you: https://medium.com/kitchen-sink-data-science/software-fundamentals-for-machine-learning-series-understanding-the-why-of-vms-containers-89621cf66d23?source=friends_link&sk=4b4a9f37c1f609db2addbeb6fa219fb8&utm_content=buffer8c348&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer

#mlops #mleng #productionml #datascience #productdatascience

RT @BazeleyMikiko: 🤔 Do you think one of the reasons why your #datascientists aren't adopting your internal #mlops or #productionml tools is b/c the interface is hard-to-use

🤔 Do you think one of the reasons why your #datascientists aren't adopting your internal #mlops or #productionml tools is b/c the interface is hard-to-use

😳 My talk proposal to the #mlops track was accepted to #DataCouncilAustin 2023 🤯

🎉 What an exciting way to start the year! 😃

Looking forward to connecting with folks in Austin from March 28-30th on #mlops #productionml #mlengineering #productiondatascience #DataCouncilAustin2023 #datacouncil

Please feel free to connect with me on LI if you're attending or presenting!
https://www.linkedin.com/in/mikikobazeley/

How can organizations get running with #ML?

Organizations should :

✅ understand their #MLOps maturity and be honest with where they're going;

✅ not try to build what other companies are building, and should instead focus on getting their fundamentals down.

✅ be problem-oriented and focus on solving the bottlenecks in their #productionml and #deployment stack.

✅ bridge the knowledge gap between the data science and engineering teams.

https://youtu.be/l1MZRSJ-x4s

A few weeks ago a couple of us met to talk about the challenges of production ML. Twitter space included a group of data scientist, engineers, MLOps engineers, and DevRels.

I summarized the discussion & clipped out relevant parts of the audio here:

https://mikiko.hashnode.dev/a-discussion-top-challenges-of-moving-data-science-to-production

#mlops #productionml #datascience #dataengineering

My intro post:

👩🏻‍💻 #MLOps @ Featureform 🤖
Focused on: DevRel ==
Community + Content + Product + Ecosystem

Also talk about:
#dataengineering #productionml #platformengineering

What I do:
⭐️ Develop ML platforms & systems
⭐️ Contribute to the open-source ecosystem
⭐️ Content

📬 Substack: https://mikikobazeley.substack.com/

📖 Blog: https://mikiko.hashnode.dev/

📹 Youtube: https://bit.ly/3MBR8N3

🐙🐈 Github: https://github.com/MMBazel

👾 Twitch: https://bit.ly/3Akmwfe

💼 LinkedIn: https://www.linkedin.com/in/mikikobazeley/

So don't let the shift in topic to Data-Centric AI fool you into thinking modeling, algorithms, feature engineering, etc aren't important.

Instead see the focus of convo on data as an acknowledgement of an impactful area that has been underappreciated in its impact on ML.

#mlops #dataengineering #productionml #mlsystems

If you talk to most serious athletes or bodybuilders, they'll tell you how important diet is in achieving their goals. (Hint: The phrase "Abs are made in the kitchen")

But they'll also wax lyrical about
✔️ their splits (upper vs lower, arms/shoulders/core vs back/chest vs legs),
✔️ how much they hate cardio (which I find inexplicable as secretly they love it, they just say they hate it because everyone else says it),
✔️ their cheat meals.

#mlops #dataengineering #productionml #mlsystems

❓❓ What is the difference between Model-Centric AI vs Data-Centric AI ❓❓

By analogy:

👉🏻 #ModelCentricAI ➡️ The workout matters 🏋🏻‍♀️
👉🏻 #DataCentricAI: ➡️ The diet matters 🥗

So the difference between Model-Centric AI and Data-Centric AI is like optimizing on the workout (types of lifts, cardio, reps & intensity, etc) versus optimizing the diet (caloric intake, macros, timing, etc).

#mlops #dataengineering #productionml #mlsystems

Who Am I:
👉🏻 Head of #mlops & solutions at #featureform , a #virtualfeaturestore

What I Do:
⭐️ Develop #MLplatforms & #MLOpssystems that work, no matter the business or technology constraints;
⭐️ Contribute to the #opensource MLOps ecosystem & continue to drive innovation (as well as #GCP , #AWS , #Azure);
⭐️ Create high-quality & thoughtful content that helps everyone in #productionML become more productive, collaborative, & happier.

I also talk about:
#dataengineer #datascience #career