Any good resources on architecture design for managing multiple machine learning models in production? #machinelearning
20.03.2025 11:17
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Any good resources on architecture design for managing multiple machine learning models in production? #machinelearning
ML Engineering:
Learn programming -> machine learning concepts -> system design -> deployment strategies -> cloud computing -> focus on practical projects.
Many people will waste weeks or even months trying to build a model only later to realize that simple python script achieve, better accuracy.
One of the most important skills for a machine learning engineer or data scientist to have is being able to know when NOT use AI.
Before you even think about trying to use AI to solve a problem, you need to first try to solve the problem without AI.
#datasceince
AI is creating a generation of illiterate programmers. That's for sure.
Pretty much 🤷♂️
This 👇
DeepSeek AI is experiencing service disruptions due to a large-scale malicious attack.
Chinese LLMs are much less censored than American LLMs.
Will that change?
So recently, I started using speech to text on my phone, and I found that it has been very convenient in writing down notes and all kinds of things. I’m gonna try to use this to share my daily insights because it makes writing post a lot more easier and it feels very natural.
#datasceince
In 2025 learn machine learning.
Merry Christmas and Happy Holidays everyone! 🎅🎄
I hope Santa's latest AI model predicted that you will get presents this year!
#machineleanring
Want to learn more machine learning?
Be sure to follow me 👉 @dankornas.bsky.social
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dankornas.substack.com
Embracing all four of these pillars creates a well-rounded data scientist who can drive impact from raw data to real-world solutions.
🔸 Communication & Visualization
Storytelling skills, visual design, and an ability to engage effectively with stakeholders are what set great data professionals apart—because insights only matter when people understand them.
🔸 Computer Science
Programming, databases, and big data technologies are essential for efficiently working with large-scale datasets and deploying solutions.
🔸 Math & Statistics Skills
A strong foundation in linear algebra, calculus, and both descriptive & inferential statistics forms the backbone of any solid data analysis.
🔸 Domain Knowledge
Understanding the business context and user needs is crucial. It allows data scientists to focus on the right problems, whether it’s in healthcare, finance, marketing, or beyond.
Data Science is more than just crunching numbers—it’s built on four important pillars that help us turn data into actionable insights.
Let's take a closer look 👇
What are some #datascience analytics tools/libraries you cannot live without?
When your model scores 99% accuracy
- In Kaggle => you are a master.
- In the real world => you have a bug (data leakage)
#machinelearning
It’s quite exhilarating when a company gives you the freedom to solve a problem. It fosters creativity, ownership, and a deeper connection to the work.
It’s quite exhilarating when a company gives you the freedom to solve a problem. It fosters creativity, ownership, and a deeper connection to the work.
First day of onboarding on the new job - so many things not working 😂😅
Let the debugging journey begin.
Thank you!
#OpenSource framework for voice and multimodal conversational #AI
🔗 github.com/pipecat-ai/p...
Super excited to be starting a new role as a Full-Stack Machine Learning Engineer.
This is not your traditional ML role.
I will be working on the entire ML pipeline:
- data analysis, collection & preprocessing
- model training, evaluation, deployment & maintenance
#datascience