On a VM? Seems too manual. 
  
 First yes API end point is mandatory.. Something should have it. 
  
 1. Kubernetes deployment with CI-CD enabled 
 2. Use a model host and push model there. Your kubernetes service should always call the latest model from the model host.

What kind of a model host would you recommend? 
 Huggingface?

Product Manager

Data engineer

Mostly, I have seen people in my team creating an API endpoint for it and deploy on a VM via uvicorn. Anything better?

How do you all deploy ML models for production?

How do you all deploy ML models for production?- Grapevine

Welcome to Product Managers's Private Community

Hey everyone, happy to answer any questions around Data Science/ML/AI and making a career within it

A brief about me, I work currently as a Sr. ML Engineer at an Indian unicorn startup 
I graduated 5+ years ago and have since worked as a Data Scientist / ML Engineer at an ecommerce company and social media company. My designations have changed based on the industry requirements and work too changed based on that.

Would love to take questions on what it means to be an ML Engineer, my early learnings from a career in data science, and thoughts on AI.

Look forward to all questions :)

AMA 🎙️: Hi, I am diffusedbandit, Senior ML Engineer at an Indian Unicorn

Data scientist

Welcome to Business Roles's Private Community

AMA. How does a day to day pan out for a data scientist, Who is data science not really for, Does FOMO really affect my career in DS

Myths busted by a Data Scientist

Welcome to Software Engineers's Private Community

Personally for me? Shifting from pandas to polars for data related tasks changed my life. Code remained similar in syntax but the systems became more performant at extracting efficiency out of the same machine. 

Using connectorx for interfacing with the postgres servers was also a gamechanging moment instead of the OG pd.read_sql_query.

Across both of these minor changes, on moderate to large datasets, we achieved a near 10x improvement in querying data + doing operations on them.

As a Software Engineer, which library or framework was a gamechanger for you?

Robotics engineer

Welcome to Data Scientists's Private Community

Hi
I am working as a senior data scientist. I know ML and Deep Learning very well, but I lack the experience of productionizing models. I need help with a proper mentored course to understand MLops, LLMops, docker etc.
Any help is appreciated.

MLOPS course details

I'm a backend developer with 5 years of experience.
I've been tasked to create a simple website for a friend's startup.
I'm very strong with the development and hld of things but I've never done independent deployment of an app or website end to end.
What are my options here ?
I'm aware of options like AWS, digital ocean etc.
Should I even consider things like WordPress or the genz things like no code ? Do they give me enough control to do fun things or I'm always at their mercy?
Since it's a pretty early stage thing.. infra cost is very much an issue.
Thanks in advance

Cost effective solution for deploying a website

Software Developer

I am an ML enthusiast and have been learning Reinforcement Learning and other Algorithms to shift my profile to ML engineer. But as soon as I started applying for jobs, every job is looking for people who have experience in GENAI, LLMs, etc. So I want to know, how can I become one?

How to become a GenAI Developer?

Current CTC is 25lpa. 6yrs of experience started as data analyst then as business Analyst. Have been working as machine learning engg for past 2 years in series c funded product based company. My work is mostly related to model scaling, maintainability and reproducibility.

Do you think, is lower, on par or more than market rate.

(Also if anyone is hiring, I can share more details)

CTC check

I'm a third year student and I'm interested in backend dev and ML stuff. But the sort of math that ML demands is too much for me. If anyone has some tips and tricks to understand ML, please share. Also, one of the seniors who joined the company in which I was working as an intern said that the backend I wrote using python from scratch might be an issue in scalability in the near future and said using Ruby. I had made it using fastapi and now I'm confused. Should I consider a change of interest?

Help regarding managing interests

Talking to my nephew nowadays seems like the AI bug has gotten to him. Even going back during the early parts of my own career and even undergrad, ML and stats was still a fledgling field. Most of the smart folks went into SDE roles at top tech.

I still remember that even back then, hiring for roles like these were extremely theoretical. You would test even experienced candidates with undergrad topics which was slightly outdated tbh because most of the functions were abstracted away and it is obvious that you would not remember the matrix approach to finding weights for a simple linear regression model.

Has this changed now? Because I don't think interviews like that actually evaluate the accumulated wisdom that experienced professionals bring to the table.

I mean it is generally a waste of time for anyone to brush up manual derivations or remember SQL niche intricacies. Usually it is better to test for the 80% knowledge that is actually useful on a day to day basis. I mean if the main things that companies test in experienced candidates is something that can be rote-memorized from a book then they are not testing the right things no?

What do you all think? Just wondering about this for some time now especially considering the hype around AI?

How do you all deploy ML models for production?

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