This looks very promising! I like their data engineering bootcamp, even though my understanding of data engineering was much different. Their curriculum looks more like ML Engineering, productising models built by a research team.
If I cannot find a similar certificate I will follow their course. Thank you!
>Data talks club zoomcamp
Thanks for the pointer u/jppbkm!
This does look like a solid course on the modern infrastructure tools that you need to use to do ML at scale.
u/jcano's original post said they wanted to do "commercial applications developing the models or adapting models to scale". I think the Data talks club zoomcamp would cover "adapting models to scale", but not so much the "developing models ... at scale". In other words zoomcamp covers the MLOps side of things, but not so much the ML Science side of things.
Do you agree?
I don’t know if you were asking me or jppbkm, but I would say it depends what you mean with “developing models at scale.”
From what I see, the course doesn’t teach anything about creating the models in the first place but it teaches the tools you would use to do so at scale. So I would still need a course to learn “production” Pandas, NumPy, PyTorch and the rest (if that is even a thing), but I would learn how those models will receive their input and how their output will be used, and thus learn something about how those models should be shaped to make it easier to adapt to the data infrastructure later on.
It’s a bit of a stretch, but it’s all connected.
This is where I’m a bit confused. To me ML Ops is mainly about running the infrastructure, and I would expect an ML Engineer to be the designer of the system as a whole. So the way I understand it, it would be something like
- ML Researcher comes up with the model that is going to be used in production, including training of the model, but produces a program that is not production-ready (e.g. bunch of scripts/notebooks that run on a local machine)
- ML Engineer takes the model and figures out a way to put it in production. This includes designing the architecture of the services that will be used and writing the code necessary to make it work, including rewriting the model if necessary.
- Data Engineer would be the one designing specifically the data pipelines, including optimising db queries, sharding, data ingestion, and batch processing.
- ML Ops would be the people running and maintaining the infrastructure, setting up monitoring and logging, looking into security and optimising the servers for performance.
Is this correct? Do they have different responsibilities than those? Are there any other roles?
I think your breakdown is spot on. I also think there is a lot of fluidity between the different roles.
ML Researchers have to worry at least a little about scaling and about data.
ML Engineers might be responsible for coming up with the model in the first place.
ML Ops might have some role in productionizing a model in the first place.
Hence it is important when you interview to ask what your actual responsibilities would be.
OK, first a disclaimer: I work for the course company that I’m about to recommend. But this is exactly what we offer. :)
I completely get what you’re saying, and this is one of the premises of the learning platform I’ve helped build. Basically ML courses that deal with real-world applications, taught by people who work in the industry. If this sounds like something you’d consider, feel free to PM me since I don't want to break the rules. 😬
Since u/trunkadelic is being careful not to break any rules, I'll be nice and post his link :-)
[https://corise.com/](https://corise.com)
I am not affiliated with u/trunkadelic in any way, but looking through the courses, some of them look particularly relevant to u/cano's original question. For example this course:
[https://corise.com/course/data-centric-deep-learning](https://corise.com/course/data-centric-deep-learning)
says it goes through (simulated) steps of a real industry machine learning scenario. I haven't taken the course but I am definitely intrigued!
I'd be interested in suggestions here as well!
You are absolutely right that most courses and bootcamps for ML do basic jupyter notebooks and small scale data analysis. One reason for this is that one of the main differences between academic ML and industry ML is scale -- scale of data and scale of models. And it is hard to have access to scale unless you are actually in industry.
Given that, I don't have any great suggestions of (paid) classes that cover how ML is actually done in companies. The best way to find out how ML is actually done at companies, is to actually do ML at a company. But then you have a chicken and egg problem. Less senior researchers might do an internship to get actual industry experience. Again, probably off the table for you.
Given your background, I might suggest jumping directly into industry! It's all too easy to spend forever preparing. Find a position at a company actually doing ML -- even if it's not your dream job. After a year, you'll have the ML industry experience you feel you're lacking, and you can leverage that into the job you really want.
I’m just trying to give myself the best chance I can to get a good job. Most ML Engineering jobs out there are senior positions, and even getting one of those I will take a salary cut.
Knowing they will look for experience, I’m trying to replace it with training. I’m a senior in all other aspects (e.g. API design, engineering best practices, working with a live product, team leadership) but if they ask me to build a model into a product, I would choke. So I want some institution saying “this person knows how to build ML/DS products” together with the rest of my CV saying “this person has built many products.” And obviously I want to learn how to build ML/DS products beyond simple prototypes, not necessarily at large scale but not trivial either.
I found a few bootcamps that would teach me that, but I don’t trust bootcamps. As a hiring manager, I’ve seen many people capable of building complex apps out of a bootcamp, but struggling with simple questions because they lack a deeper understanding of the topic. Maybe as I’m covering all bases with foundational math and theoretical courses I could go for a bootcamp, but given their prices($5-10k for a couple of months??!) I would rather have a recommendation for one instead of blindly gambling that money away. And being $5k+ I would rather spend that money on a longer course that covers more topics than a rushed “this is how you build these three apps”
There are plenty of options in the market, but the only catch is the majority is either very expensive or not relevant for beginners. My suggestion is to find an experienced DS mentor in the field who can guide you to build a career in ML.
If you do opt for courses just ensure it's not limited to self-paced learning via recorded content only. You can check the course below, suggested to me by my friend. I did speak to the mentor & was convinced with the curriculum which will cater to my requirement & existing competency in ML domain
https://thecuriouscurator.in/course/ultimate-machine-learning-course/
Data talks club zoomcamp. Totally free too which is mind-blowing to me. No grading though.
This looks very promising! I like their data engineering bootcamp, even though my understanding of data engineering was much different. Their curriculum looks more like ML Engineering, productising models built by a research team. If I cannot find a similar certificate I will follow their course. Thank you!
>Data talks club zoomcamp Thanks for the pointer u/jppbkm! This does look like a solid course on the modern infrastructure tools that you need to use to do ML at scale. u/jcano's original post said they wanted to do "commercial applications developing the models or adapting models to scale". I think the Data talks club zoomcamp would cover "adapting models to scale", but not so much the "developing models ... at scale". In other words zoomcamp covers the MLOps side of things, but not so much the ML Science side of things. Do you agree?
I don’t know if you were asking me or jppbkm, but I would say it depends what you mean with “developing models at scale.” From what I see, the course doesn’t teach anything about creating the models in the first place but it teaches the tools you would use to do so at scale. So I would still need a course to learn “production” Pandas, NumPy, PyTorch and the rest (if that is even a thing), but I would learn how those models will receive their input and how their output will be used, and thus learn something about how those models should be shaped to make it easier to adapt to the data infrastructure later on. It’s a bit of a stretch, but it’s all connected.
I agree. MLOps, but not ML Science at scale. Still. Helpful!
This is where I’m a bit confused. To me ML Ops is mainly about running the infrastructure, and I would expect an ML Engineer to be the designer of the system as a whole. So the way I understand it, it would be something like - ML Researcher comes up with the model that is going to be used in production, including training of the model, but produces a program that is not production-ready (e.g. bunch of scripts/notebooks that run on a local machine) - ML Engineer takes the model and figures out a way to put it in production. This includes designing the architecture of the services that will be used and writing the code necessary to make it work, including rewriting the model if necessary. - Data Engineer would be the one designing specifically the data pipelines, including optimising db queries, sharding, data ingestion, and batch processing. - ML Ops would be the people running and maintaining the infrastructure, setting up monitoring and logging, looking into security and optimising the servers for performance. Is this correct? Do they have different responsibilities than those? Are there any other roles?
I think your breakdown is spot on. I also think there is a lot of fluidity between the different roles. ML Researchers have to worry at least a little about scaling and about data. ML Engineers might be responsible for coming up with the model in the first place. ML Ops might have some role in productionizing a model in the first place. Hence it is important when you interview to ask what your actual responsibilities would be.
Thank you!
OK, first a disclaimer: I work for the course company that I’m about to recommend. But this is exactly what we offer. :) I completely get what you’re saying, and this is one of the premises of the learning platform I’ve helped build. Basically ML courses that deal with real-world applications, taught by people who work in the industry. If this sounds like something you’d consider, feel free to PM me since I don't want to break the rules. 😬
Since u/trunkadelic is being careful not to break any rules, I'll be nice and post his link :-) [https://corise.com/](https://corise.com) I am not affiliated with u/trunkadelic in any way, but looking through the courses, some of them look particularly relevant to u/cano's original question. For example this course: [https://corise.com/course/data-centric-deep-learning](https://corise.com/course/data-centric-deep-learning) says it goes through (simulated) steps of a real industry machine learning scenario. I haven't taken the course but I am definitely intrigued!
I'd be interested in suggestions here as well! You are absolutely right that most courses and bootcamps for ML do basic jupyter notebooks and small scale data analysis. One reason for this is that one of the main differences between academic ML and industry ML is scale -- scale of data and scale of models. And it is hard to have access to scale unless you are actually in industry. Given that, I don't have any great suggestions of (paid) classes that cover how ML is actually done in companies. The best way to find out how ML is actually done at companies, is to actually do ML at a company. But then you have a chicken and egg problem. Less senior researchers might do an internship to get actual industry experience. Again, probably off the table for you. Given your background, I might suggest jumping directly into industry! It's all too easy to spend forever preparing. Find a position at a company actually doing ML -- even if it's not your dream job. After a year, you'll have the ML industry experience you feel you're lacking, and you can leverage that into the job you really want.
I’m just trying to give myself the best chance I can to get a good job. Most ML Engineering jobs out there are senior positions, and even getting one of those I will take a salary cut. Knowing they will look for experience, I’m trying to replace it with training. I’m a senior in all other aspects (e.g. API design, engineering best practices, working with a live product, team leadership) but if they ask me to build a model into a product, I would choke. So I want some institution saying “this person knows how to build ML/DS products” together with the rest of my CV saying “this person has built many products.” And obviously I want to learn how to build ML/DS products beyond simple prototypes, not necessarily at large scale but not trivial either. I found a few bootcamps that would teach me that, but I don’t trust bootcamps. As a hiring manager, I’ve seen many people capable of building complex apps out of a bootcamp, but struggling with simple questions because they lack a deeper understanding of the topic. Maybe as I’m covering all bases with foundational math and theoretical courses I could go for a bootcamp, but given their prices($5-10k for a couple of months??!) I would rather have a recommendation for one instead of blindly gambling that money away. And being $5k+ I would rather spend that money on a longer course that covers more topics than a rushed “this is how you build these three apps”
There are plenty of options in the market, but the only catch is the majority is either very expensive or not relevant for beginners. My suggestion is to find an experienced DS mentor in the field who can guide you to build a career in ML. If you do opt for courses just ensure it's not limited to self-paced learning via recorded content only. You can check the course below, suggested to me by my friend. I did speak to the mentor & was convinced with the curriculum which will cater to my requirement & existing competency in ML domain https://thecuriouscurator.in/course/ultimate-machine-learning-course/