As an aside, another way to determine side projects in MLOps is by understanding what's out there. You can know this by reading what other companies are doing with infrastructure. For example,

- You can see how [netflix schedules their jupyter notebook jobs using papermill.](https://github.com/nteract/papermill) Here is the [blog](https://netflixtechblog.com/scheduling-notebooks-348e6c14cfd6) too.
- You can see how [uber uses a feature store](https://www.uber.com/blog/michelangelo-machine-learning-platform/?utm_campaign=tecton&utm_medium=blog&utm_source=datagrom) to manage features ML Engineers pass into machine learning models. Here is a [link](https://www.featureform.com/?gclid=CjwKCAjwge2iBhBBEiwAfXDBRxALeRuGF2_IK6OH_ILHxNTrCPSAd2N44_fzPGqy79rdUm6-au5kKhoCPU4QAvD_BwE) to open source versions of feature stores.
- The [MLOps community](https://mlops.community/) has great resources too.

Not saying you should implement everything from scratch. But reading does help to see "What kind of work would I be associated with as an MLOps engineer".

Machine Learning Engineer

Ajay halthor

I have MLE experience here, so I'll try answering these

> Will my SWE knowledge help me, or will it complement with ML? Or being MLE is a totally different/separate field?

Agreeing with Alex on this. It will definitely help. MLEs code too with python, SQL and sometimes using frameworks like pytorch, tensorflow. In simple terms, MLE role is some combination of Data, SWE and research. In the industry, MLEs work cross functionally with different teams to get the projects rolling; your SWE background will make it way easier to communicate with engineers when you need their assistance in setting up tooling.

> Is there overlapping between ML and SW?

As far as coding is concerned, yes. Also at the end of the day, MLEs and SWEs solve problems; though they might use different tools to do so. For more information on the exact day to day, you can check out this thread- <https://www.jointaro.com/question/vQKiTHNgGLB45cSUItKd/moving-to-aiml-from-web-development/>

> Would be recommended to do a Masters or Practice/Courses?

I did a masters degree and it helped me with the following:

- **learn how to learn new things** on my own and keep up with research. This skill is useful on the job as an MLE as many times you'll need to get inspiration from research papers; understand the current landscape of how other companies solved similar problems; and implement some version of what you read. Note: MLEs serve different kinds of roles in different companies so I don't know how much this applies to MLEs at Meta.
- technically understand the fundamentals of the field of machine learning.
- when i joined my job, I could tie very technical machine learning details to real world projects and products; Note: this step took me years after my masters and starting a job to realize.
- It helped me network with people with very different backgrounds and helped me understand how they think. (networking was especially important to me because I didn't study my undergrad in the united states)

That said, I am not going to say a masters is always worth it. Some of my peers in my masters program would say it wasn't worth it. To make this decision yourself, you need to first set expectations of what you wish to receive from the program. Also, ask MULTIPLE people who have done a masters specializing in machine learning to see what they got out of the program. Then ask people who also haven't done their masters to see what they think.

Also money matters - this can be a deal breaker

I am currently a Machine Learning Engineer. Will add my 2 cents here.

> would you please describe a day to day/job responsibilities for an AI/ML engineer?

The MLE role typically involves solving problems and automating workflows using machine learning; it is a good mix of SWE and Data. Depending on the company, you might be doing more of one or the other. Rather than a day-by-day. I'll give you the overview of an average project workflow:

- **Defining a Problem**: Talking to stakeholders to ensure you align on the problem to be solved.
- **Gathering Data depending on the problem**: This could be as simple as writing a SQL query (or queries) or talking to stakeholders in how to get hold of said data.
- **Data Analysis** geared towards solving a specific problem: This could involve sizing the opportunity or simply writing SQL to understand the current state of the problem. This is typically different than understanding broad company metrics and initiatives that a Data Analyst role would entail. 
- **Constructing Machine Learning Models**: The fun stuff typically in python and built in libraries like scikit-learn and pytorch.
- **Maintaining Models**: Building out Dashboards to monitor model performance over time.  Putting your SWE hat on, we would also build out typical dashboards to monitor service health (requests per second; cpu / mem utilization).
- **Communicating Results to stakeholders**: Again, probably building out dashboards to show them whats going on.
- **A/B testing**: Probably before rolling out to production, you'd be comparing the beautiful model you built to something currently in place to solve the problem. This is typically a rule based system with contrived logic to handle edge cases.

If you are more senior, then you would come up with opportunities yourself too and even handle much larger scale and longer term projects that involve a ton of shepherding. This doesn't just involve technical machine learning knowhow; but also talking to a bunch of people; looking into code bases you aren't necessarily familiar with. 

I personally read a lot of technical blogs from large companies and read (and sometimes) research papers for my work; but this isn't always going to be the case

> Granted everything checks out - do you think going for Masters makes sense? Given that finances and time is not the problem.

Maybe? No right answer here (I have a Masters in Data Science). **Grad school helps you to learn to learn**. This is the most important skill I picked up in grad school that is very useful in the MLE world (and professionally in general). Aside from this, my masters certainly helped me understand the technicalities and theoretical aspects of machine learning. I have no regrets and I have a solid understanding because of it. But it's not an absolute necessity. I too started with the Stanford Coursera Course by Andrew Ng that I believe you were referencing - It's solid. But by no means alone can get you the job in the field. 

What is best regardless of whether you go for grad school is do some end-to-end projects. Get data (from maybe Kaggle) and solve problems keeping in mind the steps I outlined above.

> Would you say I have to leave a large portion of web development skills behind when making the move? What are some of the *transferables*?

At the end of the day, we all solve problems whether you are a SWE or MLE or Sales. Your critical thinking skills about a problem and domain knowledge you have in the industry you're working in are probably the best transferables. Problem Solving skills become useful when you are communicating ideas to stakeholders; domain knowledge is useful when you are engineering features for your model - you know the right questions to ask.

> Would I have to start as an entry engineer? Do you think Masters helps here?

I doubt you'll need to start at entry level; MLEs code too. I have seen a common path is for people to work as a SWE for a few years and then transition into a senior MLE role. I have also seen the same from Data Analysts. A masters isn't an absolute necessary here. But it can be helpful to maybe gain technical insight about the models themselves; understand the bigger picture with them instead a pure application standpoint. It also helps you you want to acquire the skill of "learning to learn". 

Hope this helps you and everyone else out there thinking about transitioning into the field. Good luck :)  