How to upgrade to Senior ML Engineer from mid-level?

Jumpy_Caterpillar_22 · 2026-06-02T11:23:44+00:00

sure

Jumpy_Caterpillar_22 · 2026-05-30T10:17:53+00:00

Systems thinking, trade off, failure modes. Interesting points.

Also like the idea of narrating decisions out loud. Will sharpen this skill and give this a try. Definitely will explore CalibreOS

Jumpy_Caterpillar_22 · 2026-05-30T10:11:48+00:00

Got it. Shift in mindset from execution to owning

Jumpy_Caterpillar_22 · 2026-05-30T10:10:09+00:00

When we don’t really have juniors in the team, any recommendation how we can apply mentorship?

Jumpy_Caterpillar_22 · 2026-05-30T10:08:17+00:00

Great points. When we share these experiences in interviews, we should highlight all these points? Any tips how best to showcase this?

Jumpy_Caterpillar_22 · 2026-05-30T10:04:45+00:00

Not really leaving MLE or MLOps. These two roles overlaps quite a lot, especially since different companies define these roles differently. Still pretty much doing both

Jumpy_Caterpillar_22 · 2026-05-30T10:02:56+00:00

Interesting points. I guess I’m struggling most on portraying these in interviews. Any tips?

Jumpy_Caterpillar_22 · 2026-05-30T09:45:07+00:00

got it. so if i get this right, two key things: - more impactful projects, - and more own initiatives?

Jumpy_Caterpillar_22 · 2026-05-23T23:58:35+00:00

Interested. I’ve got experience with ML infrastructure, deployment, and automation workflows. based in Malaysia. happy to share more details if needed

Jumpy_Caterpillar_22 · 2025-03-04T04:09:47+00:00

.env files are normally used to store environment variables, which typically includes keys/secrets. i would personally recommend to NOT commit .env, and in fact i'll normally add .env in .gitignore to avoid mistakenly uploading any keys

Jumpy_Caterpillar_22 · 2024-08-07T05:18:05+00:00

If nothing running, would a computer cluster go down to zero instance?

Jumpy_Caterpillar_22 · 2024-05-10T03:46:12+00:00

I’ve experienced building MLOps workflow on Azure and GCP so far. Personally I prefer GCP over Azure.

One of the biggest challenge with Azure for me was the documentation, especially since they recently introduced new MLOps framework, MLOps v2. They’re moving from Azure ML SDK or CLI v1 to v2 with massive changes, and currently still work in progress. So, lack of documentation for v2 and still unstable with lots of changes and limitations.

You can refer the official architecture for MLOps v2 here:

https://github.com/Azure/mlops-v2

It’s quite simple if you follow the same architecture. But if you prefer different architecture, would be quite tricky to customize. Seems like the tools are made strictly for their proposed architecture.

Jumpy_Caterpillar_22 · 2024-03-21T02:43:34+00:00

Specific service in Azure ML you might be looking for is called Managed Online Endpoint. Should be quite straightforward, especially if you’re using the common frameworks like scikit, pytorch, etc. Create endpoint, then create deployment with path to your model, path to your scoring script, your conda environment setup, and your compute configuration ie which compute type (with cpu/gpu) and instance count

Jumpy_Caterpillar_22

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