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This article feels like it’s written by someone who doesn’t understand the problems faced by ML. These people come through the MLops community every so often, t
by mountainriver 4y ago
This article feels like it’s written by someone who doesn’t understand the problems faced by ML. These people come through the MLops community every so often, they think no one has realized that dev ops and DE are similar, when in reality they just don’t yet realize how different ML is yet.
For one, the customer is entirely different. You are mostly serving data scientists who don’t have strong engineering skills, which dramatically skews the solutions toward things like Python and Jupyter.
This is a big reason why the tool space is different and has been successful at what it does.
Model training and serving are absolutely nothing like traditional methods. In serving, you are deploying a stateful model, not a stateless backend. That model’s state should ideally be continuously trained, requires different scaling and monitoring capabilities.
In training, the GPU problem is far from solved and it is unique to ML with things like how you shard models and fit their weights into memory.
There are extremely challenging problems in this space that simply aren’t the same as devops, and this is coming from a former k8s contributor.
- mylons 4y agoas someone who’s been in the industry for 20 years, and worked in ML data engineering at twitch, i think you’re just using new words (some are tools but so what) to describe moving data around and making it accessible to people who need it.
- eropple 4y agoYeah - I've never been a "data engineer", but I've been doing this sort of shoveling for twenty years. We called it "ETL" and felt faintly foolish blogging about it.
- mylons 4y agoi think ETL is the most timeless phrase because it's not tied to a trend. devops, data engineering, ML engineering, MLOps are all just lipstick on the pig.
- mountainriver 4y agoYes I've been an engineer for 15 years at a bunch of big companies. MLops isn't ML data engineering. I doubt data engineering has many changes outside of active learning.
- mylons 4y agoagain, i think these labels are useless. they just say you might understand the trendiest tool in the arena right now.
- mountainriver 4y agoML isn't about trendy tools, its a fundamentally different paradigm from deterministic programming that has a different skillset
- mylons 4y agomaybe if you’re writing gradient descent, but not if you’re creating ETL pipelines.
- nonethewiser 4y agoIt sounds more like MLops does fall into devops or data engineering but it expands the definition. That data engineers or devops people don’t have the background to do MLops doesn’t mean MLops is outside of those disciplines. MLops increasingly seems like a very specific version of these things with unique challenges. Whether or not serving models fits “traditional” methods seems irrelevant.