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The problem is scaling up. You do 2,3 - 5 projects the "hard way" manually deploying the data pipelines, model images, api, etc... then you are like.. there has

The problem is scaling up. You do 2,3 - 5 projects the "hard way" manually deploying the data pipelines, model images, api, etc... then you are like.. there has got to be a better way. It's the difference between a side project and a full department supporting the entire enterprises needs.

exdsq 5y ago
Out of interest where can I learn about MLOps specifically? I’m okay at aws and containers etc but have no idea how it looks to deploy an ML project
- dpbrinkm 5y ago
  surprised no one has said anything about the MLOps community yet
  - 0x008 5y ago
    The blog post mentions it.
- pyronik19 5y ago
  This has been my "bible" for the last 2-3 years... https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning https://cloud.google.com/architecture/mlops-continuous-deliv... It covers differing levels of maturity, with the Maturity Level 0 being what the op was mentioning. Our team is trying to dig out of Level 1 to Level 2.
- mmanulis 5y ago
  Check out https://mlops.community/ https://mlops.community/ as well. The slack community is pretty active too.
- WaxProlix 5y ago
  AWS has SageMaker Pipelines, which you can spin up into a default MLOps setup through the SageMaker Studio -> Projects -> Default Projects(?) flow. It's a bunch of stuff effectively vended through Service Catalog, so you're free to modify and inspect it all.
- reachtarunhere 5y ago
  Andrew Ng has a series of courses on Coursera https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops https://www.coursera.org/specializations/machine-learning-en... Even the free videos are valuable if you do not have experience deploying real world systems.
- aiaffinity 5y ago
  At the risk of self promoting, I have been teaching a (selective and somewhat basic) course on MLOps since 2020: https://github.com/thejat/mlops-notebooks/blob/master/Syllabus.ipynb https://github.com/thejat/mlops-notebooks/blob/master/Syllab...

exdsq 5y ago

Out of interest where can I learn about MLOps specifically? I’m okay at aws and containers etc but have no idea how it looks to deploy an ML project

dpbrinkm 5y ago
surprised no one has said anything about the MLOps community yet
- 0x008 5y ago
  The blog post mentions it.
pyronik19 5y ago
This has been my "bible" for the last 2-3 years... https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning https://cloud.google.com/architecture/mlops-continuous-deliv... It covers differing levels of maturity, with the Maturity Level 0 being what the op was mentioning. Our team is trying to dig out of Level 1 to Level 2.
mmanulis 5y ago
Check out https://mlops.community/ https://mlops.community/ as well. The slack community is pretty active too.
WaxProlix 5y ago
AWS has SageMaker Pipelines, which you can spin up into a default MLOps setup through the SageMaker Studio -> Projects -> Default Projects(?) flow. It's a bunch of stuff effectively vended through Service Catalog, so you're free to modify and inspect it all.
reachtarunhere 5y ago
Andrew Ng has a series of courses on Coursera https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops https://www.coursera.org/specializations/machine-learning-en... Even the free videos are valuable if you do not have experience deploying real world systems.
aiaffinity 5y ago
At the risk of self promoting, I have been teaching a (selective and somewhat basic) course on MLOps since 2020: https://github.com/thejat/mlops-notebooks/blob/master/Syllabus.ipynb https://github.com/thejat/mlops-notebooks/blob/master/Syllab...