Submitted by aumzzzz t3_116sttj in MachineLearning
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Submitted by aumzzzz t3_116sttj in MachineLearning
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I'm gonna start sprinkling cinnamon on my GPU. Apparently that's what has been missing!
Get a consultant and they can show you how. It depends on your processes.
If you haven't already started I'd say begin with the standard engineering statistical quality control (SQC) stuff. AI/ML is great but honestly only when the existing classical techniques are no longer sufficient.
To echo the top comment, it is very difficult to implement AI/ML. Beyond the obvious technical challenges (learning how to code, problem framing, data warehousing, identifying signal in data, model fitting, tuning, retraining, etc etc) there’s the entire business implementation/change management required to actually capitalize on the predictions you’re receiving. I’m in the field and would also recommend a consultant to even see if it’s a worthwhile endeavor for your business.
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So many things you need to look into getting something set up. I don’t even know where I’d suggest you start if you want to take that on as a personal project. TBH, might be worthwhile finding someone to set it up for you and teach you to how to monitor.
I think what you're asking is how to implement ML instead of building something from the ground up. I don't know your industry, but there are lots of suppliers and startups that would happily partner with you to help you adopt these capabilities without you needing to hire a team to build your own infrastructure. Many other industries already do!
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So if I understand you correctly, you want to learn ML specifically to solve this one problem?
This is very doable. What domain is your data? e.g. tabular, images, videos?
If it's vision, are you able to share a bit on what quality control metrics you have and whether you need to detect or classify anything in the images / video?
Too generic a question - what data do you use for QC? Why do you think AI is a good fit?
There are a ton of "intro to AI" videos on YT that will explain the main domain areas (problems) where ML is a good fit. Start there.
I work for a data and AI consulting company. You can contact me on LinkedIn for a noncommitting chitchat.
millenial_wh00p t1_j98b5ma wrote
I apologize for how this post might come across, but your question is actually a very deep one and it will probably take a lot of up front work to get you an answer. Ai/ml is not like cinnamon- you can’t just sprinkle it on your business process and expect it to improve.
First you need to start with instrumenting your processes and building your data warehouse. Is your production flow instrumented for quality and efficiency measurement? If so, are the instruments verified? Do you have baseline performance metrics defined and expectations for improvement? Do you currently conduct any statistical process control? All of these questions have books that go with them, and we haven’t even built a trainable model yet.
I would start with some industrial engineering and applied stats textbooks and go from there. That should give you some idea of how to formulate a hypothesis and determine a method to validate it. From there you can start with the classics like an introduction to statistical learning by James et al and introduction to machine learning by alpaydin.