interpretable-ml-book VS jina

If you're a student, I'd recommend this book :https://christophm.github.io/interpretable-ml-book/

Pretty much everyone shows SHAP plots now. Definitely the way to go. Check out the Christoph Molnar book. https://christophm.github.io/interpretable-ml-book/

Yes, there are many techniques beyond the two you listed. I suggest doing a survey of techniques (hint: explainable AI or XAI), starting with the following book: Interpretable Machine Learning.

Ah, alright. It sounds like you're looking for interpretability so I'd suggest this amazing overview of it by Christoph Molnar. If you choose the right models, or the right way of interpreting those, it can help a ton in communicating not only your results, but also what you did to obtain them.

Not necessarily; decision trees, Naive Bayes, etc., are interpretable. I'd refer to Molnar--specifically his Interpretable Machine Learning text--if you are interested in that subject.

Consulting something like Interpretable Machine Learning or the documentation of a package like the vip package would also be a really, really good place to start.

Just read a book called Interpretable Machine Learning which focuses on analyzing ML models and determine which inputs has more impact in the result.

Calling it a book on SHAP undersells it. https://christophm.github.io/interpretable-ml-book/

Jina mainly because of their use of neural networks and AI.

Jina - It’s an open-source project where you can build search engines. Well maybe not no code but it claims that you only need a few lines of code for creating projects. The project supports semantic, text, image, audio, and video search. What I’m also interested in is with their neural search and generative AI. I’m also interested in the amount of github repo that they have. I have this on my radar since this is also something I was interested in.

Do you guys have any ideas how we can match images on our database? We’re working on a project that about matching images on our database. We were trying to use SIFT and some other similar methods, but for some reason, nothing doesn’t seem to be working that well. Does anyone have any suggestions for the most effective way to do this? Maybe some open-source solutions like HuggingFace or Jina AI? We just want to make sure our image matching is correct and that part’s been a bit of a struggle on our part.

The tech lets you find 3D models without sifting through tons of text - An information retrieval framework does the heavy lifting and compares models to each other, no descriptions or keywords needed.

Jina AI -They offer a neural search solution that can help build smarter, more efficient search engines. They also have a list of cool github repos that you can check out. Similar to Vertex AI, they have image classification tools, NLPs, fine tuners etc.

Well, it's not exactly a new feature, but we've been working on early support for DocArray v2 in Jina.

Jina let’s you serve all of your models through the same Gateway while deploying them as individual microservices. You can also tie your models together in a pipeline if needed. Also some nice ML focussed features such as dynamic batching.