Examples: query, "exact match", wildcard*, wild?ard, wild*rd
Fuzzy search: cake~ (finds cakes, bake)
Term boost: "red velvet"^4, chocolate^2
Field grouping: tags:(+work -"fun-stuff")
Escaping: Escape characters +-&|!(){}[]^"~*?:\ with \, e.g. \+
Range search: properties.timestamp:[1587729413488 TO *] (inclusive), properties.title:{A TO Z}(excluding A and Z)
Combinations: chocolate AND vanilla, chocolate OR vanilla, (chocolate OR vanilla) NOT "vanilla pudding"
Field search: properties.title:"The Title" AND text
Hi Community, I’Ve Just Posted My First Blog Post About Mlops. I Am Open To Any Suggestions.

Hi community,
I’ve just posted my first blog post about MLOps.
I am open to any suggestions.


Posted 2 years ago
Votes Newest

Answers 3

Here's my two cents worth.
I thought its really nice to start off the topic highlighting 'pipelines', its unfortunately one of the most missed component when ppl start off with ML work. Your article mentioned about drfits and how MLOps process covered it. I thought there are 2 more components that was important and deserves some mention.Retraining pipelines. ML engineers tend not to give much thought to how they want to transit a training pipeline in development to a automated retraining pipeline in production. Personally i feel the export/import process of the above should be seamless. Model Robustness Testing. Against the so many vulnerabilities such as Adversarial attacks, Biasness, Explanability...etc. I would imagine a Robustness Testing Pipeline in the MLOps process to be an important one as well. The problem is, this area is so nascent, with no industrial standards. What technique is right?

Posted 2 years ago

Hi. nice read. Your permalink is wrong though, here's the right one.

Posted 2 years ago

Only today I've saw your comments (did not get notified for some reason)
Thanks for you suggestions

Posted one year ago
3 Answers
2 years ago
one year ago