Is it in the serve instance task console that I should check the exception stack?

I test this: None , and I haven't encountered any error. I will test the custom example and I will provide you with feedback, thank you very much for your response.

Here it is:

How is the endpoint rehistred: clearml-serving --id 6c9c2c38e70b41e0a63547e3c16db234 model add --engine sklearn --endpoint "best_diabetes_detection" --preprocess "/home/caleb/diabetes_clearml/preprocess.py" --model-id e7532b8017ad4a0f92d5b537401f0585

I ran the test, but there was no result. I need to calculate a variation of average: avg(100*increase(test12_model_custom:Glucose_bucket[1m])/increase(test12_model_custom:Glucose_sum[1m])). The variation per minute. I tried using delta, but encountered an error.

Thank you very much for your help. I will test it.

I used this PromQL query: 100 * increase(test12_model_custom:Glucose_bucket[1m]) / increase(test12_model_custom:Glucose_sum[1m]) to visualize the distribution of the variable (in my case called Glucose). So according to your explanation, I should calculate a new metric: sum(abs(test12_model_custom:Glucose_bucket - histogram_avg(test12_model_custom:Glucose_bucket[1m]))). I set up the alert rule on this metric by defining a threshold to trigger the alert. Did I understand correctly?

Or the new metric should be: sum(abs((100 * increase(test12_model_custom:Glucose_bucket[1m]) / increase(test12_model_custom:Glucose_sum[1m])) - histogram_avg((100 * increase(test12_model_custom:Glucose_bucket[1m]) / increase(test12_model_custom:Glucose_sum[1m]))[1m])))?

Thank you very much

@<1523701205467926528:profile|AgitatedDove14> , thank you very much for your help, I was able to fix most of my bugs thanks to your recommendations

This p is not in the original code.

When I calculated the average, I got this result. Now, with this new metric, I need to calculate the variation per minute. I tried increase, rate, delta, but no result, just an error: bad_data: 1:110: parse error: ranges only allowed for vector selectors: delta(avg(100*increase(test12_model_custom:Glucose_bucket[1m])/increase(test12_model_custom:Glucose_sum[1m]))[1m])

I've gone through the tutorial, and I've more or less understood it. I will perform a test to make sure. Thank you very much for sharing. Please, I have a question to submit to you, do you think it's a good idea to combine this monitoring with Evidently, to calculate new metrics and visualize them in Grafana?

Here it is: curl -X POST " None " -H "accept: application/json" -H "Content-Type: application/json" -d '{"Pregnancies": 6, "Glucose": 148, "BloodPressure": 72, "SkinThickness": 35, "Insulin": 0, "BMI": 33.6, "DiabetesPedigreeFunction": 0.627,"Age": 50}'
{"detail":"Error processing request: node array from the pickle has an incompatible dtype:\n- expected: [('left_child', '<i8'), ('right_child', '<i8'), ('feature', '<i8'), ('threshold', '<f8'...

from typing import Any

import numpy as np

Notice Preprocess class Must be named "Preprocess"

class Preprocess(object):
def init(self):
# set internal state, this will be called only once. (i.e. not per request)
pass

def preprocess(self, body: dict, state: dict, collect_custom_statistics_fn=None) -> Any:
    # we expect to get two valid on the dict x0, and x1
    return [[body.get("Pregnancies", None), body.get("Glucose", None), body.get("BloodPressure...

Thanks to the exception stack I examined, I understood that I had a model registry issue. I had used joblib to save the model file on my system, and I believed that the model registration in ClearML storage was automatic. So when I made the API call, the model path returned NoneType. Once I fixed that, I was able to serve my model and make API calls giving prediction results. Also, thanks to your help, I understood that I needed custom serving, and I was able to modify the preprocess.py file ...

All these run correctly

Great!

To check the data drift, I need to calculate the avg of the last query by time bucket and calculate the variation by minute of the new metric

Thank you, @<1523701070390366208:profile|CostlyOstrich36>

@<1523701205467926528:profile|AgitatedDove14> , Thank you very much, I will follow your recommendation.

Now, when I add delta to calculate the variation of this: error: bad_data: 1:110: parse error: ranges only allowed for vector selectors

I had already followed this tutorial, but the configuration of alerts was not covered.

I feel like to do this, I need to create a recording rule from the metric avg(...) at the Prometheus level and then query increase(). However, this approach requires me to interact directly with Prometheus.

I have other similar endpoints for testing; that's why, if not, there is no error at this level. Even with the two endpoints, I get the same error. One clarification: I built my ML model with scikit-learn pipeline and Optuna. Now, by building another simple model without Optuna and the preprocessing pipeline with scikit-learn, that is, by simply using, for example, LogisticRegression().fit(X, y), I do not encounter any error for serving with clearml-serving; the request via its endpoint gives...

Just that. Thank you.

I will work on it and provide you with feedback. Do you have a list of monitoring metrics provided by clearml-serving?

Alternatively, can I directly define my alert on avg(...)

Do you have any advice for this step, (monitoring)? I feel like it's not very well documented.