thankyou thankyou , thankyou very much dear ashutosh , i have wasted literally 1 year just to finding a good online course to learn about mlops , i have seen your playlist as a suggestion mannier time in past months but ignored beacuse of less number of views , but the end when i was frustated i dont know how i opened your playlist , and from that day im gladdd!!!! , thankyou brother

very well planned content ashutosh, really appreciate your efforts 👍

This was a fantastic tutorial! Thanks for taking the time to make such high-quality content.

Very well explained.. please upload more to the series.. :)

Part 1: experiment tracking using MLFlow: kzfaq.info/get/bejne/qJaUopR8u6uroYE.html

Awesome

ashutosh, u created well organized content, really helpful for complete understanding of mlflow. ultimate explanation. thanks :). keep it up.

Excellent Ashutosh....very detail presentation makes easy to understand the mlflow.....thanks for the detail explanation ashutosh....🙌🙌🙌🙌🙌🙏🙏🙏🙏

very excellent explanation Bro,i got some good knowledge after watching your videos' Thx a lot for your efforts

great video and playlist pls keep uploading

Great tutorial. Thank you for sharing.

Super tutorials on ML Flow...keep it up buddy on these topics

excellent material for beginners

After going through so many tutorials I found your tutorial to be clear and on point. Amazing tutorial. Can you make a video on docker and mlflow?

This is so amazing Channel ❣️🙌🙌

Fantastic tutorial, just one more request, can you please make a playlist for PySpark for Data Science, much needed sir

Very nice video and I appreciate your effort. One thing happened with me while trying to serve an LSTM model using TensorFlow. There is always an error because of the data shape and data type?

Good

at 30:12, you get the Run ID from an already exisitng source. I'm doing the same but having an error: RestException: INVALID_PARAMETER_VALUE: Invalid model version source: '67fd8db1a7be49fd9badace4b3a0a6e8\artifacts\model'. To use a local path as a model version source, the run_id request parameter has to be specified and the local path has to be contained within the artifact directory of the run specified by the run_id.

thanks.. it's helpful.. in 40:11 you activated the serve the model to production. but what if you want to change the version? should you stop the serving or change anything or just update the model version by using this py? thank you

Hi Ashutosh - Can you please share Model serving notebook. Your videos are very helpful. Please do upload code to github and share link soon.

How come you are getting string as prediction and model do not throws any error for ordinal encoding?

hi ashuthosh. Great videos, by the way whats the main purpose of serving a model?

could u make a video on docker w.r.t Machine learning

Hi Ashutosh, As we are using sqlite as Database, Can you please explain it in detail? How can we do that?

may i know how to do model serving for multiple models please thanks in advance

Can you show the same thing which is model serving part for CNN, Image classification import requests import numpy as np import mlflow,os, cv2 IMAGE_SIZE=120 new_prediction, img_data=[],[] NEW_TEST_DIRECTORY= 'new_prediction_data' #it has cat and dog mixed images for predictions for img in os.listdir(NEW_TEST_DIRECTORY): img_path=os.path.join(NEW_TEST_DIRECTORY,img) img_data.append(img) img_arr=cv2.imread(img_path) img_arr=cv2.resize(img_arr,(IMAGE_SIZE, IMAGE_SIZE)) new_prediction.append(img_arr) new_prediction=np.array(new_prediction) new_prediction=new_prediction/255 # Prepare inference request inference_request = { "data": new_prediction.tolist() } endpoint = "localhost:1234/invocations" response = requests.post(endpoint,data=inference_request,headers={"Content-Type": "application/json"}) predictions=np.argmax(np.array(response.json()),axis=1) print(predictions) for me its showing error can you fix this then it will be great help