Відео

if you are facing issues regarding some api error when you run docker compose up, then try installing the extension "superset support for vs code" by Netanel Gilad and restart vs code. Also uninstall docker and re install and then delete the superset folder and clone again and repeat steps from starting again.

Fantastic explanation, i am looking for this on yt for a lot time, very well explained, Thanks a lot buddy!

After watching tons of lectures, I finally found the best channel to learn MLOps. Thank you and keep going

C:\Users\xyz\incubator-superset>docker-compose up validating C:\Users\xyz\incubator-superset\docker-compose.yml: services.superset-init.env_file.0 must be a string getting this error please help me in this regard

Great job, Ashutosh! I am kind of unhappy with the UA-cam algo for not promoting your videos much. You definitely deserve more views and subscribers. The MLFlow playlist is very good.

is this a complete playlist for mlops?

This is the best video on the internet for complete beginners trying their hand at docker/airflow on a windows pc ! After multiple videos you are the only one who's addressed the issue of not having the required packages in the default docker image ! Please keep making more such videos !

continue mlops series

great sir

Can I track a file which is loaded in the GS bucket without downloading it? The source and destination will both be GS location and any change in the file in this location to be tracked by DVC?

This was very helpful, thanks!

When I run "docker-compose up" at the end it gives me errors :( . I am following steps same as video. Plz help

can you please give the link for conda vs pip?

Paper?

Sir I am from Devops background Please start mlops course in hindi

Thank you so much for the tutorials. I have been following it closely, but now I am stuck with part 3. Facing some internal server error which I do not know how to fix.

Sir how much ml required for mlops engineer

for me airfow.cgf inside config didn't created. also how to use email operator

I am a student who wants to make career in devops and cloud but with AI hype and hearing news that it will take the job roles of Devops Engineers or cloud engineers, I am sad. I want to know if AI ever replaces devops and cloud engineers , will MLops or AIops be a future proof career. Please do reply I am sad and confused. This video was helpful.

Where is diference ??? Duffer

Good video. Thanks for sharing.

PS E:\Apache> docker-compose up airflow-init no configuration file provided: not found I am getting this above error can you help me about this issue

Btw why don't you do the inference directly in fastapi? Isn't it using mlflow endpoint adding overhead cost?

What if we have custom metric? How to add this in artifect?

One of the best explainations professor. Watched the whole video. Thank you so much. Could you please explain more cutting edge AI research papers ?

thanks for the videos , Please post some video for docker based deployment of model for api serving

Man you talk too much. This would have been a 5mins video.

I am not finding the file incubator-superset

i am getting error error: evalsymlink failure on 'C:\Users\Ishwar\AppData\Local\Temp\kustomize-1010310857\manifests\kustomize\env\platform-agnostic-pns' : CreateFile C:\Users\Ishwar\AppData\Local\Temp\kustomize-1010310857\manifests\kustomize\env\platform-agnostic-pns: The system cannot find the file specified. can you please help me with this ?

❤

How can i know that superset apache is installed successfully

this video is gold. Thank you so much

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.

Now the version run to KFP v2, will you update this tutorial ? Thanks.

My project contains src folder and int this folder my service and dao class are present but when i am running it using airflow how to include src folder in airflow as i am getting error: Module not found on airflow UI

My project contains src folder and in that folder all the services and dao class are present. How to configure src folder with airflow as i am getting error module not found error on airflow UI

THANK YOU!!

i watched bunch of videos about RAG but none explained like you did with flowchart. Crystal clear

is this a complete ml ops playlist covering each and everything? @Ashutosh Tripathi

@Ashutosh Tripathi Please can you help me. What's the problem with this response? how canI resolve it? thanks a lot. "[superset-init lean 10/11] RUN --mount=type=cache,target=/root/.cache/pip pip install -e . && flask fab babel-compile --target superset/translations && chown -R superset:superset superset/translations: 2.908 /bin/sh: 1: flask: not found ------ failed to solve: process "/bin/sh -c pip install -e . && flask fab babel-compile --target superset/translations && chown -R superset:superset superset/translations" did not complete successfully: exit code: 127"

HI Ashutosh, thanks for providing the overview, it would be great if you share the project related github repo link

Instead of saying you can check by print statement .. please help to understand backend process of that as well.

Great job, I can run my first pipeline from this tutorial. Thanks.

Hello, mlflow server in your case was local that you used in endpoint, what if i am usint a remote sever like dagshub, how will i take the Letest model automatically in my dagshub

what is the ideal approach to compare data drift as Historical dataset sample size is large and the production dataset sample size is less. So for data drift do we need to select sample size matching both datasets and if also will it be correct if we choose a sample will that represent the population

video is informative...thanks for sharing

Thanks for explaining so nicely. Thank you for such a wonderful presentation and explanation.

Hi guys, thanks! One note - the incubator-superset is a fork of the original superset which does not look like alive at the moment.

Good explanation @ashutosh

Sir could you please share the link for the next video on this for backward propagation where we optimize the weights. Thanks