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need to implement preprocessor in mlflow model serve using MLFLOW

I have saved and loaded model(IMAGE CLASSIFICATION) which is in pickle format using mlflow.
I have served model using mlflow model serve and REST endpoint has been exposed, i am getting reponse while providing image in numpy arrat,but it is not accepting the image directly , i need to apple the preprocessor befor calling the predict method,
Can anyone tell me how can i achieve this

AWS SageMaker: Create an endpoint using a trained model hosted in S3

I have following this tutorial, which is mainly for jupyter notebook, and made some minimal modification for external processing. I've created a project that could prepare my dataset locally, upload it to S3, train, and finally deploy the model predictor to the same bucket. Perfect!
So, after to train and saved it in S3 bucket:
ss_model.fit(inputs=data_channels, logs=True)
it failed while deploying as an endpoint. So, I have found tricks to host an endpoint in many ways, but not from a model already saved in S3. Because in order to host, you probably need to get the estimator, which in normal way is something like:
self.estimator = sagemaker.estimator.Estimator(self.training_image,
role,
train_instance_count=1,
train_instance_type='ml.p3.2xlarge',
train_volume_size=50,
train_max_run=360000,
output_path=output,
base_job_name='ss-training',
sagemaker_session=sess)
My question is: is there a way to load an estimator from a model saved in S3 (.tar)? Or, anyway, to create an endpoint without train it again?

So, after to run on many pages, just found a clue here. And I finally found out how to load the model and create the endpoint:
def create_endpoint(self):
sess = sagemaker.Session()
training_image = get_image_uri(sess.boto_region_name, 'semantic-segmentation', repo_version="latest")
role = "YOUR_ROLE_ARN_WITH_SAGEMAKER_EXECUTION"
model = "s3://BUCKET/PREFIX/.../output/model.tar.gz"
sm_model = sagemaker.Model(model_data=model, image=training_image, role=role, sagemaker_session=sess)
sm_model.deploy(initial_instance_count=1, instance_type='ml.p3.2xlarge')
Please, do not forget to disable your endpoint after using. This is really important! Endpoints are charged by "running" not only by the use
I hope it also can help you out!

Deploy the model using the following code
model = sagemaker.Model(role=role,
model_data=### s3 location of tar.gz file,
image_uri=### the inference image uri,
sagemaker_session=sagemaker_session,
name=## model name)
model_predictor = model.deploy(initial_instance_count=1,
instance_type=instance_type)
Initialize the predictor
model_predictor = sagemaker.Predictor(endpoint_name= model.endpoint_name)
Finally predict using
model_predictor.predict(##your payload)

getting a 403 response with Google AutoML vision Python API despite having assigned right Service Account

I have trained a model using google cloud AutoML Vision API, however when I specifically try to obtain the model performance metrics via the Python package I keep getting a 403 response:
PermissionDenied: 403 Permission 'automl.modelEvaluations.list' denied on resource 'projects/MY_BUCKET_ID/locations/us-central1/models/MY_MODEL_ID' (or it may not exist).
I am using the python code as layed out in the documentation and also not having any unauthorised ops with the other operations (Create Dataset, Train Model), so really struggling to understand why is this the case. Here is the code:
# Get the full path of the model.
model_full_id = client.model_path(project_id, compute_region, model_id)
print(model_full_id)
# List all the model evaluations in the model by applying filter.
response = client.list_model_evaluations(model_full_id, filter_)
Thanks for your help

After a few tests I found the problem. When calling out the model details you need to use model_id and not model_name, whereas in the previous API calls in the documentation the model_name was the identifier to use.
model_full_id = client.model_path(project_id, compute_region, model_id)
This fixed the issue.

Save word2vec model on google drive through colab

I have created a word2vec using Google Colab. However, when I try to save it using the code that I generally use to save on my computer, the file doesn't appear:
model.init_sims(replace=True)
model_name = "Twitter"
model.save()

How to deploy a Keras CNN Model to webservice?

I am currently in the process of trying to deploy a Keras Convolutional Neural Network for a webservice.
I had tried converting my saved keras hdf5 model to a tensorflow.js model and deploying that but it slowed down the client-side app as the model is relatively robust and thus, takes a large amount of space in the client memory.
Thus, I am trying to figure out a way to deploy the model in the cloud and make predictions through a request from the web-app with an image, and then receive a response which holds the prediction tensor. I know that gcloud may have some similar abilities or feature but I am unsure of how to get started.
Essentially, I am asking if there is any service that will allow me to deploy a pre-trained and saved convolutional neural network model to which I can send images in a request and use the model to return a predicted tensor?

You can export a trained Keras model and serve it with TensorFlow Serving. TF Serving allows to host models and call them via either gRPC or REST requests. You could deploy a flask app with an endpoint that accepts an image, wraps it as a payload and calls your model via the requests module.
Your code to export the model as a servable would look like this:
import tensorflow as tf
# The export path contains the name and the version of the model
model = keras.models.load_model('./mymodel.h5')
# Feth the Keras session and save the model
with keras.backend.get_session() as sess:
tf.saved_model.simple_save(
sess,
export_dir,
inputs={'images': model.input},
outputs={t.name:t for t in model.outputs})
This will store the files necessary for TF Serving. From this directory, you can host the model as follows:
tensorflow_model_server --model_base_path=$(pwd) --rest_api_port=9000 --model_name=MyModel
Your request would then look like this:
requests.post('http://ip:9000/v1/models/MyModel:predict', json=payload)
Where payload is a dictionary that contains your request image.

If you want a click-to-deploy solution for serving your model on Google Cloud, consider using Cloud ML Engine's Online Prediction service. First, follow the instructions in #sdcbr's response to export your SavedModel. Copy the model to GCS then you simply create a model and a version:
gcloud ml-engine models create "my_image_model"
gcloud ml-engine versions create "v1"\
--model "my_image_model" --origin $DEPLOYMENT_SOURCE
Or, even easier, use Cloud Console to do the above with a few clicks!
You will get a serverless REST endpoint that includes authentication and authorization, autoscaling (including scale to zero) as well as logging and monitoring, without having to write or maintain a line of code.