AWS Sagemaker, InvokeEndpoint operation, Model error: "setting an array element with a sequence."

will help you I am trying to convert Matlab code into Python, but I'm receiving an error when I append zeros in my array. , You want to join the list with the array, so try

bits=concatenate(([0,0,0,0,0,0,0,0], bits))

>> x = [1,2,3]

x =

     1     2     3

>> [0,0,x]

ans =

     0     0     1     2     3

>>> x = [1,2,3]
>>>
>>> [0,0,x]
[0, 0, [1, 2, 3]]
>>> 
>>> [0,0] + x
[0, 0, 1, 2, 3]

I wish this helpful for you The one thing I can spot is that the y[5] here looks like a typo:

Any of those help The spokenMean type is object, i.e. it's a 1D array which holds smaller 1D arrays. The first thing you need to do is to convert it to a 2D float array. This worked for me:

spokenMean = features(spoken, np.mean)
spokenMean = np.vstack(spokenMean[:]).astype(np.float32)

result = np.concatenate([spokenMean, written], axis=1)

wish helps you The problem relied on the data format as suspected. In my case all I had to do is send the data as a json serialized string array and use ContentType = application/json because the python function running on the endpoint which is responsible for sending the data to the predictor was only accepting json strings.
Another way to solve this issues is to modify the python function which is responsible for the input handling to accept all content types and modify the data in a way that the predictor will understand.

        var data = new string[] { "this movie was extremely good .", "the plot was very boring ." };
        var serializedData = JsonConvert.SerializeObject(data);

        var credentials = new Amazon.Runtime.BasicAWSCredentials("","");
        var awsClient = new AmazonSageMakerRuntimeClient(credentials, RegionEndpoint.EUCentral1);
        var request = new Amazon.SageMakerRuntime.Model.InvokeEndpointRequest
        {
            EndpointName = "endpoint",
            ContentType = "application/json",
            Body = new MemoryStream(Encoding.ASCII.GetBytes(serializedData)),
        };

        var response = awsClient.InvokeEndpoint(request);
        var predictions = Encoding.UTF8.GetString(response.Body.ToArray());

it should still fix some issue The problem was with the MFA autharization. When I invoked the model from inside the model, the MFA was passed. But when I tried to invoke the model from my machine, the MFA was not passed, so the access was denied.
I created special user without MFA to debug the model, and that solved my problem.