1. Real-time Visualization of AI Inference Data with Kibana


    To set up real-time visualization of AI inference data with Kibana, you would typically need to collect the AI inference data into a data store that Kibana supports, and then configure Kibana to query and visualize this data in real time.

    Elasticsearch is a common data store used with Kibana for logging, monitoring, and security analytics. For AI inference data, a common architecture involves:

    1. AI Inference service generating the data.
    2. Data streamed into Elasticsearch.
    3. Kibana connected to Elasticsearch for visualization.

    Below is a Pulumi program in Python that demonstrates how you could set up the necessary AWS infrastructure for this purpose. This includes creating an Elasticsearch domain for data storage and processing, a Kinesis Data Stream to receive inference data, and configuring IAM roles for access control. Kibana comes integrated with Elasticsearch Service in AWS, so once the Elasticsearch domain is set up, you would access Kibana through the provided endpoint:

    import pulumi import pulumi_aws as aws # Create an AWS Elasticsearch Domain for storing and searching inference data elasticsearch_domain = aws.elasticsearch.Domain("esDomain", elasticsearch_version="7.1", # Specify Elasticsearch version cluster_config=aws.elasticsearch.DomainClusterConfigArgs( instance_type="r5.large.elasticsearch", ), ebs_options=aws.elasticsearch.DomainEbsOptionsArgs( ebs_enabled=True, volume_size=10, ), tags={ "Domain": "InferenceData", }) # Create an IAM Role and Policy that allows Kinesis to send data to Elasticsearch kinesis_to_es_policy = aws.iam.Policy("kinesisToEsPolicy", policy=elasticsearch_domain.arn.apply(lambda arn: f"""{{ "Version": "2012-10-17", "Statement": [{{ "Effect": "Allow", "Action": "es:ESHttpPost", "Resource": "{arn}/*" }}] }}""") ) kinesis_to_es_role = aws.iam.Role("kinesisToEsRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "firehose.amazonaws.com" } }] }""") aws.iam.RolePolicyAttachment("kinesisToEsRoleAttachment", role=kinesis_to_es_role.name, policy_arn=kinesis_to_es_policy.arn) # Set up Kinesis Data Stream to collect inference data kinesis_stream = aws.kinesis.Stream("inferenceDataStream", shard_count=1, retention_period=24) # Export relevant properties for accessing the resources pulumi.export('elasticsearch_domain_endpoint', elasticsearch_domain.endpoint) pulumi.export('kinesis_stream_name', kinesis_stream.name)

    How to use this program:

    1. Elasticsearch Domain: The domain is set up for storing and searching the inference data. ElasticSearch version 7.1 is specified here, but you may need to adjust based on your requirements and the compatibility with Kibana.

    2. IAM Role and Policy: An IAM role and policy are created to allow Kinesis to securely send data to the Elasticsearch domain. It would be best to refine the policy to adhere strictly to the least privilege principle.

    3. Kinesis Data Stream: This stream is where the inference data will be sent. It has a retention period of 24 hours, meaning data will be stored for 24 hours before being deleted.

    4. Exports: The endpoint for the Elasticsearch domain and the name of the Kinesis Stream are exported. You would use these in your AI service to send data to Kinesis and to access Kibana, respectively.

    After running this Pulumi program, you will have the AWS infrastructure in place. The next steps involve configuring your AI service to send inference data to the Kinesis Data Stream and using the Elasticsearch domain endpoint in Kibana for setting up real-time visualizations.

    Remember, you'll need to have Pulumi CLI installed and your AWS credentials configured for this program to work. Additionally, this program assumes you already have a way to generate and send AI inference data to the Kinesis stream.

    To access Kibana, navigate to the URL provided by the elasticsearch_domain_endpoint once the domain is up and running. There, you can set up dashboards and visualizations as needed.