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Overview about actual AI and ML Services on Google Cloud (September 2023) Service Description Vertex AI A unified platform for building, training, deploying, and managing machine learning models. Vertex AI Notebooks A managed service for running Jupyter notebooks, providing a single interface for your data, analytics, and machine learning workflow. Vertex Explainable AI Tools and frameworks to help you understand and interpret your machine learning models. AI Platform Prediction A managed service for deploying and managing machine learning models, making it easy to serve predictions to your applications and users....

Overview about actual AI and ML Services on microsoft Azure (September 2023) Service Description Azure Machine Learning A cloud-based platform for building, training, deploying, and managing machine learning models. Azure Synapse Analytics An analytics platform that combines data warehousing, big data analytics, and machine learning. Azure Databricks A unified analytics platform that combines data science, engineering, and business intelligence. Azure Cognitive Services A set of AI services that enable developers to add cognitive capabilities to their applications....

Overview about actual AI and ML Services on Amazon AWS (September 2023) Service Description Amazon SageMaker A managed machine learning (ML) service that helps data scientists and developers build, train, deploy, and manage ML models. Amazon Rekognition An image and video analysis service that provides facial recognition, object detection, scene detection, and image classification capabilities. Amazon Comprehend A natural language processing (NLP) service that provides text analysis, sentiment analysis, entity extraction, and topic modeling capabilities....

Create a Kubernetes managed Cluster on Google GCP with one line of code !!Be Careful: running ths may incur costs on your project - make sure to delete the resources when finished!! prerequisits A project with Kubernetes API enabled GCP CLI installed An environment variable CLUSTER_NAME defined with the name of the cluster Compute/Region set in project configuration to your preferred Region default VPC in place. gcloud config set compute/zone <preferred-region> PROJECT_ID=$(gcloud config get-value project) CLUSTER_NAME=<cluster-name> The Code : gcloud beta container clusters create $CLUSTER_NAME \ --cluster-version=latest \ --machine-type=e2-standard-4 \ --enable-autoscaling \ --min-nodes=1 \ --max-nodes=3 \ --num-nodes=1 Get Credentials gcloud container sclusters get-credentials $CLUSTER_NAME Check pods...

Start scripts for kubernetes native nodes These statup scripts require a UBUNTU 20.04 LTS base image! If you want to use Ubuntun 22.04 LTS, exchange for the kubenretes part ‘xenial’ with ‘jummy’. Script for the master node on AWS #!/bin/bash # Load necessary kernel modules for containerd cat << EOF | sudo tee /etc/modules-load.d/containerd.conf overlay br_netfilter EOF sudo modprobe overlay sudo modprobe br_netfilter # Configure kernel networking requirements for Kubernetes cat << EOF | sudo tee /etc/sysctl....