#other #aws #azure #cloud #cloud_computing #cloud_providers #free_hosting #free_tier_offers #freebie #freebies #gcp #google_cloud #google_cloud_platform #hacktoberfest #ibm_cloud #oci #oracle #postgresql #serverless

https://github.com/cloudcommunity/Cloud-Free-Tier-Comparison

#html #amazon_web_services #aws #aws_certified_cloud_practitioner #aws_cloud_practitioner #certification #clf_c01 #clf_c02 #cloud #cloud_practitioner #devops #practice_exams

https://github.com/kananinirav/AWS-Certified-Cloud-Practitioner-Notes

#python #aws #aws_cloudformation #cfn_lint #cloudformation #linter

https://github.com/aws-cloudformation/cfn-lint

#hcl #aws #aws_eks #aws_eks_cluster #eks #elastic_kubernetes_service #kubernetes #terraform #terraform_module

This Terraform module helps you create and manage Amazon EKS (Kubernetes) resources on AWS. It allows you to set up an EKS cluster, manage node groups, and configure various settings such as security groups, IAM roles, and logging. You can enable features like Elastic Fabric Adapter (EFA) support and IRSA (IAM Roles for Service Accounts) for enhanced performance and security.

Using this module, you can easily automate the creation of your EKS cluster and associated resources, making it simpler to manage your Kubernetes environment on AWS. This automation saves time and reduces the complexity of manual configuration, ensuring your cluster is set up correctly and securely.

https://github.com/terraform-aws-modules/terraform-aws-eks

#jupyter_notebook #aws #data_science #deep_learning #examples #inference #jupyter_notebook #machine_learning #mlops #reinforcement_learning #sagemaker #training

SageMaker-Core is a new Python SDK for Amazon SageMaker that makes it easier to work with machine learning resources. It provides an object-oriented interface, which means you can manage resources like training jobs, models, and endpoints more intuitively. The SDK simplifies code by allowing resource chaining, eliminating the need to manually specify parameters. It also includes features like auto code completion, comprehensive documentation, and type hints, making it faster and less error-prone to write code. This helps developers customize their ML workloads more efficiently and streamline their development process.

https://github.com/aws/amazon-sagemaker-examples

#python #ai #aws #developer_tools #gpt_4 #llm #llmops #python

Phidata is a tool that helps you build smart AI agents with memory, knowledge, tools, and reasoning. You can use it to create agents that can search the web, get financial data, or even write and run Python code. Here’s how it benefits you You can install Phidata using a simple command `pip install -U phidata`.
- **Versatile Agents** Agents can use reasoning to solve problems step-by-step and access knowledge bases to provide accurate information.
- **User-Friendly Interface** It includes built-in monitoring and debugging tools to help you track and fix issues with your agents.

Overall, Phidata makes it easy to create and manage intelligent AI agents that can perform complex tasks efficiently.

https://github.com/phidatahq/phidata

#python #aws #cloud #continuous_integration #developer_tools #localstack #python #testing

💻 A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline

https://github.com/localstack/localstack

#typescript #agents #ai_agents #ai_agents_framework #anthropic_claude #aws #aws_bedrock #aws_cdk #aws_lambda #chatbot #framework #generative_ai #machine_learning #openai #openaiapi #orchestrator #python #serverless #typescript

The Multi-Agent Orchestrator is a powerful tool that helps manage multiple AI agents to handle complex conversations. It intelligently routes user queries to the most suitable agent based on context and content, ensuring coherent interactions. Here are the key benefits Automatically directs user queries to the right agent.
- **Context Management** Supports both streaming and non-streaming responses, and can run on various platforms including AWS Lambda and local environments.
- **Customization** Comes with ready-to-use agents and classifiers for quick deployment.

This makes it ideal for applications ranging from simple chatbots to sophisticated AI systems, providing efficient and scalable solutions.

https://github.com/awslabs/multi-agent-orchestrator

#jinja #ansible #aws #bare_metal #gce #hacktoberfest #high_availability #k8s_sig_cluster_lifecycle #kubernetes #kubernetes_cluster #kubespray

You can use Kubespray to easily deploy a production-ready Kubernetes cluster on various cloud providers like AWS, Azure, OpenStack, and more, or even on bare metal. This tool offers a highly available cluster and allows you to choose your network plugin, supporting many popular Linux distributions. It also includes continuous integration tests to ensure stability. To set up, you can use Ansible or Vagrant, and there are detailed guides and community resources available to help you through the process. This makes it easier and faster to get a reliable Kubernetes cluster up and running, saving you time and effort.

https://github.com/kubernetes-sigs/kubespray

#go #aws #terraform #terraform_provider

The Terraform AWS Provider helps you manage Amazon Web Services (AWS) resources using Terraform. This tool allows you to create, update, and delete AWS resources easily and efficiently. You can find guides on how to contribute, a development roadmap, FAQs, tutorials, and community forums for support. Using this provider, you can automate your AWS infrastructure management, making it easier and faster to set up and maintain your cloud resources. This saves time and reduces errors, making your work more efficient.

https://github.com/hashicorp/terraform-provider-aws