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Green Software Foundation has come a long way. We have a vibrant community through various workgroups, all working toward our goal of reducing carbon emissions associated with the software.

I remember my conversation with Sanjay Podder in July 2021, who is instrumental in setting up the Green Software Foundation, and the first goal was to create a specification for measuring and reducing software emissions and making Software Carbon Intensity specification actionable. Glad to see it being realized.

Thanks to the standards co-chairs, Henry Richardson and Abhishek Gupta as we keep improving the SCI specification, and Asim Hussain for steering the foundation.

A Typical Lifecycle of a Cloud Project consists of the following -

① Setup/Enable Billing and Create Cloud Project
② Give the required permission to project
③ Choose Regions and Create a Virtual Private Cloud (VPC) for your environments - Select regions based on cost, latency, and low carbon regions..
④ Determine what cloud services to use based on your requirements - Managed services, Serverless …
⑤ Designing and develop cloud applications - Container-based development, Event-driven architecture …
⑥ Automation, Test, and CI/CD - Setup Container Registry, Automated Build, and Integration …
⑦ Logging, Monitoring, and Backups Strategy
⑧ Production setup - Setup and Security Controls - Setup Firewalls, Security, Audits
⑨ Cloud Optimization - Resource optimization, Cost optimization…

Where do you think the maximum time is spent? Which phase impacts your cloud costs?

Checkout my latest video for more details - https://lnkd.in/dsTAFHnH.
Do subscribe to the youtube channel for regular updates.

Learn about Containers, Container Managed services, and Kubernetes. If you are new to container-managed solutions and Kubernetes and looking to get started, enroll for the free course at - https://lnkd.in/d-9mUzT5 to

A Container management service provides container orchestration and the ability to create, deploy, and scale containers automatically. Containers package everything you need to run the application consistently.

A container management service also provides features like load balancing, networking, monitoring, rollback of containers, and handling upgrades of cluster nodes.

There are many container managed platforms, and one of the leading open-source container orchestration software is Kubernetes. Kubernetes was originally developed by Google and made open source in 2015 through Cloud Native Computing Foundation (#cncf )
.

Learn about Containers, Kubernetes, Google Kubernetes Engine (GKE), and GKE Autopilot. If you are new to container-managed solutions and Kubernetes and looking to get started, enroll for the free course at - https://lnkd.in/d-9mUzT5 to

A Container management service provides container orchestration and the ability to create, deploy, and scale containers automatically. Containers package everything you need to run the application consistently.

A container management service also provides features like load balancing, networking, monitoring, rollback of containers, and handling upgrades of cluster nodes.

There are many container managed platforms, and one of the leading open-source container orchestration software is Kubernetes. Kubernetes was originally developed by Google and made open source in 2015 through Cloud Native Computing Foundation (#cncf )
.

Deploy CI/CD pipeline with Policy Configuration - A step-by-step guide with code and configurations to set up CI/CD pipeline with Policy configuration and validations. Download the guide for more details.

At a high level, the process consists of the following -
➤ An application is broken down into a set of modules. Developers work on the respective modules and check their code in the Git source repository.
➤The CI tools like Cloud Build or Jenkins are configured to listen to any changes in the Git source repository. The changes could be a code commit to a particular branch, tagging a release, etc. It then tests the code, builds the container image, and stores it in Google Container Registry or Artifact Registry.
➤ Once the container image is pushed to the container registry, the config files are updated with the new image URL. The operations team also uses Git to store all the configurations like Kubernetes manifests, security policy manifests, and infrastructure code like terraform scripts. The declarative code is usually templatized using tools and best practices which are then applied for deployment across environments.
➤ The template files provide placeholders for injecting property values at runtime (like replicas : 2 for development and 5 for production). Tools like Kustomize or Helm can create templates for your manifests without changing the original base manifest (YAML) files. Anthos provides integration with various open-source packaging and customization tools implemented as part of best practices.
➤ The changes from earlier steps are reviewed, approved, and then merged into the respective branch in that environment. Once the code is merged, the CD tools like Cloud Build pick up the changes from that branch and apply the configuration for deployment to the respective GKE environment.
➤The operations or security team stores the governance or security policy configurations in Git. The ACM component of Anthos can be used to enforce these policies to multiple GKE clusters across environments.
➤Once the application is up and running, you can leverage ASM to gain more visibility into your services and benchmark its performances by defining Service Level Objective (SLO) in alignment with your application requirements.

Learn about Containers, Kubernetes, Google Kubernetes Engine (GKE), and GKE Autopilot. If you are new to container-managed solutions and Kubernetes and looking to get started, enroll for my free course at - https://lnkd.in/d-9mUzT5

A Container management service provides container orchestration and the ability to create, deploy, and scale containers automatically. Containers package everything you need to run the application consistently.

A container management service also provides features like load balancing, networking, monitoring, rollback of containers, and handling upgrades of cluster nodes.

There are many container managed platforms, and one of the leading open-source container orchestration software is Kubernetes. Kubernetes was originally developed by Google and made open source in 2015 through Cloud Native Computing Foundation (#cncf )
.

How do you improve the CI/CD and MLOps Pipeline? One way is to gather the required metrics to track your DevOps CI/CD and MLOps Pipeline. Some of the suggested metrics are -

Commit to Deployment Time- The average time from commit to deployment.
Number of Failures - Number of Failures due to code, configuration, or policy violations
The number of Code Changes - Number of code changes/features in the commit.
Frequency of Deployment - Number of deployments
Time to Fix failures - Average time required to fix deployments
Training Time - Time to train the ML model

How do you improve the CI/CD and MLOps Pipeline? One way is to gather the required metrics to track your DevOps CI/CD and MLOps Pipeline. Some of the suggested metrics are -

Commit to Deployment Time- The average time from commit to deployment.
Number of Failures - Number of Failures due to code, configuration, or policy violations
The number of Code Changes - Number of code changes/features in the commit.
Frequency of Deployment - Number of deployments
Time to Fix failures -  Average time required to fix deployments
Training Time - Time to train the ML model

What is Multi-Cloud Development, and how should you design
and develop Multi-Cloud applications for Google Cloud, Azure, and AWS.

A roadmap to integrate FinOps, and DevOps to maximize cloud efficiency and drive business outcomes

Follow for latest #cloud updates: https://lnkd.in/dJ8hTWrs

If you are building Infrastructure as code, look at
Pulumi Python SDK, which makes it easier to build #cloud
Infrastructure code using python.

Here is a sample code representation for a GKE Setup with preemptible nodes. Pulumi is multi-cloud and supported on #Azure, #AWS , and #GCP. They also convertors to covert CloudFormation, and Terraform scripts to Pulumi and support for other languages like #Go, C#, #java

You can also add Policy and development practices as part of CI/CD integration. I will share how to embed policy and devSecOps in the next post.

Link - https://lnkd.in/dY4V-JFg

Follow for the latest #cloud updates: https://lnkd.in/dJ8hTWrs

If you are building Infrastructure as code, look at
Pulumi Python SDK, which makes it easier to build #cloud
Infrastructure code using python.

Here is a sample code representation for a GKE Setup with preemptible nodes. Pulumi is multi-cloud and supported on Azure, AWS, and GCP. They also convertors to covert CloudFormation, and Terraform scripts to Pulumi.

You can also add Policy and development practices as part of CI/CD integration. I will share how to embed policy and devSecOps in the next post.

Link - https://lnkd.in/dY4V-JFg

If you are building Infrastructure as code using python, look at
Pulumi Python SDK, which makes it easier to build #cloud
Infrastructure code using python.

Here is a sample code representation for a GKE Setup with preemptible nodes. Pulumi is multi-cloud and supported on Azure, AWS, and GCP. They also convertors to covert CloudFormation, and Terraform scripts to Pulumi.

You can also add Policy and development practices as part of CI/CD integration. I will share how to embed policy and devSecOps in the next post.

Link - https://lnkd.in/dY4V-JFg

Get Upskilled - Happy to share the launch of the Free Learning module with 15+ courses. These courses range from byte size learnings to detail Hands-on Lab.

Enroll at - https://lnkd.in/dT5amJ7r.

The current course list includes the following -
Getting Started with Cloud Computing
Getting Started with Google Cloud – Fundamentals
Getting Started with Google Cloud – Advanced Course – Kubernetes, Serverless, API Gateway, and Security
Google Cloud Production Lab – Architecture and End-2-End Deployment
Google Cloud Anthos – Introduction, Architecture, and Deployment
Google Cloud Anthos – Advanced Labs
Multi-Cloud Development and Cloud Development Lifecycle
Google Vertex AI – How to build an end-to-end Retail AI classification system
Cloud Migration – Migration from any cloud to AWS
AI Chatbots and Limitations
Bitcoin, Blockchain, and Enterprise Blockchain Architecture
Web 3.0 and NFT – Basic Introduction
Metaverse and Metaverse Technical Architecture
Internet of Things – Basic Concepts and Architecture
Introduction to the Ethereum Platform and Consensus Algorithm
Application Of IoT In Manufacturing

Happy Learning !!

As you build multi-cloud applications as part of the DevOps strategy, consider the choices of compatible services for flexibility and moving across cloud providers as and when required.

Last year 20+ new services were launched by AWS, Azure, and Google Cloud, and 100+ feature enhancements to existing services.

If you are building multi-cloud applications or migrating across cloud providers, I have created a web-based tool that can help -

• compare and discover cloud services by functions
• strategize on what compatible cloud services to use for multi-cloud development.

The cloud comparison tool is updated with the latest service offerings. You can visit the comparison tool at - https://lnkd.in/d4aWq8zZ

Feedbacks welcome !!

As part of your multi-cloud strategy, one of the key requirements is handling data across cloud providers and getting a unified data view.

With Google BigLake, you can continue hosting your data on AWS and Azure and still get a unified view.

Here is a representative view of BigLake and how it provides the unified capability. In this scenario, data execution happens in the required environment, providing service flexibility and saving on data transfer, and only the results are transferred (if needed).

Follow for the latest #cloud updates: https://lnkd.in/dJ8hTWrs

Last year 20+ new services were launched by AWS, Azure, and Google Cloud, and 100+ feature enhancements to existing services.

If you are building multi-cloud applications or migrating across cloud providers, I have created a web-based tool that can help -

• compare and discover cloud services by functions
• strategize on what compatible cloud services to use for multi-cloud development.

The cloud comparison tool is updated with the latest service offerings. You can visit the comparison tool at - https://lnkd.in/d4aWq8zZ

Feedbacks welcome !!

Starting next week, I will share a series of posts summarising the services launched by the cloud providers.

Follow for the latest updates: https://lnkd.in/dJ8hTWrs

AI has a significant carbon footprint. As per a study by MIT, training large AI models can emit more than 626,000 pounds of carbon dioxide equivalent —nearly five times the lifetime emissions
of the average American car.

As we build and deploy AI models, we need to consider how we can develop, train, and inference/deploy ML models using Green AI principles.

Green AI is all about how to embed energy efficiency and reduce carbon emissions as part of the overall AI process.

It is imperative that large language models like #ChatGPT follow Green AI practices, where millions of users would use AI.

Here is a snippet from the IndoML conference, where we shared our perspective on Green AI.

Last year 20+ new services were launched by Azure, AWS, and Google Cloud, and 100+ feature enhancements to existing services.

If you are building multi-cloud applications or migrating across cloud providers, I have created a web-based tool that can help -

• compare and discover cloud services by functions
• strategize on what compatible cloud services to use for multi-cloud development.

The cloud comparison tool is updated with the latest service offerings. You can visit the comparison tool at - https://lnkd.in/d4aWq8zZ

Feedbacks welcome !!

Starting next week, I will share a series of posts summarising the services launched by the cloud providers.

Follow for the latest updates: https://lnkd.in/dJ8hTWrs

AI has a significant carbon footprint. As per a study by MIT, training large AI models can emit more than  626,000 pounds of carbon dioxide equivalent —nearly five times the lifetime emissions
of the average American car.

As we build and deploy AI models, we need to consider how we can develop, train, and inference/deploy ML models using Green AI principles.

Green AI is all about how to embed energy efficiency and reduce carbon emissions as part of the overall AI process.

It is imperative that large language models like #ChatGPT follow Green AI practices, where millions of users would use AI.

Here is a snippet from the IndoML conference, where we shared our perspective on Green AI.

#ai #ml #learning #greenai #technology Green Software Foundation