Posts

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

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 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

Cloud Migration - How can you migrate AWS EC2 instance to Google Cloud Engine VM.

In the latest Newsletter, I cover step-by-step details of how I migrated my website from AWS to Google Cloud.

Subscribe to the Newsletter for regular updates - https://lnkd.in/dSX8BNk6

ChatGPT has a long way to go for enterprise adoption.  To make ChatPT relevant for enterprise adoption, we need

Domain Adaptability
Domain Intelligence
Explainability
Transparency
Non-biased
Privacy
Scalability - Compute power for Training and Inference
Lower Environmental Footprint

What do you think? Where are ChatPT and other large language models in enterprise adoption?

Reduce your application carbon footprint - AWS Graviton3-based instances use up to 60% less energy for the same performance than comparable EC2 instances.
If you plan to move to Graviton3-based instances to reduce your carbon footprint, you must ensure your code runs on AWS Graviton3-based processor.

AWS has just released the porting Advisor for Graviton, an open-source command line tool that analyses source code and generates a report highlighting missing and outdated libraries and code construct that may require modification, along with recommendations for alternatives. It accelerates your transition to AWS Graviton-based instances by reducing the iterative process of identifying and resolving source code and library dependencies.

Link - https://lnkd.in/dD6MFu4j

#aws #sustainability #greensoftware #learning #esg Green Software Foundation

Build end-2-end AI Models with less effort. Google Cloud Vertex AI is a unified ML platform that provides end to journey for building and deploying your ML Models.

Follow these videos on how to build an end to end Retail AI applications using Vertex AI

Google Vertex AI - Unified ML Platform Overview
https://lnkd.in/dTXMtvWm

Google Vertex AI - Data Capture Overview and Pre-Processing
https://lnkd.in/eNF7xYDS

Google Vertex AI : Building Model and Training Hands-on Lab
https://lnkd.in/dHN35uyD

Google Vertex AI : Endpoint and Testing the Model Endpoint
https://lnkd.in/dm-i-kwu

Do subscribe to my channel, https://lnkd.in/eQnQACE, for weekly content on exponential tech.

What is ChatGPT? What are the current limitations? Will it replace human jobs in the future?
I have shared my perspective in my latest youtube video — https://lnkd.in/dV4iQa4A

In my view, ChatGPT (and other AI chatbots to follow) would be used like any other tool to assist you with the required information, and you apply your thinking, expertise in the domain, and intelligence to get work done.

As large language models are released, the outputs need to be explainable, safe, and non-biased. Right now, the responses are also templatized. In one of the previous videos, I also talked about whether AI is changing your thinking ability - https://lnkd.in/eU4DA6hW

We have a long way to go to infuse domain and real intelligence into AI.

If you like the content, please subscribe to my youtube channel https://lnkd.in/dJaTc7qn for weekly updates.

What is ChatGPT? What are the current limitations? Will it replace human jobs in the future?
I have shared my perspective in my latest youtube video - https://lnkd.in/dV4iQa4A.

In my view, ChatGPT (and other AI chatbots to follow) would be used like any other tool to assist you with the required information, and you apply your thinking, expertise in the domain, and intelligence to get work done.

As large language models are released, the outputs need to be explainable, safe, and non-biased. Right now, the responses are also templatized. In one of the previous videos, I also talked about whether AI is changing your thinking ability - https://lnkd.in/eU4DA6hW

We have a long way to go to infuse domain and real intelligence into AI. Here is roadmap that ChatGPT needs to tackle for enterprise adoption - https://lnkd.in/dNCxYMuu

If you like the content, please subscribe to my youtube channel https://lnkd.in/dJaTc7qn for weekly updates.