Josh Cohenzadeh’s Post

Really like this Back to Basics series by AWS. Always helpful to get a refresher on Core concepts 😉 https://lnkd.in/enVkHdQz

Really like this Back to Basics series by AWS. Always helpful to get a refresher on Core concepts 😉 https://lnkd.in/enVkHdQz

🚀 Exploring AWS Serverless: Key Takeaways 🚀 I've been diving into AWS Serverless, and it's been an enlightening experience! Here are some key points I've gathered: Benefits: 🌟 No Infrastructure Management: With AWS Serverless, you don't need to worry about the underlying infrastructure. Just focus on writing your code, and AWS takes care of the rest. 🌟 Simplicity in Deployment: Write your code, hand it over to AWS, and they handle everything from container images to scaling. It’s that simple! 🌟 On-Demand Execution: Your code runs only when needed, making it highly efficient and cost-effective. 🌟 Cost Efficiency: You are charged based on the number of invocations, which means you only pay for what you use. Disadvantages: ⚠️ State Management Challenges: Serverless architectures are stateless, making it difficult to manage application state across invocations. ⚠️ Socket Connection Limitations: Persistent socket connections can't be maintained, which can be a drawback for real-time applications. ⚠️ Database Performance Issues: There can be performance challenges, especially with database connections, due to the stateless nature of serverless functions. Despite the challenges, the benefits of AWS Serverless are significant, especially for applications with unpredictable workloads. It’s a game-changer for scaling and cost management. #AWS #Serverless #CloudComputing #TechLearning #AWSLambda A big thanks to Piyush Garg for the insightful youtube playlist that helped me gather these key takeaways and other aws learning. Complete playlist: https://lnkd.in/gKekKpHG

I am using the serverless framework which lets you invoke Lambda functions locally. I have now made Check it out: https://lnkd.in/d8tW3fbM Join the conversation! #amazonvpc #amazonwebservices #awslambda #serverless

Just got our first test Amazon Web Services (AWS) Bedrock Agent up and running using an aws-sample from Manju Prasad, Archana Inapudi and Amit Arora - although it was a bit out of date already. Next steps are to CDKify it and apply guardrails. It was interesting to see how AWS have given Agents the "ability" to use an entire OpenAPI schema definition when generating structured outputs. That's a nice feature. I noticed the code generation beta also which I'll be checking out. The plan is to integrate this with Google Docs for auto Doc generation

I thought this sort of poorly communicated product murder was Google's schtick: Today I learned that users can no longer create new repositories in AWS CodeCommit if they don't already have repositories there. I haven't seen any official comms from AWS about the fact they're deprecating this service, but the CodeCommit page has a banner linking to an article about moving to another repo provider, and buried away in an AWS re:Post forum an AWS employee "Veer" commented to say that: "Beginning on 06 June 2024, AWS CodeCommit ceased onboarding new customers. Going forward, only customers who have an existing repository in AWS CodeCommit will be able to create additional repositories." If you're using CodeCommit in an existing AWS Organizations org account, and wish to create repos in your other accounts, you can raise a support ticket asking for an exception. In the meantime, if you're an existing CodeCommit user, you might want to look at some other options. Let me know if my team can help you with that.

What’s the most important thing to learn in AWS

Clean up that spaghetti code. 🍝👋 #QGotThis Learn more. ➡️ https://go.aws/4gdW5e5 Stuck with someone else’s code? #AmazonQ Developer can help you untangle their mess. Documentation has never been easier with Amazon Q agents. #AWS

Let’s build in AWS!

That time estimate for AWS projects… is a SUGGESTION, not a fact! 🤣 The "Time to complete" went from 25mins to 2 hours, respectfully. If you want to know why it took me so long, I wrote a short blurb about it 😁 The goal is to complete 1 ML project a day, connecting what I study with hands-on experience and I'm feeling fancy because I just deployed a machine learning model to a real-time endpoint on Amazon Web Services (AWS). This project helped me solidify my understanding of model deployment and inference. The resource I used for this: ☁ https://lnkd.in/ekgUv7n8 #AmazonQ #AWSSageMaker #AWSCloudFormation #CloudEngineer #CloudDeveloperAdvocacy #MachineLearning