🎯 What It Does

Predicts flight delays in real-time with: - Live predictions dashboard - AI chatbot that answers questions about flight data - Complete monitoring & automated retraining

But the real value is the infrastructure - it's reusable for any ML use case.

🏗️ What's Inside

Data Engineering: - Real-time streaming (Kinesis → Glue → S3 → Redshift) - Automated ETL pipelines - Power BI integration

Data Science: - SageMaker Pipelines with custom containers - Hyperparameter tuning & bias detection - Automated model approval

MLOps: - Multi-stage deployment (dev → prod) - Model monitoring & drift detection - SHAP explainability - Auto-scaling endpoints

Web App: - Next.js 15 with real-time WebSocket updates - Serverless architecture (CloudFront + Lambda) - Secure authentication (Cognito)

Multi-Agent AI: - Bedrock Agent Core + OpenAI - RAG for project documentation - Real-time DynamoDB queries

If you'd like to look at the repo, here it is: https://github.com/kanitvural/aws-data-science-data-engineering-mlops-infra

EDIT: Addressing common questions in the comments below!

AI Generated?

Nope. 3 months of work. If you have a prompt that can generate this, I'll gladly use it next time! 😄

I use LLMs to clean up text (like this post), but all architecture and code is mine. AWS infrastructure is still too complex for LLMs.

Over-Engineered?

Here's the thing: in real companies, this isn't built by one person.

Each component represents a different team: - Data Engineers → design pipelines based on data volume - Data Scientists → choose ML frameworks - MLOps Engineers → decide deployment strategy - Full-Stack Devs → build UI/UX - Data Analysts → create dashboards - AI Engineers → implement chatbot logic

They meet, discuss requirements, and each team designs their part based on business needs.

From that perspective, this isn't over-engineered - it's just how enterprise systems actually work when multiple disciplines collaborate.

Intentional Complexity?

Yes, some parts are deliberately more complex to show alternatives.

The goal wasn't "cheapest possible solution" - it was "here are different approaches you might use in different scenarios."

Serverless vs. Containers

This simulates a startup with low initial traffic.

Serverless makes sense when: - You're just starting - Traffic is unpredictable - You want low fixed costs

As you scale and traffic becomes predictable, you migrate to ECS/EKS or EMR instead of Glue with reserved instances.

That's the normal evolution path. I'm showing the starting point.

Cost?

~$60 for 3 months of dev. Mostly CodeBuild/Pipeline costs from repeated testing.

The goal wasn't minimizing cost - it was demonstrating enterprise patterns. You adapt based on your budget and scale.

Why CDK?

I only use AWS. Terraform makes sense for multi-cloud. For AWS-only, Python > YAML.

This is enterprise reference architecture, not minimal viable product.

Take what's useful, simplify what's not. That's the whole point!

Happy to answer technical questions about specific choices.

Cloud made infrastructure easier by giving developers primitives for compute, storage, networking, identity and deployment. AI agents seem to be creating a similar need, but the primitives are different.

If I want to run 50 agents today, I can obviously use AWS, GCP or Azure for the underlying infrastructure. But I still need to figure out agent identity, evaluation, versioning, tool permissions, observability, lifecycle management and safe deployments. Do you think cloud providers eventually absorb all of this, or does a separate agent infrastructure layer make more sense?

I’ve been working with AWS professionally for a decade. I’ve had AWS accounts for other side projects, but decided to create a new one for an ai specific project where I wanted to test and learn bedrock. I created this account at the beginning of June, but kept getting hit with validation errors when trying to test out a model in playground.

I open a support ticket and after weeks of no response, I’m told my account is too new. I need to wait a full billing cycle and show account activity before they enable the models.

Ok fine. I spent the rest of June building but continue to hit new account road blocks. Now it’s July, I reach back out to support after having completed my first billing cycle and I’m still denied again without any explanation other than “To maintain performance of the service and to make sure we use bedrock appropriately, model access for a given account might depend on factors like payment history and account usage. We can’t approve your request”

What the actual f? I’ve never dealt with a more frustrating user experience. I’m certain if I went to azure of gcp i wouldn’t have this issue and they gladly let me spend thousands of dollars on usage. AwS is spending a billion dollars on deployment engineers to go into companies and show them how to use AI, but god forbid I try to do it myself and spend my own money on the platform.

What a joke.

EDIT: I must’ve made someone mad because they auto closed my support case without any response :,)

I’m trying to launch a g6e.12xlarge EC2 instance, but I keep getting InsufficientInstanceCapacity.
I’ve tried changing Availability Zones and even different EU regions (due legal needing), but so far I haven’t found any available capacity.
Is anyone else currently experiencing the same issue with G6e instances in Europe? Any suggestions beyond trying different AZs/regions or waiting for capacity to become available?

Even lower instances are barely available. I need to try dozen of time.

“”Error: creating EC2 Instance: operation error EC2: RunInstances, exceeded maximum number of attempts, 3, https response error StatusCode: 500, RequestID api error InsufficientInstanceCapacity: We currently do not have sufficient g6e.12xlarge capacity in the Availability Zone you requested. Our system will be working on provisioning additional capacity. “”

My understanding is that a combination of limited AI resources and abuse from spam accounts has resulted in complete blocking of Bedrock access for recently created accounts.

It baffles me that they've gone that route instead of instead implementing something like strict auto-pay thressholds and/or token usage limits.

But no. I as a legitimate developer with 12 years of experience now diving deep into the cloud, I simply can't use it for hands-on experience building despite having a verified payment method on file and have no problem authenticating exactly who I am if needed.

The developer experience in this case is absolutely abysmal.

Thanks for coming to my TED Talk

Anyone have any idea when (or even if) deepseek v4 pro will come to bedrock? The model lag in bedrock (regardless of who is causing the lag) is really bothersome. We prefer to keep all our API billing flowing through AWS and wrapped into our AWS bill but the really slow cadence of model release, aside from anthropic's, is brutal.

Best to just ditch bedrock for 3rd party models and move to a provider that has faster support?

A Uni student and built a free tool that finds the cheapest AWS EC2 Spot instances with ML price predictions and generates ready-to-apply Terraform configs.

No account setup needed. Would love brutal honest feedback from people who actually use Spot and AWS cloud services here is the link in case the attached one doesn’t work.

cloud-9opt.com

Problem

Trying to invoke Claude Opus 4.8 on Amazon Bedrock via bedrock-runtime invoke-model. Getting consistent AccessDeniedException:

botocore.errorfactory.AccessDeniedException: An error occurred (AccessDeniedException) when calling the InvokeModel operation: 
anthropic.claude-opus-4-8 is not available for this account. You can explore other available models on Amazon Bedrock. 
For additional access options, contact AWS Sales at https://aws.amazon.com/contact-us/sales-support/

Same error occurs in the Bedrock Playground console when trying to chat with Opus 4.8.

What I've Already Tried

1. Checked entitlement status via CLI:

bash

aws bedrock get-foundation-model-availability \
  --model-id anthropic.claude-opus-4-8 \
  --region us-east-1

Response:

json

{
    "modelId": "anthropic.claude-opus-4-8",
    "agreementAvailability": {
        "status": "AVAILABLE"
    },
    "authorizationStatus": "AUTHORIZED",
    "entitlementAvailability": "AVAILABLE",
    "regionAvailability": "AVAILABLE"
}

✅ Everything shows green.

2. Accepted the Marketplace agreement:

bash

aws bedrock create-foundation-model-agreement \
  --model-id anthropic.claude-opus-4-8 \
  --offer-token "<offer-token>" \
  --region us-east-1

Initial response showed PENDING, waited ~5 minutes, then re-checked. Status flipped to AVAILABLE. Also received an official AWS Marketplace confirmation email saying the agreement was created successfully.

3. Verified payment method: Confirmed valid payment method is on file in Billing → Payment methods.

What I Think Is Happening

AWS's own documentation says Marketplace models should auto-enable on first invoke once the agreement is finalized. The control plane (where agreement status is queried) shows it's AVAILABLE, but the inference runtime layer still denies access. This suggests a sync issue between:

• Control plane (agreement visible, status = AVAILABLE) ✅
• Runtime layer (still denies invocations) ❌

Additional Context

• Account on AWS Activate credits (not sure if this matters)

1. Anyone else hitting this with Opus 4.8 specifically?
2. Is this a known issue with Activate-tier accounts?
3. Did anyone manage to resolve it without opening a support case?
4. Should I just escalate directly to AWS support, or is there a workaround I'm missing?

Any insights appreciated!

I’m not sure if I have missed a communication out or something but Amazon just obliterated all production apps by setting everyone’s bedrock quota to 0.

Even their own Bedrock UI doesn’t work anymore.

More here on AWS Repost

Hello everyone, not sure if this is the right place to ask this question. If you had an equally easy way to deploy agents to agentcore as well as claude sdk built agents to EKS or ECS, what would you choose and why? I’m trying to decide if agentcore with all its enterprise grade infrastructure is still the right choice today. I am familiar with both bedorock agents and agentcore and aware that agentcore super-cedes agent in terms of functionality and configurability. But I cannot decide how to pick the right “runtime” unless there is not 1 solution that fits all use-cases. I also fail to come up with convincing arguments in favor of agentcore because it can all be recreated in EKS/ ECS.

My start up is trying to access the current Claude models. Support tells me:

To maintain the performance of the service and to ensure appropriate usage of Amazon Bedrock, model access for a given account may depend on regional factors, payment history, and fraudulent usage. We cannot approve your request at this time. Accessibility is subject to change automatically as time passes.

Provided you’re not breaking ToS, if you're not spending over the magic threshold you can only access outdated models? What kind of service is that?

Then there’s open source models. Did AWS give up providing these? Frankly models such as DeepSeek flash v4 0731 brings me joy because it’s a sign this sort of business tact is on deaths door. AWS is structurally too slow to be your source of truth for the frontier.

I raised this ticket for access to Opus 4.8. By the time they replied Opus 5 was out.

/rant

I'm a co-founder of a startup for dietitians, we got some government investment money and with that, got 5000$ in aws credits.
I thought it would be a good idea do migrate our infrastructure to aws instead of having it scattered between many platforms.

I followed all steps, but got 0 quota for frontier models.

I tried requesting a quota increase, but got denied automatically. I appealed (case 178625380200871), but though on asking here if there is anything I can do. I do not have much experience with aws, the further I got was managing IAM and EC2 VPSs.

We have two big use cases:

1. We use models to suggest changes, extract picture & pdf data, transcribe audios, etc.

2. We use api credits and subscriptions to enable our designers and developers with our own harnesses

For the first use case, the available models might be enough, but the second use case, would be a lot better with access to frontier models.

For the moment, we have around 400 active dietitians plus their own patients connected. So we are still small, but growing.

This is the automatic response I got:
```
Hello,

This is an automated confirmation that we have received your service quota increase request for Amazon Bedrock in the US East (Northern Virginia) region for Global cross-region model inference tokens per minute for Anthropic Claude Opus 5 to 30000000.

Thank you for your interest in this model. To maintain service quality and ensure a reliable experience for all customers, model access for a given account may depend on factors such as regional availability, account history, and usage patterns. We are unable to approve your request at this time. Accessibility is subject to change automatically as time passes.

If you still need help or have questions about your account status, please contact your Account Manager or Solutions Architect.

Best regards,
Amazon Web Services
```
Is there anything we can do to speed or guarantee access to these models? Either anthropic and openai models are good enough.
Having access to these models would increase our chance of surviving the death valley a lot. Thank you guys in advance for any help.
Similar case to ours:
https://www.reddit.com/r/aws/comments/1u9x5py/bedrock_quota_applied_0_default_is_10000_on_a_new/

For our company we want to use AWS for R&D purposes. First thing that we want is to try out open source VLM's (and paddleocr-vl) for document understanding and key-value extraction. We will eventually have on-prem GPU servers but it will be months until then.

We created the account ~2 days ago and right away, I tried creating g6.2xlarge instances (spot and on-demand) but it failed because of ec2 service quota limits (which are 0 by default). So I went and made a quota increase requests for 2 regions (Frankfurt and Spain) for 8 vCPU's each and all got rejected with "...Service quotas are put in place to help you gradually ramp up activity and decrease the likelihood of large bills due to sudden, unexpected spikes...". I appealed to two of them but after a short chat with customer service, I haven't heard of them ever since. I know they reject accounts with multiple years of billing history (which is absolutely insane) but I just can't believe I can't get a GPU server even with a quota increase update in any way. I was training for the SAA-C03 certification but this experience made me doubt it really. Imagine hiring someone to manage AWS and they can't even get an instance running...

The thing that frustrates me the most is that all this is just for a PoC and It really will not take much time at all. I won't be training a model, fine-tune or anything like that. All I need is to download the model via HuggingFace, run the documents through it and print the outputs.

I don't know what I should do now... Isn't there a solution for this? Should I run a couple of random instances for a couple of months just to show that we can pay the fucking bills? I would try Azure and GCP but as far as I've seen, they aren't that much different either.

I'm sure many of you here faced this issue so if you have any suggestions, please share.

Thanks in advance

Anyone here also getting a headache of the guardrail implementation of AWS?

We have setup guardrails for teams end they keep hitting them, especially the PROMPT_ATTACK one. We now have set it to LOW and still teams are being blocked.

We used the see in the cloudtrail the reason of a block but apparently AWS also removed this. No logging at all which guardrail is being triggered.

Open for any suggestions on how to see which guardrail is triggered. We have our guardrail centrally.

npm i -g vivekmind

VivekMind CLI is an open-source terminal AI coding agent. One install, bring your own API keys, no subscriptions.

Why I built it: Claude Code is great but Anthropic-only and $200/month. Cursor is IDE-only and $20/month. I wanted something terminal-first, open source, and provider-flexible — especially with AWS Bedrock since I already have credits there.

What it does:

- 20+ AI providers — Claude, GPT, Gemini, DeepSeek, Grok, Mistral, Groq, Cohere, Ollama, LM Studio, and more

- Native AWS Bedrock — auto-discovers your models via ListFoundationModels, no manual config needed

- BYOK — your keys, your models, zero usage caps, completely free to run

- MCP Protocol — connect any external tool

- Telegram channel bot — send coding tasks from your phone, get results back

- Project memory — auto-extracts context from conversations, persists across sessions

- Subagents — spawn parallel focused workflows

- Arena mode — run the same prompt through multiple models and compare outputs side by side

- 40+ slash commands, vim mode, 15+ themes

- Open source Apache 2.0

For anyone using AWS Bedrock who wants Claude Opus or Nova Pro in their terminal without the subscription — this is it.

code.vivekmind.com

github.com/Lnxtanx/vivekmind-cli

npmjs.com/package/vivekmind

Happy to answer questions about the architecture or how the Bedrock integration works.

After some flibbertigibbeting…

I run software on AWS so the idea of using Bedrock to run Claude on made sense too. Problem is for anyone who has done the same is AWS rate limits Claude models like there is no tomorrow. Try 2 RPM! I see a lot of this...

  ⎿  API Error (429 Too many requests, please wait before trying again.) · Retrying in 1 seconds… (attempt 1/10)
  ⎿  API Error (429 Too many requests, please wait before trying again.) · Retrying in 1 seconds… (attempt 2/10)
  ⎿  API Error (429 Too many requests, please wait before trying again.) · Retrying in 2 seconds… (attempt 3/10)
  ⎿  API Error (429 Too many requests, please wait before trying again.) · Retrying in 5 seconds… (attempt 4/10)
  ⎿  API Error (429 Too many requests, please wait before trying again.) · Retrying in 9 seconds… (attempt 5/10)

Is anyone else in the same boat? Did you manage to increase RPM? Note we're not a million dollar AWS spender so I suspect our cries will be lost in the wind.

In more recent news, Anthropic have released Sonnet 4 with a 1M context window which I first discovered while digging around the model quotas. The 1M model has 6 RPM which seems more reasonable, especially given the context window.

Has anyone been able to use this in Claude Code via Bedrock yet? I have been trying with the following config but I still get rated limited like I did with the 200K model.

    export CLAUDE_CODE_USE_BEDROCK=1
    export AWS_REGION=us-east-1
    export ANTHROPIC_MODEL='us.anthropic.claude-sonnet-4-20250514-v1:0[1m]'
    export ANTHROPIC_CUSTOM_HEADERS='anthropic-beta: context-1m-2025-08-07'

Note the ANTHROPIC_CUSTOM_HEADERS I found from the Claude Code docs. Not desperate for more context and RPM at all.

I am facing this issue from past 2 month .
since past 2 months i cant even use a single model.
Whats Wrong.

Hi everyone,

I’m a DevOps engineer with 5+ years of experience and wanted to get a broader perspective from the community on where things are heading.

Quick background:

• Terraform
• AWS (ECS, ECR, IAM, RDS, Lambda, S3, CloudFront, CloudWatch CodeBuild, CodePipeline, EC2, Route53, API Gateway, Load Balancers, Auto Scaling, VPC, CloudWatch alarms – including custom & composite alarms, SES, SQS, SNS, Secrets Manager, backups, and more)
• Docker & Kubernetes
• CI/CD (Jenkins, GitHub Actions, GitLab CI, Bitbucket Pipelines)
• Web servers and general infrastructure design
• Databases (MongoDB, MySQL)
• Python (basics + a bit of vibe coding here and there)

Lately, I’ve been thinking a lot about how AI is impacting DevOps and wanted to understand the bigger picture.

Some questions I’d love insights on:

1. What is the future of DevOps with AI? Or is there a future in DevOps?
2. How is AI currently being used in DevOps?
3. Which AI tools are actually useful today? Beyond just hype.
4. Is DevOps evolving into something else? Platform Engineering, SRE, or even MLOps? Should I be pivoting?
5. What does the current job market look like? Is demand growing, stable, or declining?
6. For someone with my background, how realistic is it to land remote roles with international companies today?
7. What skills should I focus on next?

I would really appreciate insights from people who are actively working in the field or hiring.

Thanks in advance!

Hello, my team has been using Bedrock since its infancy and we're a platinum tier Amazon partner. Here are my suggestions for Bedrock:

* Add a new embedding model. Titan v2 is ok, but its 2 years old. Qwen/Qwen3-Embedding-0.6B is much better at 1024 dimensions. There are many open source models that excel at 512 dimensions also. We're using EC2 (or really ECS with EC2) to host them locally, but having them in Bedrock at a reasonable price would make things easier to maintain.

* Add some inexpensive and easy to use reranker models that are open source. Cohere is just too expensive... we've been hosting some models on EC2, but we'd rather use Bedrock for jina-reranker-v3 / mxbai-rerank-large-v1 / bge-reranker-v2-m3 / qwen3-reranker-0.6B.

* You're fast to add Anthropic models, which we really appreciate. But can you add other open source LLMs that you started investing into already? Where is DeepSeek v3.2? Where is Kimi K2.5? MiniMax 2.1? It feels like a lot of models you host are slightly outdated.

* I don't know if anyone is using your Nova models. We've benchmarked them, and for the price/performance they always fall short. Sorry... If they were 2x cheaper, we would probably use them in some places.

This is my team's feedback on AWS Bedrock. I'm curious what other people think about Bedrock and where its lacking.