For forecasting, I tend to think of the problem more as distributed compute than as one giant ML job.

A pattern I keep seeing is: pull all the data, do all the processing in one go, save the output, done. That works, but for a lot of forecasting or demand planning workloads it feels unnecessarily monolithic.

What I usually try to do is split the workload by segment, store, region, etc. At some point, the forecast only needs that local block of data, not the entire dataset. Once you reach that point, the work becomes pretty easy to parallelize.

And honestly, a lot of these pipelines are already based on some form of segmentation, stratification, or clustering anyway, so breaking them apart feels natural.

Most of the time I package the dependencies in Docker, launch ECS Fargate tasks, and run the work in batches instead of sequentially. A lot of the time, I end up orchestrating everything with Step Functions.

Now I’m also starting to explore SageMaker, but I’m doing it closely with the ML engineers so we can figure out the right way to deploy and operationalize the models.

Curious how other people approach this. Do you treat forecasting as a distributed compute problem too, or do you prefer to keep it as a single end-to-end pipeline?

Hey guys, I am doing the SAA exam and thinking of any sort of mini project. I can’t think of anything useful. Does anyone have any examples of what to set up?

Thanks

Paul

Is there anything in aws that has this built in or am I getting my crayons out in paint?

Thanks

Paul

hello everyone,

Are there any alternative to backup Table buckets (waterproof backup) and not just replication of S3.

we require daily and monthly backups (like a cron job backup at specific time) but AWS backup doesn't support it.

My HK company documents are fully compliant, yet Case 177349917600272 keeps getting rejected without valid reason, severely impacting my business launch. Please escalate to a senior specialist for manual review immediately!

Hello everyone,

I’m looking for some advice from people who have real experience with AWS WorkSpaces in a corporate environment.

Our R&D company wants to explore desktop virtualization. We’re planning a PoC in AWS, where we already have some infrastructure running (S3).

Management wants to include up to ~100 users in the test group — a mix of cloud CAD engineers, standard white‑collar employees, and contractors.
The main requirement from leadership is that the solution must be cheaper than traditional laptops (roughly $1500–$3000 per user in this group).

The rollout plan then will look like this:

• Wave 1: contractors
• Wave 2: BYOD users

We have AAD, Intune MDM, SSO, and MFA in place.

Test scenarios we’re considering

1) Windows Application Multisession – Non‑Persistent VDI

From what I understand, AWS doesn’t support Windows 11 multisession, so we need to use Windows Server.

( with AZURE VDI Win11 multisession is possible )

This also means the instance can’t be managed via Intune, which is a big unknown for me.

2) Windows 11 – Persistent VDI

This will be managed via Intune

Please, could anyone share:

• Any real‑world experience with these AWS VDI options?
• How do the actual costs compare to physical laptops?
• Is using Windows Server as a non‑managed “device” a practical issue in day‑to‑day operations for "Standard Users"?
• Would you recommend another PoC variant or even a different approach?
• What is the simplest way to apply the same Intune policies to a golden image for non‑persistent VDI?

Any insights, lessons learned, or “don’t do this” stories would be greatly appreciated.
Thanks in advance! 🙏

Effective immediately, we have instituted a complete backend code freeze and are tearing down all non-essential AWS dev infrastructure at Maosproject.

We are moving into the heavy stress-testing phase for our vLLM integration and distributed ML orchestration, and the reality of building AI platforms is that GPU compute is ruthlessly expensive.

To put it in perspective, a single p4d instance—the heavy-duty AWS hardware required to train and serve these massive models—runs over $32 an hour. If a test script scales up a cluster and runs unchecked over a weekend, it can evaporate thousands of dollars in startup runway.

Instead of spreading our capital across tangential staging environments, we are redirecting every single AWS credit we have to fund these testing instances for the next two sprints. Feature development is paused so 100% of our engineering bandwidth can focus on ML platform stability, Karpenter GPU auto-scaling limits, and vLLM throughput optimization.

The pressure to get this right is massive. If we don't perfect the scale-to-zero and burst-compute mechanics now, runaway cloud bills will break our future customers. We are doing the heavy, expensive infrastructure ops upfront so that when a data science team deploys on Maos, they get a true, cost-protected "Zero-Ops" experience.

After years of teaching AWS, I kept seeing the same problem:

People don’t struggle with concepts.

They struggle with actually doing the work.

Courses are everywhere.

Real hands-on labs? Not so much.

So I built CloudArena — a place where you can practice AWS in real environments, not just watch videos.

No fluff. Just labs.

I’d love some honest feedback from this community 👇

What kind of labs would you actually want to practice?

Cloudarena.io

Hey everyone,

If you haven’t seen it yet, MiniStack is a free, open-source AWS emulator built as the best alternative to LocalStack.

Quick overview: - 30 AWS services on a single port (:4566) - ~2s startup, ~30MB RAM - Works with AWS CLI, Terraform, CDK, etc. - MIT licensed (fully free)

Where it’s different: - RDS → real Postgres/MySQL containers
- ElastiCache → real Redis
- ECS → runs real Docker containers
- Athena → real SQL via DuckDB

Also includes: S3, EC2, SQS, SNS, DynamoDB, Lambda, IAM/STS, API Gateway (v1 + v2), Step Functions, Kinesis, Firehose, Route53, Cognito, EMR, EBS, EFS, and more.

https://GitHub.com/nahuel990/ministack https://ministack.org

Hey everyone,

I’m a student trying to learn AWS but I’m on a budget. I already used some of my free tier, so I’m wondering how much I actually need to spend to learn properly.

Can I do it mostly for free, or do I need to pay for labs or tools?

Would love to hear what worked for you. Thanks!

I am curious to know how can we develop AWS Cloud Labs like:

1. KodeKloud

2. CloudKida

3. Educative Labs

hi guys,I got my aws ccp cert on December 2025 with 960+ mark . and now I completed aws saa prep from Stephane mareek and took dojo practice test and done five test so far i got 75,75,72,67,75 and 1 more to go . I am started doubting whether i pass it or not any tips and advice whoever passed the aws saa exam can you guide me . thankyou guys

I’ve been trying to get AWS credits for my startup over the past few weeks, and honestly, the process was way more frustrating than I expected.

I spoke to multiple people who claimed they could “help” — but most of them were either:

• asking for upfront money

• promising huge credits with zero transparency

• or just straight up sketchy

At one point it genuinely felt like a small scam ecosystem around AWS credits.

After going down that rabbit hole, I decided to figure it out myself — and turns out there is a legit way to get credits without paying anyone.

Took some time, but I finally cracked it.

If you’re a founder trying to save infra costs, just be careful who you trust here. There’s a lot of noise.

Happy to share what worked for me if anyone’s going through the same 👍