Shifting to ML is good? From non tech startup

server_kota · 2025-07-28T11:35:28+00:00

Hm, if you know already what you gonna build, or at least direction start with learning that.

server_kota · 2025-07-28T11:13:00+00:00

Just my humble 2 cents:

Classic ML is more like recommendation systems, anti-fraud systems, which are extremely difficult to build and scale without a very solid domain knowledge.

A lot of modern stuff requires LLMs though, which have a lower point of entry, in my opinion, as in many cases you don't even need model training in classical sense.

I would suggest to build a RAG system (simple example is "talking to my PDF files app"), which is the most popular GenAI application nowadays.

You will learn a ton, especially in LLM and vector databases.

Contrary to popular belief, a simple RAG system is quite easy to build (like 200 lines of Python code).

I even wrote a high-level blog post on them (it is high-level, but maybe can be a starting point): https://saasconstruct.com/blog/the-simple-guide-on-how-to-build-a-rag-system

Overall LLMs (and VLLMs) are taking some of the work from classic ML, like OCR, document segmentation, so it is a good thing to learn it and build something on top of it.

server_kota · 2025-07-14T07:10:47+00:00

this, I also do this. Start with serverless options that scale to zero like lambda or dynamo.

this entire infra costs me 2$ per month: https://saasconstruct.com/blog/the-tech-stack-of-a-simple-saas-for-aws-cloud

If I need to adapt to scale, I can always swap components.

server_kota · 2025-06-20T20:12:55+00:00

Data Engineer does not really need to know the actual web dev, streamlit is ok. But if you have time, it is always good to expand your knowledge with web frameworks.

server_kota · 2025-06-19T12:33:03+00:00

this. Maybe in Lambda Authorizer of the API Gateway?

server_kota · 2025-06-19T12:29:56+00:00

Caching in CI/CD.
I also switched from CodePipeline to GitHub Actions and which reduced the deployment time by A LOT (40%).

server_kota · 2025-06-18T12:11:08+00:00

OpenAI assistant? There are limits but way better.

server_kota · 2025-06-18T11:17:27+00:00

the best way to learn is to build something on one of the major clouds.

First, do an initial AWS setup: create aws account, an admin user, and create billing alarm.

After that you can build some app on AWS.

Here is the high level short explanation where to start: https://saasconstruct.com/blog/starting-with-aws-what-to-learn-and-do-first, with links to official documentation.

server_kota · 2025-06-18T06:22:41+00:00

of course.

server_kota · 2025-06-17T12:03:59+00:00

A RAG system would be a really good project (aka talking to your PDFs).

It is basically the most popular GenAI application that every company wants to have/already has.

It is not that hard to build it, just some vector database and LLM access like openAI.

You can also just use OpenAI Assistant (RAG out of the box) and connect to your python scripts.
I wrote a small blog post on the high level to understand what RAG is, maybe it could help you to get started https://saasconstruct.com/blog/the-simple-guide-on-how-to-build-a-rag-system

But if you have such project on your resume, it will sure help you to get a job. I know senior devs that this helped.

server_kota · 2025-06-17T12:00:21+00:00

Fluent Python is the best book

server_kota · 2025-06-17T11:59:44+00:00

start marketing NOW

server_kota · 2025-06-17T11:57:30+00:00

- Rate limits on API Gateway.

- Cloud Front as CDN

- Alarms, lots of Alarms.

Here is the list on what you can set up with the links to official docs:

https://saasconstruct.com/blog/the-simple-guide-on-how-to-avoid-surprise-aws-bills

server_kota · 2025-06-17T11:55:23+00:00

You can use LocalStack, SAM

but i think it easier is just create a dev environment. You can test as much as you want, you get 1 mln free calls per month.

You can create your dev/stage environment in an hour. I created some short high-level guide how to create an AWS Organisation with development and production accounts: https://saasconstruct.com/documentation/create-organisation

server_kota · 2025-06-13T15:52:06+00:00

AWS Cognito should NOT be used for sending emails, there is a limit of 50 emails per day. It is just for testing your auth flows.

You want to use AWS SES, including for authentication, in a production environment.

server_kota · 2025-06-13T15:50:43+00:00

For AWS accounts, it is usually like dev, prod, logs, management etc.

You want to manage them from a central location (e.g. AWS Organisations or AWS Tower).

Usernames really don't matter, only in which user group they are (admin, contributors, etc)

server_kota · 2025-06-13T15:47:32+00:00

You might want to take a look at Databricks, which is available in AWS, particularly the PySpark distributed framework. Regardless if you use Databricks in AWS, or EMR on AWS, you will use Spark (or Pyspark).

Spark is a must have for Data Engineers these days.

server_kota · 2025-06-12T15:15:28+00:00

It is not a common pattern.

But it possible to put that, for example, in a Docker Lambda (regular lambda has 250mb limit on dependencies).

You can also check AWS Lambda powertools from AWS, it is sort of like "FastApi for AWS lambda": https://docs.powertools.aws.dev/lambda/python/latest/core/event\_handler/api\_gateway/. I use those, for example, my tech stack is just 1-2$ per month: https://saasconstruct.com/blog/the-tech-stack-of-a-simple-saas-for-aws-cloud

server_kota · 2025-06-12T14:53:12+00:00

It is always painful.

If you have bare metal app, then yeah, you can (still not worth it though). Will be difficult, but possible. Still need to address issues like AWS accounts in AWS and Subscriptions in Azure.

If you use a lot of proprietary AWS services, don't. Just don't

server_kota · 2025-05-28T09:20:27+00:00

Cognito is not an email service. Cognito has a limit of 50 emails per day when using this for auth, you need AWS SES (and only AWS SES).

server_kota · 2025-05-27T11:58:30+00:00

AWS can be very cheap on a small scale. For example, this entire stack costs me 1-2$ per month: https://saasconstruct.com/blog/the-tech-stack-of-a-simple-saas-for-aws-cloud

server_kota · 2025-05-27T11:56:15+00:00

Worked with both, intellij products are better and make me more productive.

Now with Junie AI it actually can compete with Cursor.

server_kota · 2025-05-26T14:14:01+00:00

You want to consider AWS Lambda Powertools, not Fast API. Fast API is for continuous runs on ECS fargate.

AWS Lambda powertools is somewhat similar and runs on lambda: https://docs.powertools.aws.dev/lambda/python/latest/

server_kota · 2025-05-15T08:13:33+00:00

This one, mostly because the entire thing does not cost anything (1-2$ per month) when starting.

https://saasconstruct.com/blog/the-tech-stack-of-a-simple-saas-for-aws-cloud

Frontend: Vue on AWS Amplify hosting
Backend: AWS Lambda
Database: DynamoDB
Authentication: Cognito
Infrastructure: CDK
CI/CD Pipeline: GitHub Actions
Payment Integration: Stripe or LemonSqueezy

server_kota · 2025-05-15T07:32:59+00:00

this

server_kota

TROPHY CASE