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  3. Deepdesk
Deepdesk

Deepdesk

www.deepdesk.com

AI tools for the enterprise

45tools
5decisions
14followers
OverviewTech Stack45Dev Feed

Tech Stack

View all 45
Stack by Layer
AI7
Application & Data22
Utilities4
DevOps10
Business Tools2
AI
7 tools (16%)
Application & Data
22 tools (49%)
Utilities
4 tools (9%)
DevOps
10 tools (22%)
Business Tools
2 tools (4%)

AI

7
scikit-learnTensorFlowSciPyNumPyNVIDIA Deep Learning AMIKubeflowPyTorch

Application & Data

22
Flux CDGoogle Cloud Pub/SubGoogle Cloud PlatformHTML5JavaScripthtmlGoogle Cloud FunctionsGoogle Cloud StorageGoogle BigQueryFastAPITypeScriptRedisKubernetesDjangoFlaggerGoogle Cloud SQL for PostgreSQLFlaskDockerPythonGoogle DriveGNU BashBeautifulSoup

Utilities

4
CeleryRabbitMQIstioSlack

DevOps

10
PrometheusGrafanaTerraformGoogle Cloud BuildCircleCILinearVisual Studio CodePyCharmGitGitHub

Business Tools

2
ReactWhimsical Wireframes

Latest from Engineering

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Lukas Batteau
Lukas Batteau

CTO at Deepdesk

Feb 4, 2023

ReviewonFastAPIFastAPI

If you want framework-agnostic code, Django is really not the right framework for you. Its large ORM layer, heavy use of middleware, and reliance on class inheritance, will make migrating to another framework very painful. For more freedom and control I would recommend Flask, or even better FastAPI.

At the same time, it seems DDD has nothing to do with using other libraries or not.

Either way, whatever framework you choose, remember the golden rule: Don’t Fight The Framework.

5.52k views5.52k
Comments
Lukas Batteau
Lukas Batteau

CTO at Deepdesk

Jul 1, 2021

ReviewonFastAPIFastAPIFlaskFlask

Since the course is in Python, a Python web framework seems like a logical choice. Django is a good option, but Flask and FastAPI are far more lightweight than Django, and great for quickly adding an API on top of your ML/NLP code. Django has its merits, especially the generated admin interface, but requires a lot of boilerplate code.

81k views81k
Comments
Lukas Batteau
Lukas Batteau

CTO at Deepdesk

Jun 2, 2021

DecidedonGoogle Cloud PlatformGoogle Cloud PlatformHerokuHeroku

Heroku worked great for us, although it became pricy as soon as traffic picked up. Before long we needed more control over autoscaling and health checks, as our sizeable machine learning models took a while to load. That, coupled with the desire to break our monolith up into smaller microservices, led to us migrating to Google Cloud Platform, specifically Google Kubernetes Engine. Coming from Heroku, this was a huge step, so be advised you only want to do this if you have enough staff with the right expertise. Heroku may become pricy, just remember to compare this with fulltime engineering salaries.

14.3k views14.3k
Comments
Lukas Batteau
Lukas Batteau

CTO at Deepdesk

Jun 2, 2021

DecidedonTerraformTerraformGoogle Cloud Deployment ManagerGoogle Cloud Deployment Manager

After working with Deployment Manager for a while, we discovered it to be lacking in many areas, where Terraform, surprisingly, is better maintained, has way better documentation, and keeps up with the rapid Cloud API developments.

1.91k views1.91k
Comments

Team on StackShare

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Lukas Batteau
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