147.0K
DEFlask vs FastAPI
Same API. Same test. Why did Flask take 2× longer?
#python #fastapi #learnpython #pythonforbrginners #devwaymhaab
@devwaymahab
#Fastapi Vs Flask
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3.4K
WEBackend :
FastAPI: Used to build very fast and modern APIs in Python.
Flask: Used to build simple web apps or APIs in Python.
--------------------
Frontend :
Streamlit: Used to build quick UI apps for data and AI using Python.
@welcomeaiengineer
1.8K
LAFastAPI: Auto-Validate JSON POST Requests #programming
#python #coding
Let me try a new way of teaching you stuff in Python's Fast API. Here is how to handle JSON data in POST requests with FastAPI – using Pydantic BaseModel for automatic validation. Plus: Why FastAPI is exploding in popularity and overtaking Flask and Django for modern APIs. DB in a separate file included!
@laskentatechltd
506
TH💻Boost your API development with Flask and FastAPI!🧠 Learn how to Build ML Model Flask and Fast API to Deploy it in Production with endpoints and automatically generate API documentation.
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Follow @theaiinsider_
Follow @theaiinsider_
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Tags 🏷️
#deployment #modeldeployement #googlecloud #aws #azure #artificialintelligencemarketing #cloudcomputing #DataScience #MachineLearning #api #python #BeginnerFriendly #DeepLearning #DataScientist #DataAnalysis #ArtificialIntelligence #Amazon #elonmusk #Google #Microsoft #ChatGPT #GPT4 #flash #Memes #Datasciencememes #tech #instapic #datasciencejobs #flask #fastapi #api
@theaiinsider_
55.6K
EDYour vibe coding tool is likely to recommend Flask over FastAPI because it’s more popular and more flexible. But, FastAPI performs better and is much more well suited for production applications.
@edhonour
28.6K
DOYou can literally make your own.
All it takes is:
• A Twilio WhatsApp sandbox
• A FastAPI (or Flask) app in Python
• Redis for storage
• OpenAI for conversation
Hook it up with ngrok → done.
Jokes aside this is prob the fastest way I’ve ever turned a tweet into a working product.
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#fyp #tech #foryoupage #explore #automation
@dotslashsimran
35.7K
YEافضل باك اند فريمورك في بايثون .. FastAPI 🍏
🌟 في بايثزن هتلاقي فيه ٣ فريمورك مشهورين تقدر تبني بيهم باك اند .. Flask و Django و FastAPI.
🌟 بعد تجربتي لDjango حسيته معقد وكبير ومناسب اكتر للمشاريع الكبيرة .. وFlask عكسه ويفتقر لحاجات كتير اووي ومناسب لمشاريع تجريبية او صغيرة جدا .. لقيت بقى FastAPI محقق المعادلة المناسبة والمرونة اللي ممكن تحتاجها في اي مشروع .. وجربته واقدر ارشحه كباك اند فريمورك في بايثون.
ما تنسوا الLike والFollow ✌️🚀
وشوفوا قناة اليوتيوب عليها فيديوهات برمجية هتفيدكم ✌️🔴
@yehia.tech
5.5K
AF🐍 Day 48 – Simple Model Deployment Idea
“Your ML model can power real applications.” ⚡
Content:
• Wrap model inside an API
• Use Flask or FastAPI
• Apps send data → model returns prediction
• Used in production ML systems
Example Concept:
from flask import Flask, request
app = Flask(__name__)
@app.route(“/predict”)
def predict():
data = request.json
result = model.predict([data])
return {“prediction”: result.tolist()}
Pro Insight:
Deployment turns models into real products.
Save this and comment ML if you want to build real ML apps 💼🔥
@afterhours_rahmat
29.0K
HAFastapi?
@halltec
21.9K
PIEn este video te hablo de las funciones de dependencia para FastApi la cual permite reutilizar lógica común, como la validación de datos, autenticación, conexiones a bases de datos, o cualquier otra operación que necesites ejecutar antes de procesar una solicitud.
#programacion #python #fastapi
@pildoras_de_programacion
33.4K
DAIn my first years as a data scientist, I wasted hours on broken SQL, slow pandas scripts, messy Flask deployments, and “works on my machine” chaos.
These 4 tools fixed that:
• dbt → modular, documented SQL transformations
• Polars → faster, cleaner alternative to pandas
• FastAPI → quick, reliable model deployment
• Docker → consistent environments, no more deployment nightmares
If you’re just starting out, learning these early will save you months of frustration.
@data_pumpkin
6.8K
SHRead caption! 👇🏼
1. AI Engineer (Applied AI Engineer)
What you do
Build AI-powered features inside applications
Integrate ML models into web apps and APIs
Use LLMs, embeddings, RAG pipelines
Skills :
Python
ML frameworks (PyTorch, TensorFlow)
APIs with FastAPI or Flask
LLM tools like LangChain
Vector DBs like Pinecone
2. ML Engineer
What you do
Train models
Deploy them into scalable production systems
Optimize performance and pipelines
Skills :
Python + ML
Data pipelines
Model serving
Containers like Docker
Orchestration with Kubernetes
3. AI Backend Engineer
What you do
Build backend systems for AI apps
Create APIs for model inference
Build scalable pipelines
Tech:
Python / Node
FastAPI
Redis
PostgreSQL
queues like Apache Kafka
4. MLOps Engineer
What you do
Deploy ML models
Maintain training pipelines
Monitor models in production
Tools:
MLflow
Kubeflow
Docker
Kubernetes
6. AI Systems Engineer (Advanced / Research-level)
What you do
Work on large-scale ML infrastructure
Optimize model training / distributed compute
Concepts:
Distributed systems
GPUs
model parallelism
frameworks like Ray
Comment which role you think is best for you🙌🏻
#fypppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp #instagood #explorepage fyp #student
{student life, tips, helpful, AI roles, engineering students, skills, projects, consistency, content creation, discipline}
@sharanya_sukrutha
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