Julia
High-performance language for scientific computing
Machine Learning / AI
Artificial intelligence and machine learning
Pros and Cons
Ventajas
- + Rapidly expanding field
- + Very competitive salaries
- + Innovative applications
Desventajas
- - Requires advanced mathematics
- - Needs a lot of data and compute
- - Models difficult to debug
Recommended Languages
8 popular languages in Machine Learning / AI
Lisp
Historic language family with parenthesis notation
MATLAB
Proprietary language for numerical computing and simulation
Mojo
Modular's language combining Python with systems performance
Prolog
Logic programming language for AI and expert systems
Python
Versatile language for web, data and AI with clear syntax
R
Specialized language for statistics and data science
Scala
Functional and OO language for JVM
Frameworks and Tools
12 popular frameworks
BentoML
Simplified model serving framework
FastAPI
Modern, fast Python framework with types
Gradio
Framework to create user interfaces for ML models
Kedro
ML pipeline framework with engineering best practices
LangChain
Framework for applications with LLMs
MLflow
Open-source platform for ML lifecycle
NVIDIA Omniverse
3D simulation and collaboration platform
OpenCV
Computer vision and image processing library
PyTorch
Deep learning framework preferred in research
Ray
Distributed computing framework for ML and Python
Streamlit
Python framework to quickly create data science apps
TensorFlow
Google's ML framework for production