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Python es el lenguaje universal para ciencia de datos e inteligencia artificial. Con su sintaxis clara, ecosistema inigualable y portabilidad multiplataforma, aceleramos desde prototipos en notebooks hasta modelos productivos y microservicios de inferencia a escala.
¿Por qué Python lidera la IA?
Ecosistema ML
TensorFlow, PyTorch, scikit‑learn, JAX, Hugging Face.
Prototipado veloz
Notebooks interactivos, visualización Matplotlib/Plotly.
Rendimiento ampliable
CUDA, XLA, Tensor RT y compilación Numba.
DevOps & nube
FastAPI, Docker y CI/CD para MLOps.
Librerías y frameworks clave
| Categoría | Herramientas | Uso principal |
|---|---|---|
| Deep Learning | TensorFlow • PyTorch • JAX | GPU/TPU training |
| ML clásico | scikit‑learn • XGBoost | Modelos tabulares |
| Datos | pandas • Polars • Dask | ETL & wrangling |
| Serving | FastAPI • BentoML | Inference APIs |
| MLOps | MLflow • Kubeflow | Tracking & pipelines |
Workflow desde notebook a producción
1. Exploración & EDA
Jupyter + pandas‑profiling.
2. Feature engineering
pipelines scikit‑learn y validación cruzada.
3. Entrenamiento
GPU clusters / Ray distributed.
4. Registro & pruebas
MLflow + pytest.
5. Despliegue CI/CD
Docker + K8s + Terraform.
Casos de uso destacados
NLP & chatbots
Visión por computadora
Predicción de demanda
Diagnóstico médico IA
¿Por qué Itrion con Python?
Plantillas FastAPI + CI/CD listas
Optimización CUDA & TensorRT
Auditoría ética de IA
MLOps gestionado 24/7
Experiencia de Itrion
1.5 PB
Procesados con pipelines Python
300+
Modelos IA en producción
10 años
Desarrollando con Python
Buenas prácticas Python IA
- • Fija versiones en
pyproject.toml. - • Usa
venvopoetrypara entornos aislados. - • Serializa modelos con formatos portables (ONNX).
- • Añade tests unitarios y de rendimiento.
- • Documenta API con OpenAPI/Swagger.
Python is the universal language for data science and artificial intelligence. With its clear syntax, unmatched ecosystem, and cross-platform portability, we accelerate from prototypes in notebooks to production models and scalable inference microservices.
Why Python leads AI
ML ecosystem
TensorFlow, PyTorch, scikit‑learn, JAX, Hugging Face.
Fast prototyping
Interactive notebooks, Matplotlib/Plotly visualization.
Scalable performance
CUDA, XLA, Tensor RT, and Numba compilation.
DevOps & cloud
FastAPI, Docker, and CI/CD for MLOps.
Key libraries and frameworks
| Category | Tools | Main use |
|---|---|---|
| Deep Learning | TensorFlow • PyTorch • JAX | GPU/TPU training |
| Classical ML | scikit‑learn • XGBoost | Tabular models |
| Data | pandas • Polars • Dask | ETL & wrangling |
| Serving | FastAPI • BentoML | Inference APIs |
| MLOps | MLflow • Kubeflow | Tracking & pipelines |
Workflow from notebook to production
1. Exploration & EDA
Jupyter + pandas‑profiling.
2. Feature engineering
scikit‑learn pipelines and cross-validation.
3. Training
GPU clusters / Ray distributed.
4. Logging & testing
MLflow + pytest.
5. CI/CD deployment
Docker + K8s + Terraform.
Featured use cases
NLP & chatbots
Computer vision
Demand prediction
AI medical diagnosis
Why Itrion with Python?
FastAPI + CI/CD templates ready
CUDA & TensorRT optimization
Ethical AI auditing
Managed MLOps 24/7
Itrion Experience
1.5 PB
Processed with Python pipelines
300+
AI models in production
10 years
Developing with Python
Python AI best practices
- • Pin versions in
pyproject.toml. - • Use
venvorpoetryfor isolated environments. - • Serialize models with portable formats (ONNX).
- • Add unit and performance tests.
- • Document API with OpenAPI/Swagger.
Python es el lenguaje universal para ciencia de datos e inteligencia artificial. Con su sintaxis clara, ecosistema inigualable y portabilidad multiplataforma, aceleramos desde prototipos en notebooks hasta modelos productivos y microservicios de inferencia a escala.
¿Por qué Python lidera la IA?
Ecosistema ML
TensorFlow, PyTorch, scikit‑learn, JAX, Hugging Face.
Prototipado veloz
Notebooks interactivos, visualización Matplotlib/Plotly.
Rendimiento ampliable
CUDA, XLA, Tensor RT y compilación Numba.
DevOps & nube
FastAPI, Docker y CI/CD para MLOps.
Librerías y frameworks clave
| Categoría | Herramientas | Uso principal |
|---|---|---|
| Deep Learning | TensorFlow • PyTorch • JAX | GPU/TPU training |
| ML clásico | scikit‑learn • XGBoost | Modelos tabulares |
| Datos | pandas • Polars • Dask | ETL & wrangling |
| Serving | FastAPI • BentoML | Inference APIs |
| MLOps | MLflow • Kubeflow | Tracking & pipelines |
Workflow desde notebook a producción
1. Exploración & EDA
Jupyter + pandas‑profiling.
2. Feature engineering
pipelines scikit‑learn y validación cruzada.
3. Entrenamiento
GPU clusters / Ray distributed.
4. Registro & pruebas
MLflow + pytest.
5. Despliegue CI/CD
Docker + K8s + Terraform.
Casos de uso destacados
NLP & chatbots
Visión por computadora
Predicción de demanda
Diagnóstico médico IA
¿Por qué Itrion con Python?
Plantillas FastAPI + CI/CD listas
Optimización CUDA & TensorRT
Auditoría ética de IA
MLOps gestionado 24/7
Experiencia de Itrion
1.5 PB
Procesados con pipelines Python
300+
Modelos IA en producción
10 años
Desarrollando con Python
Buenas prácticas Python IA
- • Fija versiones en
pyproject.toml. - • Usa
venvopoetrypara entornos aislados. - • Serializa modelos con formatos portables (ONNX).
- • Añade tests unitarios y de rendimiento.
- • Documenta API con OpenAPI/Swagger.
Python is the universal language for data science and artificial intelligence. With its clear syntax, unmatched ecosystem, and cross-platform portability, we accelerate from prototypes in notebooks to production models and scalable inference microservices.
Why Python leads AI
ML ecosystem
TensorFlow, PyTorch, scikit‑learn, JAX, Hugging Face.
Fast prototyping
Interactive notebooks, Matplotlib/Plotly visualization.
Scalable performance
CUDA, XLA, Tensor RT, and Numba compilation.
DevOps & cloud
FastAPI, Docker, and CI/CD for MLOps.
Key libraries and frameworks
| Category | Tools | Main use |
|---|---|---|
| Deep Learning | TensorFlow • PyTorch • JAX | GPU/TPU training |
| Classical ML | scikit‑learn • XGBoost | Tabular models |
| Data | pandas • Polars • Dask | ETL & wrangling |
| Serving | FastAPI • BentoML | Inference APIs |
| MLOps | MLflow • Kubeflow | Tracking & pipelines |
Workflow from notebook to production
1. Exploration & EDA
Jupyter + pandas‑profiling.
2. Feature engineering
scikit‑learn pipelines and cross-validation.
3. Training
GPU clusters / Ray distributed.
4. Logging & testing
MLflow + pytest.
5. CI/CD deployment
Docker + K8s + Terraform.
Featured use cases
NLP & chatbots
Computer vision
Demand prediction
AI medical diagnosis
Why Itrion with Python?
FastAPI + CI/CD templates ready
CUDA & TensorRT optimization
Ethical AI auditing
Managed MLOps 24/7
Itrion Experience
1.5 PB
Processed with Python pipelines
300+
AI models in production
10 years
Developing with Python
Python AI best practices
- • Pin versions in
pyproject.toml. - • Use
venvorpoetryfor isolated environments. - • Serialize models with portable formats (ONNX).
- • Add unit and performance tests.
- • Document API with OpenAPI/Swagger.
At Itrion, we provide direct, professional communication aligned with the objectives of each organisation. We diligently address all requests for information, evaluation, or collaboration that we receive, analysing each case with the seriousness it deserves.
If you wish to present us with a project, evaluate a potential solution, or simply gain a qualified insight into a technological or business challenge, we will be delighted to assist you. Your enquiry will be handled with the utmost care by our team.
At Itrion, we provide direct, professional communication aligned with the objectives of each organisation. We diligently address all requests for information, evaluation, or collaboration that we receive, analysing each case with the seriousness it deserves.
If you wish to present us with a project, evaluate a potential solution, or simply gain a qualified insight into a technological or business challenge, we will be delighted to assist you. Your enquiry will be handled with the utmost care by our team.