Python has established itself as the world's leading language for data science and machine learning. At PXL, AI development is as much about solid system architecture as it is about algorithms. We build scalable solutions that transform your company's data into decision-making foundations, automation, and new business opportunities.
We differ from pure analytics agencies by being system developers at our core. A machine learning model provides no value if it lives in isolation within a Jupyter Notebook. We take models into production, wrap them in robust APIs, and integrate them seamlessly with your existing business systems.
The Toolbox: Right Tool for the Right Job
In machine learning, there is no "one-size-fits-all". Technology choice depends on data type (structured vs. unstructured), precision requirements, and the need for explainability. We have deep expertise in the Python ecosystem and select libraries based on technical specifications.
Structured Data and Scikit-Learn
For classical machine learning on tabular data (Excel sheets, SQL databases, CSV files), Scikit-Learn is our primary workhorse. This library gives us access to efficient algorithms for:
Regression: For calculating continuous values (e.g., property prices or energy consumption).
Classification: For sorting data into categories (e.g., "churn" vs "loyal customer").
Clustering: For finding natural groupings in customer data without predefined answers.
The strength of these models lies in their speed and interpretability. We can often explain exactly which variables are weighted highest, which is critical for auditability.
Deep Learning with TensorFlow and PyTorch
When data is unstructured – like text, images, or audio – deep neural networks are required. Here we use TensorFlow or PyTorch. These are the same frameworks that power technology at companies like Google and Meta.
We build and train models that can "see" and "read". These are computationally intensive processes where we optimize code for running on GPU clusters, while ensuring that inference (the actual use of the model) is fast enough for real-time applications.
MLOps: Operating and Maintaining Models
A model is perishable. Changes in the market or user behavior can lead to "model drift", where precision decreases over time. As a development house focused on DevOps, we treat machine learning as part of the CI/CD pipeline (MLOps).
Versioning: We track code, data, and model parameters so that results can always be reproduced.
Monitoring: We set up dashboards that alert if the model's accuracy falls below a critical threshold.
Retraining: We automate processes to retrain models when new data becomes available.
Local Language Models and Privacy
Although services like OpenAI offer powerful APIs, there are many cases where sensitive data cannot leave company control. We specialize in setting up and running open language models (like Llama 3, Mistral, or Gemma) locally in your infrastructure.
Using tools like Ollama and dedicated inference hardware, we can provide AI functionality such as chat assistants or document analysis completely "offline". This ensures GDPR compliance, eliminates per-token licensing costs, and gives us the ability to fine-tune models specifically on your domain terminology and data.
From Hypothesis to Production
Our methodology for AI projects follows a structured, scientific approach:
Data Analysis (EDA): We always start by validating the quality of your data. Is there enough history? Is the data clean enough?
Model Development: We test multiple algorithms against each other to find the one that provides the best balance between precision and performance.
Integration: The model is wrapped in a microservice (usually via FastAPI) and deployed to a test environment.
Production Deployment: The solution is rolled out with full monitoring, scaled to handle real traffic.
We don't sell dreams about what AI can do in the future. We deliver systems that use mathematics and data to solve the problems you have today.