Our process is simple
~h | Initial callAcquire information about your problem.
~w | Data Inspection/ProposalExplore your data and make a strategy proposal.
~m | Dev/Training/DeploymentStart working on a solution that will capture the data you need, train the appropriate algorithms and monitor their performance with you. This ends with the presentation of the project results.
∞ | SupportDelivery of the data and model artifacts, supplemented by dashboards and visualizations
Data Systems
A data-first approach is key.
We specialize in building Directed Acyclic
Graph (DAG) parallel orchestration systems that are able to scale to
millions of tasks.
Collecting, storing, and transforming data, and then training models on it is a complex task
that requires flexibility.
We take advantage of the variety of different machines available on the cloud. From horizontal
scaling of simple small tasks to large HPC machines and GPU-accelerated machines, we can
orchestrate them all.
Data Analysis & Research
Navigating through raw data can be a daunting task. Often cluttered with errors, missing values,
and outliers, such data can skew results and lead to
inaccurate conclusions, especially for machine learning
applications.
Data must be transformed and engineered in such a way that ML models can extract
the latent value embedded within.
We extract insights from your data with techniques like EDA, outlier
detection, and feature engineering,
preparing the way for ML models.
Machine Learning & Artificial Intelligence
The ability to predict the future from the past is a powerful tool.
Even if you have amazing data, you still
need to select the appropriate loss functions, metrics, and fit models that will generalize out-of-sample.
Overfitting is a common and extremely dangerous problem in modern machine learning.
We can either build you a fully
custom neural network architecture or leverage the tried and
tested existing models.
Visualizations & Dashboards
Visualizations are a powerful tool for uncovering patterns and insights that might be hidden in
raw data. By transforming complex data into visual formats, we make it easier to understand and
analyze.
Whether it's plots or connected graphs, our visualizations help you see the big picture and make
informed decisions.
Our dashboards feature tailored interfaces and real-time
updates, allowing you to efficiently monitor key performance indicators and
metrics.
Financial Machine Learning
Modern day finance is a data-driven field.
Known for its large datasets, high dimensionality and low signal-to-noise ratio, financial data is a perfect candidate for machine learning.
A well operating Financial Machine Learning data system will collect data from various sources, clean and preprocess it, build features, and train models to forecast future metrics or find hidden risk clusters.
Our systems can do all that, and let you focus on your idea.
Known for its large datasets, high dimensionality and low signal-to-noise ratio, financial data is a perfect candidate for machine learning.
A well operating Financial Machine Learning data system will collect data from various sources, clean and preprocess it, build features, and train models to forecast future metrics or find hidden risk clusters.
Our systems can do all that, and let you focus on your idea.
Tech Stack
We primarily use Python, but for performance-critical tasks, we can utilize C/C++. Hopefully,
Mojo will save us all.
On the modeling side, our toolkit includes scikit-learn, statsmodels, tensorflow, pytorch,
keras, xgboost, lightgbm, huggingface, SHAP,
and Optuna.
