Transforming your organization to become data-driven —that is, to make business decisions based on data— is a challenging endeavor.
You struggle to govern, manage, and monitor existing architectures and databases accurately.
You can’t quantify how your machine learning model impacts your business’s KPIs.
Your resources are more limited than those of tech giants, so every decision requires more attention and planning, which can be paralyzing.
With new technologies emerging daily, it’s challenging to distinguish between necessity and FOMO (Fear of Missing Out).
Manage the data your business needs by designing customized solutions in cloud environments.
Learn to use the technological stack that your project requires with concrete examples from your business and industry.
Unlock the communication potential of different areas, breaking data silos to get transparency and clarity about the information available throughout the company.
Identify your organization's real needs and get a clear roadmap for the processes, people, and technologies involved.
Understand the real relation and impact of metrics from a machine learning model on your business.
Design cost-efficient solutions in terms of simplicity and automation.
At deployr, we take the time to listen and understand what your business needs.
With that information, we conduct a thorough diagnosis of the resources you have and what you want to achieve.
Only then, we propose a work plan with the most functional and concrete deliverables you’ve seen to date.
Ideal for organizations that have data but struggle to take the first step in advanced analytics, either due to data disorder or not knowing how and where machine learning can add value to the business.
The aim here is to help advanced analytics teams deploy their first models to production and incorporate machine learning engineering principles for effective business opportunities.
Designed for more mature data adoption organizations, the goal is to complete process automation, incorporating all stages of the MLOps framework.
Working with data to achieve your goals involves doing it on a reasonable scale and according to your organization’s current reality. Our customized solutions will allow you to reduce the costs and execution times of your data projects, guided by professionals with real experience implementing machine learning models.
Reduce the cost and execution time of your data projects with the guidance of professionals with real experience in adopting the necessary technologies for deployment of machine learning models.
A podcast delving into the realms of data, algorithms, and how we’ve shaped today into the world of tomorrow.
Exclusively in Spanish!
Your host: Hernán Escudero.
For companies that need to design and consolidate a data architecture.
deployr foundations focuses on building a data lake/data warehouse and training in its use and exploitation using BI tools.
Ideal for organizations that have data but struggle to take the first step in advanced analytics, either due to data disorder or not knowing how and where machine learning can add value to the business.
The deployr science approach focuses on designing business-oriented machine learning projects, involving data discovery as a crucial part of the process.
The Models approach aims to help advanced analytics teams deploy their first models to production and incorporate machine learning engineering principles for effective business opportunities.
deployr models focuses on the productive deployment of machine learning models and the adoption of best software engineering practices for a scalable, secure, and efficient solution.
The Pipelines approach is designed for more mature data adoption organizations: it seeks complete process automation, incorporating all stages of the MLOps framework.
deployr pipelines focuses on implementing CI/CD/CT practices to bring analytics and machine learning processes to their most professional level.
Para aquellas empresas que necesitan diseñar y consolidar una arquitectura de datos.
deployr foundations se centra en la construcción de un data lake / data warehouse y en la capacitación en su uso y explotación mediante herramientas de BI.