Data is the most valuable resource on the planet. We help businesses commercialize their data and harness the power of cloud platforms like TensorFlow and CloudAI.

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Data Engineering

A challenge for companies today is their inability to launch and commercialize data solutions in real business settings - and in time to have a meaningful reaction. This is in part due to the fact that today's businesses invest too heavily in data science on its own. Data sciences are centered around mathematics and data inference, which in the form of an employee, translates to skill sets centered on research, analysis, and advisory. Data scientists are important and they provide plenty of value, but they are not experts in translating their sciences into professionally architected software products.

Data engineering is concerned with the design, development, and maintenance of data processing systems with an emphasis on the factors that are critical in software engineering including security, reliability, ability to scale, and efficiency. Data engineers look first to what is practical. They identify what can be operationalized or deployed into a real business setting with measurable return on investment. Although data engineers are not classically trained in statistical modelling and data inference like data scientists are, there is an increasing number of open source products including TensorFlow and AutoML, that abstract away the data science and modelling, allowing the engineer to focus on actioning data insights.

Data Architecture

We help design the way in which information flows throughout a business by bridging the business/technology divide. This is comprised of understanding the business need for certain information, sharing opportunities that a businesses may not be aware of, and designing the technology to make the target information highly available and easily accessible. Designing and optimizing data architecture may entail:

  • Consolidating information from disparate sources to produce a comprehensive understanding of users or customers

  • Leveraging cloud products or developing new data pipelines in order to produce higher quality information or to reveal new patterns

  • Integrating data sets from third parties to improve the richness of information

  • Democratizing access to information within an organization, empowering decision makers

  • Securing and encrypting information to prevent unauthorized access or to comply with regulations

  • Developing data-driven software solutions such as ML or AI

Analytics

We help businesses develop rich data sources to feed their analytics solutions. The quality and comprehensiveness of the information source will directly impact the quality of the decisions made.

Applied Machine Learning (ML) & AI

Machine Learning and AI research are a trending focus in business right now. However there is a shortage of actual solutions and products that utilize real ML or AI. At it's core, ML/AI is only possible because of academia, research, and experimentation. However, for the first time in history there are open source technologies which abstract away the development of deep learning and neural networks, so that engineers can focus on operationalizing solutions. We help businesses apply ML and AI using:

  • TensorFlow

  • AutoML

  • DialogFlow