AI & ML

The benefits that all companies can receive from the power of AI / ML will change the landscape of modern business forever.

An emerging set of digital data-ecosystems will account for nearly $60 Trillion in spend over the next five years.

Machine learning is the science of enabling computers to predict outcomes from inputs without being explicitly programmed. This is done through the design and creation of computational algorithms that are taught to “think” through the modeling and input of training data sets while leveraging pattern recognition, association, and classification to build an implicit understanding of the data it consumes. Once an algorithm is trained or taught to behave in a certain way, it can begin to produce predictions faster and more accurately than humans.

The program can then proceed to act autonomously or be programmed to make predictions upon certain events occurring. To learn more about AI and ML, see our What is Machine Learning blog series.

AI & ML

The benefits that all companies can receive from the power of AI / ML will change the landscape of modern business forever.

An emerging set of digital data-ecosystems will account for nearly $60 Trillion in spend over the next five years.

Machine learning is the science of enabling computers to predict outcomes from inputs without being explicitly programmed. This is done through the design and creation of computational algorithms that are taught to “think” through the modeling and input of training data sets while leveraging pattern recognition, association, and classification to build an implicit understanding of the data it consumes. Once an algorithm is trained or taught to behave in a certain way, it can begin to produce predictions faster and more accurately than humans.

The program can then proceed to act autonomously or be programmed to make predictions upon certain events occurring. To learn more about AI and ML, see our What is Machine Learning blog series.

Attacking Big Data Problems

With an automated ML solution in place, organizations can take advantage of superior data utilization strategies, unlocking powerful insights that can bring forward a myriad of critical information needed by business stakeholders.

This includes trends and benchmarking, customer retention and churn reports, revenue and cost forecasting, information security vulnerabilities, operational inefficiencies, market opportunities and so much more. Machine Learning can and will create the business observability and automation needed by modern organizations.

The Untitled Approach.

One crucial fact remains: without proper information architecture, scalable/elastic technology system architecture and good data hygiene processes, it is very impractical and perhaps harmful, to attempt Machine Learning strategies within your business.

Every business needs to proceed through certain milestones in order to leverage Machine Learning in a meaningful way. Typically speaking, and for the best results, a centralized data warehouse and robust ETL capabilities would be a base requirement. However, given the rate of technological diffusion and the amazing off-the-shelf tools available on the market to achieve this, businesses can set their sites on an approachable Machine Learning horizon.

Untitled has experience implementing production use cases of Machine Learning related to: 

  • Marketing Automation
  • Financial Predictions and Models
  • IoT Applications
  • GeoSpatial Data Science
  • Insurance Risk and Claims Reconciliation
  • Supply Chain Systems
  • Inventory Predictions and Fulfillment Modeling

Untitled will be your guide through implementing Machine Learning programs from ideation to proof of concept, to scaled implementation into core architecture.

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