Machine Learning / AI

GM Machine Learning / AI Service

GM helps leverage the data in your business to drive change or new business insight. Machine Learning and artificial intelligence leverages infrastructure resources such as Big Data in order to enable different business systems and processes to be autonomous and accurate in their prediction, determination and decision making. Depending on your data(type, quality, volume and accessibility), your business requirements (service improvement, product design input, insight) or a need for innovation; GM can help you get started.

 

Machine Learning / AI overview

CFOs are swamped in data coming from ERP, payment processing, business intelligence, and a host of other systems built on structured databases. The finance function owns that data on a transactional level, monitors the data for risk purposes, and turns those big data sets into insights for operational and strategic decision-making. Indeed, the ability to harness deeper, more real-time insights from that data is a competitive necessity; conclusions made in every function of the organization run the risk of being flawed without timely, detailed, and accurate financial information.

Machine learning and artificial intelligence leverages big data in order to enable systems to learn how to improve by themselves. This is done by feeding data, adjusting output requirements all without being explicitly programmed. The key is that we humans give the data and ask for an output. We then train it to give us the results we expect(review, instruct, iterate) and let it go about its’ business. Machine learning and AI use algorithms to parse data without relying on rule-based programing which in turn enables businesses to make better and faster determinations and predictions

What
is it?
  • Automated and guided learning of machines on business data for new and innovative views on the data that is the foundation of a business
  • Usage of large quantities of data to:
    • Model data
    • Run data models
    • Serving Content
    • Provide predictions for specific business process/task
  • Automation of learning by machines, in order for the business to offer “smart” solutions to its customers, or new “smart” insights into the business for its managers and executives.
Who
is this for?
  • Get extraordinary amount of data from marketing databases, transactional records and public information records.
  • Embed machine learning into products to allow for quicker and more effective decision-making.
  • Over time, the machines can learn to distinguish what data points are important from those that aren’t.
  • Insight extracted from the machines will allow customers to optimize their processes.
  • Read More
Prerequisites
How it works
steps

Identify Business problems

Establish Goals and Success Metrics

Gather and assemble data

Define technical constraints and expected business outcome

Practices
  • Start with a business problem statement and establish the right success metrics
  • Data
    a.Assessment, verification, verbosity, veracity
  • Don’t move your data – move the algorithms
  • Assemble the right data
    a.Business domain—these are the people who know the business.
    b.Information technology—the people who have access to data.
    c.You need the active participation.
    d.Create new derived variables
  • Consider the issues and test before launch
  • Deploy and automate enterprise-wide
Tools

Tools
Development languages
Server side
Client solution examples
Customer Industries
Timber
Automotive
People
Vitaliy O.
Development Engineer
Andrew R.
Development Engineer
Oleksii S.
Development Engineer
Oleksandr K.
Development Engineer
Oleksandr P.
Python Development Engineer
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