Applied Machine Learning

Software Ops can help you with your machine learning needs.


Applied Machine Learning

Software Ops can help you with your machine learning needs.

What is Applied Machine Learning?

Applied Machine Learning (ML) is a way that businesses can begin to leverage their data to create and utilize machine learning models to improve business outcomes. In other words, it is the practice of running your data through an algorithm, the ML model, and getting answers form the algorithm that can help your business succeed in the the current highly competitive marketplace.

 Our team will walk you through the steps of implementing machine learning by applying machine learning models. The outcome will produce the answers needed to make your business more successful. We will help identify what current and future business challenges can be addressed with current machine learning technology.  We will assess your data to see if it can be used in an existing model or used to train a custom model. Then we can implement a software solution for your business. Ultimately, we will get your machine learing project off the ground and help your business in ways that you could not imagine in the past.

Applied Machine Learning for Small Businesses

Software Ops helps make machine learning accessible to your small business by helping you adopt the various machine learning algorithms as well as  pre-trained machine learning models. With our help it is possible to build custom machine learning solutions that would have been impossible three years ago.

As a small business leader you must utilize computers and software to the fullest measure possible to better compete in the current technology driven marketplace. If your business has available data that can be put to work to improve operations, increase top line revenues or improve profitability, now is the time to engage with machine learning technologies. Your competition is not sitting still. You cannot afford to "wait and see" because you will never "see" what your competition is doing behind the scenes with respect to machine learning.

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Steps to Apply Machine Learning

The steps to use Applied Machine Learning are well understood by Software Ops. We guide you through the steps and help you make the decisions necessary to produce a useful and valuable Machine Learning asset for your business.

Questions to be answered:

  1. What is the business problem you are trying to solve?
  2. Can machine learning be applied to the problem?
  3. Do I have data that can be used to solve the problem?
  4. What off the shelf machine learning models can be used or do I have to train a model first?
    1. Do I have enough data and can it be labeled so it can be used to train a model?
  5. When I run known data through a model, am I getting the kind of results I am expecting?
  6. Once I have a functioning and accurate model in place, how do I put that model into production to produce the business outcomes I am after in the first place?
  7. How do I learn to trust the model?
  8. How do I improve our models over time?

Machine Learning Engagement with Software Ops

The first two steps are key to knowing if there is even an opportunity to apply machine learning to your business needs.

  1. First we will need to understand your business and what objectives you are seeking to achieve within you business.
  2. We will dive into your existing data to see if there is enough there to apply to machine learning techniques.

The next set of steps are the mechanics of Applied Machine Learning:

  1. We will identify if you can utilize existing machine learning models already created by the biggest players in Artificial Intelligence and Machine Learning, or will we have to train your own ML model.
  2. We will then begin the process of staging the data to either leverage existing models or train your own custom models.
  3. We then insert the execution of the ML models into your day-to-day business processes.
  4. Over time we assess the results of the ML models as they begin to provide the value you intended.