"Machine learning is improving almost any function and process automation by enabling operational adaptation based on changing conditions"

-Bruce Guptil

Machine Learning has grown to be a popular term in this 21st century because of the buzz of the evolution of technology. Before integrating machine learning into any workflow, there are certain things the executives of the company have to ensure are put in place.

This piece would enlighten you on what machine learning is, why it is necessary to insert it into your workflow, how you can insert machine learning into your workflow, and also give you some tips to inserting machine learning into your workflow.

Let's dive in.

What is Machine Learning?

Before understanding anything associated with machine learning and your workflow, it is essential to understand what the subject matter is.

Machine learning is the science of teaching a machine how to work. This is because machines are known to carry out repetitive tasks without getting bored like humans. For a machine to be taught or trained on how to work, it needs a lot of data to be able to work properly.

Why is it necessary to insert Machine Learning into your workflow?

  1. To carry out repetitive tasks without getting bored: Machine learning helps to carry out repetitive tasks that one can do manually in a faster way, hence eliminating boredom and human bias/error.
  2. For predictions: Machine learning is used to identify patterns in data before making predictions.
  3. Automated and smooth workflow: Shifting your workflow from a paper based to a more digital approach fused with machine learning will result in more efficiency.
  4. Avoids manual labour, hence saves time: Time they say, is money. When you employ a tool that eases you off your manual repetitive tasks, you save time and stay productive. You are eased off the manually tedious tasks of compiling and analysing documents. Such a process is still prone to human error.

Machine Learning Workflow Components

Machine learning workflow has a typical diagrammatic step-by-step representation which is almost uniform for all workflows.

The first step is the accumulation of big data, data processing, data modeling, and data testing. We would take a look at all of this in a minute.

  1. Accumulation of big data: Gathering of data is the major step in machine learning. This is because the quality of the data obtained, determines the quality of the machine you would be training. The machine would need to make credible predictions based on the data processed. Data integrity comes in here; the overall accuracy, completeness and consistency of data.
  2. Data Processing: In this process, you are trying to make sense of the relatively big data collected by cleaning, verifying and formatting the data into a usable set to be fed into your model.
  3. Data modelling: After cleaning your data, the next step is to develop and train your model on the training data set to solve a problem
  4. Data testing: You would need to validate and evaluate your model to be sure that it can solve the problem at hand. In this process, you would be checking out for bugs that will probably hinder the effective performance of your machine.

Uses of inserting Machine Learning Into Your Workflow

Uses of machine learning

Beyond providing an automated workflow that makes work easier, machine learning provides other specific benefits.

  1. Customer churn: One of the most popular uses of machine learning is to predict customer behaviour and find ways to improve the relationship between both parties. Here algorithms make use of previous data like sales to be able to make credible churn predictions.
  2. Customer segmentation : Having so many customers can be stressful when managed manually. The firm would fail to meet each customer at their various levels with the right content or product. Through predictive inventory planning and customer segmentation, the right set of customers are targeted at the right time.
  3. Fraud detection : Machine learning is trained to understand patterns and quickly detect anomalies like fraud. This is very much more efficient than humans.
  4. Image recognition:  Machine learning is used to carry out visual tasks like performing image content search, adding labels to the content of images with meta tags speedily and accurately.
  5. Information extraction: Machine learning through the help of OCR- Optical Character Recognition can extract data from images. You don’t have to bother about the tedious task of manually writing a text from a document. OCR provides the data extracted from the image in an editable and reusable format.

Problems associated with inserting Machine Learning into your workflow

It is important for the executives of a company to be very much aware of the problems associated with inserting machine learning into their workflow. That way, they would be able to tighten the knots in training an effective machine learning.

  1. Poor quality of data: As discussed earlier, the most important things to look out for while creating a machine learning model, is to ensure the quality of the dataset being used. The quality of data affects the performance of machine learning.
  2. Overfitting or underfitting: Overfitting occurs when your model is modeled around your training data so much that it does not generalize to other data points outside what it was built with; while underfitting means that your model does not even perform well on the training data. There has to be a way to find optimal balance.
  3. Time-consuming implementation: While some models can be built in one day, others may not be. The process of training models is iterative and time-intensive to achieve optimal results. From collecting data, to the complex process of data engineering, data munging, data wrangling, data training to data serving is a rigorous process.
  4. Cost: Hiring data scientists and ML engineers to help build AI into your workflow can be capital intensive. You will need to ensure you have a budget to put all of these in place.

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