Machine learning is providing enterprise-level solutions and careers for many companies. It is quite a broad field, however, and comprises many subcategories and countless uses. So what exactly is machine learning, how and why is it used, and what companies are using it? Let’s dive into those questions and clarify some of the noise surrounding the ML space and focus on specific ML examples.
What is machine learning and where is it used?
Machine learning is a branch of artificial intelligence that automates analytical model building. Machine learning systems learn from data, identify patterns, and then make decisions based on those patterns with minimal or no human intervention. It is used in every industry and across many different branches of an organization. ML has countless applications within enterprises and is a worthwhile investment toward automating previously time-consuming processes.
Some common examples of machine learning uses include sales or demand forecasting, product recommendation engines, speech recognition, and chat bots. These are all processes that can be crucial to the success of a business, but can be time consuming for individual workers. Companies can save time and resources by training a model to perform these functions, rather than using people.
What can machine learning be used for?
Machine learning can be used for any process that a model can be trained to do. That means that if there is training data available to teach a model which patterns to look for, then machine learning can do it. Many of the uses for machine learning center around labeling images or assigning meaning to words, and other pattern-identifying tasks.
What are some examples of machine learning?
The best way to understand a concept or technology is to learn from examples. So here is a list of 12 common enterprise machine learning examples.
1. Demand forecasting
Demand forecasting is crucial for businesses to perform to predict and prepare for future product demand. The process of manual demand forecasting is tedious and time consuming, so automating it with machine learning is a worthy investment for enterprises to make.
2. Sentiment analysis
It’s important for every company to understand their customers to provide them with the products or services they need in a satisfactory way. Sentiment analysis is the analysis of customer sentiment toward a brand and its offerings.
3. Customer churn prediction
Customer churn prediction is how companies analyze the number of customers who will take their business elsewhere within the customer base. Knowing when someone is likely to churn allows organizations to create marketing strategies to combat churn or attract new customers to replace customers who churn.
4. Customer retention analysis
Customer retention analysis is the counterpart to customer churn prediction. Rather than analyzing how many customers will churn, it analyzes customers who will likely stay with a brand.
5. Order fulfillment
Machine learning can be used to conduct order fulfillment, making it possible to fill orders at a faster pace and with fewer human resources than manual order fulfillment.
6. Account reconciliation
One of the ways the financial sector benefits from machine learning is account reconciliation. This simply requires a model that is trained to identify similarities and differences in order to flag inconsistencies in reports.
7. Invoice accounting
Invoice accounting can also be conducted using machine learning by programming an ML model to process invoices, thus saving time and resources typically spent performing this task.
ML has been used to review resumes for a while now. Applicant tracking system software is often used to reduce bias in the hiring process. It sorts candidates by credentials, accomplishments, experience, and skills, theoretically without the implicit biases of recruiters.
9. Predicting operations upkeep
Consider how helpful it would be for a company to know when maintenance will be needed on equipment before something breaks. Machine learning for operations upkeep relies on past maintenance records to identify patterns in usage, wear and tear, and strain, and uses those patterns to predict when maintenance will be needed.
10. Procurement tracking predictions
Machine learning can be used to predict deliveries and arrivals in procurement. The same technology that delivery companies already use to predict package arrival dates/times can be used for procurement tracking predictions in any enterprise.
11. Fraud detection
The financial sector relies heavily on ML models, which can be trained to detect anomalies that may be the result of fraud. This can be used to proactively decline transactions to prevent fraud from happening.
Lenders use machine learning to determine the creditworthiness of potential borrowers or investors. They can use ML models to identify red flags in their credit history. Credit scores are already determined using algorithms.
If it’s a large, national, or global company, it is most likely using machine learning in some capacity, but mid-size and even small startups are leveraging ML to stay competitive. Here are a few examples of the types of companies utilizing machine learning.
Every social media platform uses machine learning in at least small ways. Machine learning determines what shows up on each user’s newsfeed, which suggestions for accounts to follow appear, and which ads each user sees.
Search engines use machine learning algorithms to determine search results. For example, Google uses hundreds of data points in its search algorithm to determine which pages it will rank in its search results. This is similar for other search engines as well.
Voice assistant companies
Voice interfaces use machine learning to recognize and assign meaning to speech in order to provide a response or perform the requested action. Speech recognition is a difficult task for machines to perform, but this technology is constantly improving.
Many ecommerce companies use machine learning to provide product recommendations to provide a better user experience and upsell their customers while they are browsing the site.
If your company is ready to start a machine learning project or already using machine learning, see how Algorithmia can help with deployment and scaling.