Machine Learning Use Cases and How it Can Change Your Business
Machine learning is a multi-billion dollar industry that is taking the business world by storm. When applied with the correct MLOps strategy, it can bring enormous value to your business.
Many of the technologies that we use today use machine learning in one way or another. Many areas such as healthcare, retail, and marketing have been greatly impacted by machine learning and artificial intelligence. Experts believe that every business will be affected by machine learning by 2025, and the industry will be worth a whopping $96.7 billion.
How can businesses start using machine learning effectively? What are the use cases for this technology? Let's explore the best machine learning use-cases that companies can adopt to improve their business practices.
What is Machine Learning?
Machine learning refers to software programs that autonomously improve themselves through experience, without being explicitly programmed. The best example for this would be a baby who grows up with its parents teaching it how to speak; instead of choosing what words he/she wants to learn, the baby picks up the patterns thrown at him/her.
What makes machine learning so unique is that it doesn't need a large amount of data to function, unlike traditional software engineering practices, which would require hundreds of thousands or even millions of records for effective predictions. Since machine learning only needs the experience to grow and improve, it is much more effective for newer startups, which are usually dealing with a limited amount of data.
Using machine learning effectively in your business requires you to understand the different use cases and applications available. Depending on what industry you're in will determine how valuable ML can be. For example, if you are in retail or marketing, using this technology to optimize your customer service, marketing, and product development can be of tremendous value.
The Five Pillars of Machine Learning
There are five essential components to machine learning: Deep Learning, Neural Networks, Data Mining, Predictive Analytics, and Algorithms.
Deep learning is a sophisticated form of ML wherein the software program has an 'understanding' of the subject matter. For example, deep learning software can understand images and generate new ones or translate languages depending on its programming (deep learning). Deep Learning has been used to create programs that are capable of speech recognition and image classification.
For neural networks such as these to be effective, they need extensive training data. The training algorithms are known as backpropagation, a learning approach wherein weights for each node are adjusted to minimize errors in the prediction of the network.
This is why most machine learning programs need large amounts of data to be effective. Deep learning uses neural networks that consist of multiple layers, but only the first layer sees raw data such as images. The other layers are just different transformations of the previous layer until we reach the last one, predicting an output value.
There are two types of neural networks, supervised learning and unsupervised learning. In supervised learning, a machine is trained to predict based on labeled data (example: dogs vs. cats). In unsupervised learning, the machine is trained to identify structures and patterns in unlabeled data. The disadvantage with unsupervised learning is that the neural network needs time to 'learn' and 'grow' before making predictions.
The field of data mining has been around for a long time, but it wasn't until recently when machine learning became more popular. Data mining helps analyze large amounts of raw data and sort them into useful information (much like how we humans do)
It can be used for customer interactions, fraud detection, and market segmentation, among many other things.
Many businesses, such as Amazon, rely heavily on data mining algorithms to better understand their customers. For example, they can predict what you want or need based on your browsing habits. Amazon can also provide better recommendations because of its powerful data science team using data mining algorithms.
The Predictive Analytics branch of machine learning is used to predict future events based on a pattern that has been previously identified from the past. It's considered a combination of statistics and machine learning which allows businesses and government agencies to understand what's happened in the past to prevent or avoid similar situations in the future.
The most common form of ML algorithm is developed using linear regression for classification, linear discriminant analysis for clustering, and logistic regression for prediction. These algorithms perform best when given large datasets with clearly defined patterns, so it's not uncommon to require hundreds or even thousands of records before they can start analyzing and predicting new data.
How Can Machine Learning Be Applied To My Business?
Machine learning has been used in almost every industry, including healthcare, marketing, and finance. There are more than 9000 machine learning startups on Crunchbase alone. Here's a list of 10 different industries where ML was applied this year:
Machine learning applications in healthcare are very broad. Hospitals use IBM Watson to help doctors diagnose patients faster. IBM Watson can also create treatment plans using natural language processing (NLP) techniques, which helps doctors understand the plan better. Now, they're even working on a partnership with Apple to create an app that will provide recommendations for cancer treatment. Machine learning in healthcare has the following sample use-cases:
- Predicting Alzheimer's progression
Using machine learning, researchers predict the progression of Alzheimer's disease, which can help patients and their families prepare for future challenges. Using data from a memory clinic in Toronto, they created an algorithm that was used to check other patients who also have early-onset dementia. The team found out that humans are only right about 53% of the time, while their algorithm is more accurate at 81%.
2. Personalized cancer treatment
It's commonly known that 1 in 3 people will get diagnosed with cancer during their lifetime. Unfortunately, the survival rate has not changed much over the years because it's still difficult for doctors to identify the type of tumor and its sensitivity to medication. This is where machine learning algorithms can help by using MRI scans, CT scans, or other medical records to predict a patient's response to chemotherapy or radiation therapy.
The approach requires two steps. First, it will look at how well certain drugs work on different patients based on their DNA samples. It will then match the predictions against new data (that hasn't been used before) to determine if new tumors are similar to previously treated ones.
3. Predicting drug resistance
Researchers at a University in the UK have used machine learning to predict whether cancer patients will develop drug resistance over time. This allowed them to identify over 70 genes that might affect how abnormal forms of DNA interact with chemotherapy drugs making it easier for oncologists to give personalized treatments and potentially improve survival rates.
Machine learning chatbot applications have been created for many situations, including customer service, getting weather information, and news, to name a few. The idea is to use machine learning-based algorithms to provide customers with answers as quickly as possible using natural language processing (NLP), allowing chatbots to understand the context of questions.
Here's an example:
Chatbot for Customer Support
A popular way of how companies are implementing ML-based systems in their business is through chatbots. Using Natural Language Processing (NLP), it can recognize requests from customers over instant messaging apps like Facebook Messenger or Telegram and offer help accordingly.
Amazon has also implemented this feature into one of their services; if you want to buy something from Amazon but you're not sure which item you should get, you can ask Alexa a question, and it will help find the right product for you.
Machine learning in logistics can be used to streamline the supply chain, which means using ML-based systems to obtain information about how and where products should be shipped.
For example, a San Francisco-based startup called Pianta is creating a platform that will use AI for route optimization. They plan on selling their software as a service (SaaS) model so other companies can also benefit from this technology.
Another great example is a company named Dacheng which uses AI and big data analysis to track down vehicle fleets' issues by making predictions based on past events. This allows them to streamline operations both at the factory level and when vehicles are out in the field. Logistics software development is still in its early stages, but it has a lot of potential to revolutionize the industry.
Supply Chain Management
When it comes to supply chain management, there are multiple use cases where machine learning algorithms can be used to process data in a way that will help optimize the flow of goods from the manufacturer to your doorstep. For example, some companies now have robots that are using AI technology to scan barcodes and recognize prices; this allows them to track inventory levels at all times to predict when items need replenishing.
Machine learning in supply chain implementations includes dynamic pricing models based on customer demand or intelligent routing, suggesting alternative delivery routes for drivers that would otherwise cause delays.
Machine learning in retail is becoming very popular, and you can see it in many different use cases. For example, some companies are now using ML-based algorithms to create special discount offers, which will increase revenue for the company by enticing customers with deals that they won't be able to refuse.
Another interesting application is using AI analysis of customer service calls to improve the quality of support provided; many companies are afraid that automated customer service systems might be detrimental to their business, but research suggests this isn't true. In fact, this type of system might even improve communication efficiency between customers and customer reps, so these types of applications will likely become more prevalent over time.
How To Implement Machine Learning In Your Company
Implementing machine learning operations, or MLOps, in your company can be tricky, but fortunately, you can follow some guidelines to make sure you're on the right track.
Your team must consist of experts from various backgrounds to implement MLOps properly. This means having a mix of computational scientists, engineers, product managers, and other roles. It's also essential that everyone understands each others' job requirements, and they should all understand how their work fits into the bigger picture.
Machine Learning Consulting
If you're interested in getting MLOps implemented in your company, there's a good chance that you will be dealing with multiple vendors. To find the right fit, your team must have proper training on evaluating offerings and performing due diligence if necessary; this is where consulting can come in handy. Many consulting firms out there specialize in machine learning, and they can help you choose which vendor is the best choice for your specific needs.
Machine learning consulting companies will also help you implement your MLOps strategy by guiding your team through the entire process. They can make sure everything is being done correctly and even offer assistance if there are any issues in the future.
The whole point of machine learning consulting companies is that they know what questions to ask, when to ask them, and how to implement solutions, so if you want a smooth transition into ML Ops, you should consider this.
Machine learning consulting can help you deal with the major issues that might pop up, such as data quality or management. They can also make sure your team has proper tools for building and supporting machine learning models, so they'll know what to do if something happens.
Machine learning is a big part of our future; it will continue to impact businesses in many different ways as long as they utilize a streamlined strategy for machine learning development. While there are some concerns about automation taking jobs away from people, tech leaders believe it will create more jobs within various industries by providing new solutions that weren't possible before.
Many companies are already using AI algorithms to help them improve efficiency; as you can see from these examples, this technology is already impacting our lives.
Machine learning has become incredibly popular over the last few years because it offers a lot of value for businesses of all sizes. If you aren't already using AI algorithms, now might be a good time to start since many different ML applications can improve key business processes.