Companies across industries are implementing machine learning-based solutions to improve productivity, decision-making, product and service innovation, and customer experience. Not to be left behind, 46% of organizations plan to implement AI in the next three years. Without a doubt, this is a decision that will allow them to adapt to a constantly changing market.
Companies across industries are implementing machine learning-based solutions to improve productivity, decision making, product and service innovation, and customer experience. Not to be left behind, 46% of organizations plan to implement AI in the next three years. Without a doubt, this is a decision that will allow them to adapt to a constantly changing market.
What does it consist of and what are the characteristics of machine learning?
Machine learning, Machine Learning or simply ML, is a branch of artificial intelligence that is defined as the ability of a machine to use historical data and algorithms to identify patterns and repeat them. With this, ML is imitating the way humans understand and learn. Machine learning programs are fed by data, from which they obtain knowledge. Depending on the type of data to which they are exposed, there are supervised or unsupervised learning.
Supervised learning
It is the simplest ML technology, in supervised learning you need someone to teach or train the machine using well-labeled data. The machine is then given a new set of similar, unlabeled data to classify based on its training. A simple example would be: if we label red and round fruits as "apple" and elongated and yellow ones as "banana", when it receives information without labeling with these fruits, it will be able to identify them.
Unsupervised learning
This learning uses information that is not classified or labeled, so the algorithm acts on that information without guidance. The machine groups the data according to its similarities or patterns. For example, if you show dogs and cats without prior training on the characteristics of each animal, the machine will be able to classify them into different groups based on their similarities.
Semi-supervised learning
This would be a combination of the two previous types of learning. Few labeled data and a large amount of unlabeled data are used in its training. Based on labeled data, algorithms explore unlabeled data and generate predictive models. This type of learning is common to identify types of calls in a call center. It would be very difficult to label a large number of conversations to determine the customer's problem and his emotions, so a small labeled sample is taken and pattern detection is used to classify the rest.
Differences between artificial intelligence and machine learning
There is a lot of talk about these two technologies and what they have achieved in recent years, but there is some confusion between the scope of each one and their differences. Artificial intelligence or AI, is the ability of a computer or machine to imitate human behavior and do some tasks that require thinking, reasoning, learning from experience and making decisions. Examples of artificial intelligence are industrial robots or personal assistants.
Machine learning is a specific approach within AI focused on developing algorithms for machines to learn from data. With ML, machines take data and past experiences to then make their own predictions. That is, they can learn without being explicitly programmed.
How do organizations benefit from machine learning?
Companies across industries rely on software development services such as machine learning services to implement solutions that improve productivity, decision making, product and service innovation, the customer journey, and more. These are some of its advantages:
Data mining to identify trends and patterns. With the immense amount of data that is generated every minute, it would be impossible for humans to analyze it all and identify complex connections or correlations. Machine learning technology is capable, for example, of predicting when equipment requires maintenance, long before it shows signs of deterioration.
Process automation. Machine learning models adapt to processes without human intervention. Because there is more and more data to learn from, efficiency and accuracy improve over time. Its applications are very varied, to mention a few, we have automated order entry, sending emails or planning routes in logistics.
Multidimensional data management. ML algorithms handle data in dynamic and uncertain environments. Multidimensional data analysis organizes and combines data to provide multiple perspectives. For example, to know the income of a company, instead of only having a semiannual or annual figure, you can have information by province or by sales team. This context allows you to make better decisions.
The application of ML in companies
Below we will see some use cases where machine learning is used to…
One of the potential services of software development company is Machine learning technology. Product recommendations in retail. Machine learning algorithms provide users with personalized and relevant suggestions, taking as reference the product categories visited (Recommended for you), the product view (Others you may like) or the products you have added to your shopping cart (Frequently bought together). These recommendations may also take into account demographic information or popular trends.
Fraud detection in the banking sector . ML algorithms can detect suspicious activity or anomalies that indicate theft of identity or access data to a customer's account. To do this, factors such as the history of your transactions and customer behavior are analyzed.
Supply chain management. Relying on historical data from other years is not enough for those who sell products. Any unexpected event could change the offer of an item and sellers must be prepared. AI and ML are key tools for making predictions taking multiple factors into account. It is also useful in other aspects of the supply chain, such as setting prices and selecting the best routes for transporting goods.
Machine learning tools
There are a variety of machine learning tools that can be used in various business processes.
Google Cloud AutoML. It allows you to create custom machine learning models without requiring deep programming knowledge.
Google Cloud AI Platform. It is a managed service for training and analyzing the performance of large-scale ML models.
Google Cloud BigQuery ML. It allows developers to train ML models directly within the BigQuery cloud data analytics service.
Google Cloud Dataflow. Process data in real time to implement scalable data streaming pipelines.
H2O.ai. It is an open source platform designed for data analysis, predictive modeling and machine learning in enterprise environments.
DataRobot. It is a platform that anyone without technical experience can use. It relies on intelligent automation to streamline the model development process.
IBM Watson Studio. Provides a collaborative environment for developing machine learning models.
At Sparkout tech solutions, a software development company has helped organizations like yours to launch themselves fully into digital transformation , optimizing their processes and staying competitive. The Google Cloud platform, based on Google's own infrastructure, availability and security measures, becomes a totally reliable option. Call us and discover why machine learning is not the future, it is the present and can take your business to the next level.
