Every year, Netflix saves $1 billion thanks to its use of a recommendation system powered by machine learning.
The machine learning algorithm used by Amazon decreased the company’s “click-to-ship” time fivefold. Because of machine learning, Google is able to block 99.9% of spam, phishing, and malware emails from ending up in its users’ Gmail inboxes.
Machine learning is no longer “nice to have.” Its extraordinary spread and growing ubiquity mean that businesses risk losing out to more innovative competitors if they fail to harness its potential.
From augmenting human creativity to reaching new levels of efficiency, a tailor-made machine learning solution developed by our in-house experts will help you reach your business goals.
With STX Next, you can develop an application in any artificial intelligence area, including, but not limited to, statistical modeling, classification, regression, clustering, anomaly detection, association rules, computer vision, natural language processing, speech recognition, chatbot, and recommendation systems.
Our machine learning services are complemented by end-to-end product design and world-class software development for a comprehensive and holistic client experience.
Our machine learning services
CUSTOM-MADE MACHINE LEARNING MODELS
We offer tailor-made, innovative solutions that cater to your unique requirements. You retain full control over the whole process of building an AI application, including the data processing pipeline, the ML model, and visualization.
APPLYING THIRD-PARTY CLOUD-BASED ML SERVICES
We will help you leverage third-party services, such as AWS, Google Cloud, and Azure to seamlessly incorporate ML solutions into your business and enhance your product capabilities. Adopting a cloud-based model will save you infrastructure-related costs, allow you to prototype AI applications faster, and increase your business agility.
Machine learning FAQ
It’s often said that machine learning is part art and part science. When you look for an ML model to fit your unique business needs, it’s never a matter of just picking the right algorithm. There are several factors that need to come together to create the perfect solution. Certain problems, due to their universal nature, can be addressed using elements available in existing, open-source libraries. Others, however, require a more individual approach and an end-to-end design. To identify the best machine learning model for your business, you will need to work closely with an AI/ML expert to help them understand your goals so that they can advise you on the most suitable solutions.
AI fulfils a wide range of roles in business, and this ubiquity creates a space for each company to take advantage of its potential. Regardless of the industry you operate in, making use of ML-powered solutions will help you automate routine and repetitive tasks so that your employees can use their time to provide a high-value contribution to the business. They can also reduce your operational costs and help you achieve a higher level of efficiency, as well as add innovative features that would not have been feasible without AI, such as a 24/7 customer service chatbot. Most importantly, customized ML that responds closely to your unique business needs will help you stay ahead of the curve and gain a competitive edge over other companies in your sector.
At its core, machine learning is about using data to answer questions. Thanks to its ability to process huge amounts of data without the need for human intervention, machine learning has been applied in a number of industries, from healthcare to aviation. We see and interact with ML-driven solutions of varying complexity in products and services we use every day, from searching for information on Google to applying for a bank loan to tracking how many steps you have taken in a day with a body-worn pedometer.
Machine learning is not a new concept—many algorithms have been around for a long time. As early as in 1947, Alan Turing gave a talk at the London Mathematical Society, in which he declared that “what we want is a machine that can learn from experience.” However, the concept has gained fresh momentum in the last decade when its development has accelerated exponentially. In essence, machine learning, as a subset of artificial intelligence, describes the way that we give machines access to data and let them learn for themselves. Machine learning models are not explicitly programmed by humans: they find patterns in the data they are given, just like humans would do, and then make decisions based on them.
Artificial intelligence (AI) and machine learning (ML) have often been used interchangeably, even though they refer to different concepts. Artificial intelligence, in short, is the science that explores simulation of intelligent behaviour in machines to train them to perform tasks that mimic human activity. Machine learning, on the other hand, is the practical application of AI concepts. We see ML solutions in products and services we use every day.
Start small to make big improvements in your machine learning product.