Netflix saves $1 billion each year thanks to a Machine Learning algorithm
We think the combined effect of personalization and recommendations save us more than $1B per year.
Machine Learning can be used across practically sectors and industriesMachine Learning can support your business in many ways.
Starting a Machine Learning project is not easy. Choose the right solutionThere is a variety of Machine Learning solutions tailored for different business needs. Let’s work together
Machine Learning is a set of artificial intelligence techniques that gives web and mobile applications the ability to learn, adapt, and improve over time. It does this by processing vast amounts of data, identifying trends and patterns within it – most of which would not be apparent to a human being – and then making decisions and taking actions to help meet specific objectives.
Machine Learning opens your business to a wide variety of new opportunities. You can personalize your customer experience, automate processes, and implement solutions that will change the way customers interact with your product.
Machine Learning is widely applied to business problems, reducing companies’ costs and increasing customer satisfaction. ML algorithms can be used in applications across practically all sectors – from ecommerce to finance, healthcare to education, cybersecurity to charity.
Machine Learning can be used in various business sectors - both B2B and B2C companies can benefit from it.
Amazon’s ML-powered recommendation engine drives 35% of total sales. Thanks to the AI-Bot Harry, AXA saves roughly 17,000 man-hours a year. At the same time Vodafone noticed a 68% improvement in customer satisfaction after introducing its Machine Learning chatbot TOBi.
American Express and PayPal detect fraud in real time by quickly analyzing millions of transactions to pinpoint which charges aren’t real. The quick service means customers can resolve the problem almost instantly.
Researchers based at UCLA managed to identify cancer cells with over 95% accuracy after equipping a special microscope with Machine Learning algorithms. Maybe that is why 69% of college-aged individuals would want AI involved in their medical treatments.
Machine learning is used in different sectors: from retail and finance, through to healthcare, to education and charity. E-commerce and marketing leverage ML algorithms for their recommendation engines to cater for their users better. Hedge funds use ML to forecast stock prices, whereas insurance companies can calculate risk more accurately. Banks and other financial institutions, are able to detect suspicious transactions – also using Machine Learning. Medical companies use ML to diagnose medical conditions based on sets of symptoms.
Machine Learning projects are usually high-risk projects, due to their complex dependencies on data. That is why top Machine Learning companies offer feasibility studies to reduce the risk before engaging into the project: the data gets reviewed and confronted with project goals.
World-class specialists at your disposal
Personalized Shopping with Countr
Countr is a personalized shopping app that enables its users to shop with their friends, receive trusted recommendations, showcase their style, and earn money for their taste – all in one place. When it comes to ML, we delivered the recommendation and feed-generation functionalities and improved the user search experience.
Speech-to-Text transcription with CocoonWeaver
We have designed an intuitive UX and developed a neural network that, together with Siri, enables the app to perform speech-to-text transcription and produce notes with correct grammar and punctuation.
Audio recognition with Baby Guard
To achieve a high performance, we used custom audio processing algorithms and neural networks to handle the classification of the signal. The system can detect a baby’s cry rapidly and accurately. Our designers handled the UX to make the app easy and intuitive to use.
Our partners about the cooperation with Netguru
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Frequent questions asked by our clients.Based on our experience and questions we receive from our clients, we wrote down a list of Frequently Asked Questions. We hope it will help you better understand the issues related to Machine Learning. Let’s work together
Enterprise software development projects are very difficult to estimate. Adding a Machine Learning (ML) modules make them much more challenging. That is why you should keep in mind that you are asking engineers to write a program that will generate a program, which will learn to do something useful for your business. It is complicated. It's difficult to answer this question in few sentences that is why we've answered it on our blog.
Netguru offers a variety of services, from data collection strategy to building a scalable machine learning infrastructure.
AI Design Sprint - rapidly validate your Machine Learning project
ML Processes Audit - verify your Machine Learning delivery processes
Data Quality Assessment - plan your data collection strategy
ML-Ops Transformation - build a scalable Machine Learning infrastructure
Data-Ops Transformation - build a scalable Data infrastructure
Machine learning applications can bring you more clients, increase sales and reduce business costs. However, if not used properly, they may lead to customer outflow, money loss and reputation damage.
Data is the key to success (or lack thereof) of machine learning applications. Short and simple: in traditional software development, humans create computer systems, and machines simply follow these pre-programmed rules. Thus, the crucial part of the application is the algorithm inside.
There are hundreds of business applications of machine learning. In general, it solves several types of problems. The main ones are:
Classification: Is this credit card transaction fraudulent or not? Is this email spam or not? Machine learning is a great tool when you need to divide objects (for example clients or products) into two or more pre-defined groups.
Clustering: ML discovers patterns in chaos. It enables those who use it to find parallels between data points and divide objects into similar groups (clusters). What is important, there is no need to define the groups in advance.
Regression: It's like future prediction. On the basis of an input from a dataset (usually historical data plus other factors), ML estimates the most likely numeric value of a particular quantity. It could be anything, such as stock or real estate prices, consumer behaviour, or wear and tear on a piece of equipment.
Dimensionality reduction: In an ocean of information, ML can choose which data are the most significant and how they can be summarised. In practice, it is applied in such fields as photo processing and text analysis.
Although machine learning gives businesses numerous new options, there are situations when it's better to stick with traditional software methods.
When are you better off avoiding ML?
You don't have enough data: Machine Learning is designed to work with huge amounts of data. Really huge. 100k records is a good start. If the training data set is too small, then the system's decisions will be biased.
Data are too noisy: "Noise" in ML is the irrelevant information in a dataset. If there is too much of it, the computer might memorise noise. This was the case of Tay.
You don't have much time (and money): ML is time- and resource-intensive. First, data scientists need to prepare a dataset (if they don't do it, see point no. 2). Then, the computer needs some time to learn. Then the IT team performs test and adjusts the algorithm. Then, the computer needs some time to learn, again. IT does some testing, and adjusts the algorithm. The computer goes back to learning... The cycle repeats over and over again. The more time is needed, the more you need to pay IT specialists.
You have a simple problem to solve.
To sum up: Machine Learning helps find patterns in the chaos of big datasets. It is worth considering when you have a complex task to solve, or if you’re dealing with a large volume of data and lots of variables. But this method has its limits. It's better not to choose it if you are limited by time, or the amount or quality of available data
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