Outrank the competition with Machine Learning Solutions

Machine Learning solutions are a revolution happening right before our eyes, and those late to the party might find themselves at a huge disadvantage.

Gain competitive advantage thanks to Machine Learning applications

Machine Learning is not only a tech buzzword. It’s widely applied to business, reducing costs or increasing customer satisfaction. ML algorithms can be used in applications across practically all sectors – from ecommerce to finance, healthcare to education, cybersecurity to charity.

AI and machine learning have the potential to create an additional $2.6T in value by 2020 in marketing and sales, and up to $2T in manufacturing and supply chain planning. And those are just a few areas where this technology can be implemented.

Netflix saves $1 billion each year thanks to a Machine Learning algorithm

Engaging subscribers with tailored content

Netflix’s recommendation engine takes dozens of algorithms into account and compares users’ preferences with similar people from all the countries where Netflix's service is available. It allows the company to engage their subscribers with tailored content and improve their loyalty to the service.

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We think the combined effect of personalization and recommendations save us more than $1B per year.

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Machine Learning can be used across practically sectors and industries

Machine Learning can support your business in many ways.
Saving
Increase sales

49% of customers are willing to purchase more frequently when Machine Learning is present.

Rocket
Improve productivity

Machine Learning technologies are projected to increase labor productivity by up to 40% by 2035.

Artificial-intelligence
Analyse large volumes of data

ML gives apps the ability to learn and improve over time. It is extremely adept at processing large volumes of data quickly and identifying patterns and trends.

Love
Improve customer satisfaction

75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10%.

Starting a Machine Learning project is not easy. Choose the right solution

There is a variety of Machine Learning solutions tailored for different business needs. Let’s work together
Rocket
Data Engineering

Prepare your data to make the most of AI algorithms.

Growth
Data Science

Find meaningful insights to improve your product or service.

Users
Recommender Systems

Create a personalized experience for every user thanks to an accurate recommendation system.

Translation
Natural Language Processing

Build natural interactions with your users and identify patterns in unstructured data.

Face-recognition
Computer Vision

Automate difficult decision-making processes based on images.

Speak
Audio Recognition

Identify patterns in audio data, enabling voice communication using a range of devices.

The right process is the key advantage

Over the years, our team has tested and implemented a transparent and efficient workflow for Machine Learning projects. The process helps our customers receive more reproducible results faster and in a more flexible way.

 

Our workflow focuses on three stages:

What is Machine Learning?

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.

 

Why do you need Machine Learning?

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.

What are the best Machine Learning examples?

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.

Where can it be used?

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.

Is Machine Learning something for your business?

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 

Our developers are open source contributors with more than 220 repositories on GitHub.
We constantly invest in developing new technologies and testing various solutions in our R&D department, sharing our experience both on expert blogs and at various tech conferences such as IGARSS, AAIA and MICCAI.

Our team has worked on different engagements, including many end-to-end projects.

Our partners about the cooperation with Netguru

moonfare-logo

My experience working with Netguru has been excellent. Outstanding software teams are resilient, and our developers at Netguru have certainly proven to be that. Our Netguru friends have become as close to team members as possible, and I am grateful for the care and excellence they have provided.

Gerardo Bonilla
Gerardo Bonilla
Product Manager at Moonfare
kw_logo

Netguru has been the best agency we've worked with so far. Your team understands Kelle and is able to design new skills, features, and interactions within our model, with a great focus on speed to market.

Adi Pavlovic Keller Williams
Adi Pavlovic
Director of Innovation at KW
Countr

Working with Netguru has been a fantastic experience. We received a lot of support in terms of thinking about how we track metrics, how we design this properly, and how we build the architecture. We are extremely grateful for making our platform what it is today.

manon
Manon Roux
Founder at Countr
10+
Years on the market
600+
People on Board
600+
Projects Delivered
67
Our Current NPS Score

Delivered by Netguru

We are actively boosting our international footprint across various industries such as banking, healthcare, real estate, e-commerce, travel, and more. We deliver products to such brands as solarisBank, PAYBACK, DAMAC, Volkswagen, Babbel, Santander, Keller Williams, and Hive.

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
How to estimate a Machine Learning project?

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.

What exact Machine Learning services does Netguru offer?

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

When to use Machine Learning - Does my app really need ML?

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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