Machine Learning Development Services

Enhance your product and make informed business decisions using state-of-the-art Machine Learning models

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Machine Learning development services bg

Professional Machine Learning development

As a Machine Learning development company, we combine our expertise with software development, data analytics, visualisation and consulting support to ensure efficiency, safety and assistance to deliver end-to-end solutions. We create advanced ML solutions using supervised, unsupervised and reinforcement learning to help you optimise your business processes and enhance your products.

What we do – check out our Machine Learning services

Predictive analytics

Predictive Analytics

Predictive Analytics allows you to anticipate the future and make business decisions based on your current or historical data. It offers a variety of applications specific to a wide range of business sectors. Credit scoring can be applied to assess the likelihood of clients repaying loans. Sales forecasting is used to assess the demand for a product, while anomaly detection helps to identify risks and unexpected events with the use of data mining techniques. With the aid of predictive analytics, you can reduce risks while simultaneously improving business operations.

Example of Churn Prediction

Churn Prediction

This is a part of predictive analytics that helps to answer a specific question: which customers end their relationship with a company (or stop using the product) and why? In other words, churn prediction lets you pinpoint when users are about to stop using your services before they do so. It becomes a massive asset when it comes to increasing customer retention. Thanks to churn prediction, you can learn what are the pain points related to your product or services, and find the right way to improve them.

Machine Learning expert presenting different Custom Analytics solutions

Customer Analytics

Learning user behaviour and needs is crucial when it comes to digital businesses. Customer Analytics combines predictive analytics and customer segmentation to improve communication with customers and increase profitability as a result. We make it possible for companies to gain insights about the needs of a specific client segment and target tailored, direct marketing to their customers. When your selected user base is given the right message at the right time, you’ll see your conversion rates grow steadily.

Example of Text Analytics

Text Analytics

Text Analytics is used to translate large amounts of unstructured text into machine interpretable, quantitative data to speed up and automate all text-based processes. We can apply natural language processing methods to help you with all kinds of text, including documents, social media posts, surveys and chatbot conversations. One of the most popular applications of Text Analytics is automatic topic detection and sentiment analysis, which is particularly helpful when it comes to understanding your userbase’s needs.

A user using recommendations systems in a mobile application

Recommendation Systems

Personalisation is the key to success in the digital landscape, no matter which industry you operate in. Recommendation Systems powered by machine learning are used to predict user preferences based on their behaviour and experience. We apply recommendation engines to provide your customers or users with personalised content by suggesting the products and services they’re most interested in. By giving your users recommendations that are tailored to them, you can make sure to improve their satisfaction with the service, and increase overall sales.

Deep Learning in practice

Artificial Neural Networks (ANN) and Deep Learning (DL)

We harness most of the available Artificial Intelligence options to give you a solution you can be truly satisfied with. Neural Network-based solutions can find complex patterns in data that would otherwise remain hidden. We apply Artificial Neural Networks and Deep Learning solutions for image, character and speech recognition, where other Machine Learning methods are not applicable or efficient enough, in order to provide you with a seamless digital product that will leave your competitors far behind.

Tangible results, right on schedule

2 weeks
for the prototype
3 months
for the MVP

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Why should you use Machine Learning in your business?

Here are a few examples of how Machine Learning engineering can leverage cutting-edge techniques to help you solve complex business challenges and stay ahead of the competitors

Boosted sales revenue

The use of Machine Learning technologies can keep your conversion rates and sales growing while also reducing costs at the same time. Increase revenue results thanks to a better understanding of shopper needs, a more personalised user experience and effective marketing campaigns that make for satisfied customers.

Reduced operating costs

By limiting time-consuming human involvement with tasks that can be automated, operating expenses can be significantly reduced. Additionally, machine learning solutions help to detect unusual or unauthorised bank transactions, as well as identify and limit fraudulent insurance claims and loan applications that can put company finances at risk.

Increased operations speed

Automation of administrative tasks and client management systems reduce staff involvement. More effective customer service, detection of fake news, preventing cyberbullying and removing offensive comments in social media can be done using text analytics for instant and deepened interactions with customers and users.

Exploring unexploited areas

Harness the power of Machine Learning in a way that will benefit your business the most. Face detection and recognition, as well as attracting video-on-demand viewers based on their personalised interests are some examples of possibilities that can be brought to your company with the implementation of Machine Learning solutions.

Ready to create your ML Solution? Contact us!

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Different data science solutions

Where does Machine Learning excel?

Wherever there is enough data available along with a business justification, Machine Learning automatically translates different data types into information and actionable knowledge to support your business management.

  • Image Classification and Tagging
  • Natural Language Understanding
  • Speech Recognition
  • Speech to Text Conversion
  • Fraud Detection
  • Customer Retention
  • Recommendation Engines
  • Churn Prediction
  • Targeted Marketing
  • Processes automation
  • IoT – Internet of Things
years in software
digital solutions
of all projects
conducted remotely

Machine Learning solutions at Miquido

Our tech stack

Data Processing


Apache Spark

AWS Glue

Amazon EMR

Amazon Kinesis

Data storage


Delta Lake


Machine Learning

Amazon SageMaker







Google Data Studio

Power BI

Our development process

  • 1

    Scoping and Estimation

  • 2

    Workshops & Preparation

  • 3

    Design and Development

  • 4

    Product Release

  • 5

    Maintenance and Support

Understanding the nature of your project

First we identify the scope of your project and take our time to understand your requirements, business plans and expectations. We talk through the features you want your ML solution to have and the complexity of the entire project. This allows us to help you choose suitable tech services required to support your idea and estimate the development time. As a result of this phase, we give you a general quotation and development schedule.

Shaping the vision and discussing the details

Help us understand your business needs during Workshops with our Data Scientists and Data Engineers! During the meeting we will discuss all your requirements as well as available Machine Learning techniques, software packages and data infrastructure to help you choose the best strategy or sharpen your vision.

Bringing your project idea into reality

We start with data collection, consolidation and processing to get your data correctly formatted and ready for upcoming modeling. Then we select an algorithm suitable for your modelling case. After that, we let machines do their job! The chosen algorithm is now used to train the model using your data. Our development process is iterative as we refine and repeat modelling to make sure that the proposed model is the best for your specific case. The length of this phase depends on the project size and complexity and usually takes between 2-3 months.

Introducing your product to the market

As soon as your product is good to go, we take care of the deployment and release it under the agreed infrastructure. Timing is crucial here – we make sure that every element of the system is released on schedule and works perfectly. And even after the release, you can still count on our support and maintenance.

Taking good care of your product

It’s not a problem if you decide that your solution needs extra features or changes. We start working right away, all the while supporting the existing version. However, if you decide you want to transfer the project to your in-house team, we help you plan the process and make sure it goes smoothly.

Planter's interface - example of custom Machine Learning solution

Custom Machine Learning development services

Are you struggling to find a solution to solve your complex business questions? Choose our custom AI-based services. We combine ML development with data analytics, visualisation and consulting support to provide you with comprehensive insights.

What is there to gain? By exploring unexploited or previously unavailable analytical areas, we can help you to boost your business performance and stay ahead of the competitors. We will provide you with a complete solution, from assistance in goal setting and refinement all the way to visualisation and reporting solutions. Our fit-for-purpose approach leverages state-of-the-art methods and adapts them to make sure they do exactly what you need.

Want to know more about Machine Learning?

Does Machine Learning sound confusing to you? Don’t worry, pick a question and we will provide you with a brief answer!

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How does Machine Learning work?

Machine Learning (ML) automatically recognises complex, previously unknown and useful information in all types of data. In the ML process, a model learns by looking for patterns hidden within given data. The more data there is, the more accurately the model resembles the real process. Additionally, by adjusting model parameters we can further improve its performance. Having an adequate model built, we can then generalise its application and make predictions about fresh data.

What are the different types of Machine Learning?

  • Supervised Learning: This type of machine learning involves teaching an algorithm to make predictions based on data. Essentially, the algorithm is presented with a dataset that includes both input and output, and it learns to map the input data to the correct result. It is often used in image recognition and natural language processing.
  • Unsupervised Learning: This algorithm is presented with data without labels to find patterns and structure within the data independently. This type of machine learning is helpful in anomaly detection.
  • Reinforcement Learning: This type of machine learning involves training an algorithm to make decisions based on a reward system. The goal is to maximise the reward and minimise the punishment received by the algorithm. Reinforcement learning is used in game playing and robotics.

How to use Machine Learning in app development?

When it comes to app development, there are various ways to integrate machine learning solutions:

  • Personalisation: With machine learning algorithms, you can create apps that personalise the user experience based on their behaviour, preferences, and interactions with the app. This can help improve user engagement and satisfaction.
  • Predictive analytics: Machine learning can also be used to identify patterns in user behaviour and provide predictive analytics features that anticipate user needs and preferences. This can help increase app usage and loyalty.
  • Image and speech recognition: By leveraging machine learning algorithms for image and speech recognition, you can create apps that enable users to interact with the app through voice or image-based interfaces. This can provide a more natural and intuitive user experience.
  • Natural language processing: Machine learning can also be used to improve the natural language processing capabilities of the app. The app can provide more accurate and relevant responses by analysing and understanding user input.
  • Fraud detection: With machine learning algorithms, you can develop apps that detect fraudulent activities in real-time, such as phishing or credit card fraud. This can help protect users’ data and prevent losses.

What technologies are used in Machine Learning?

When it comes to the technologies used in Machine Learning, a variety of tools and frameworks are available that help developers build and deploy ML models.

These include popular programming languages like Python and libraries or frameworks like TensorFlow or PyTorch.

In addition, cloud-based platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure provide powerful machine learning tools and services such as data storage, model training, and deployment.

Other technologies used in Machine Learning include data preprocessing tools like Apache Spark, which can help clean and prepare large datasets for ML models. Additionally, specialised hardware such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) can significantly speed up the training of deep learning models.

What are the limitations of Machine Learning?

Although ML can be applied almost everywhere, there are some limitations we have to be aware of. It requires a large amount of high-quality data to perform well and deliver reliable solutions. There is always some bias as we are working only on an available subset of the data that might not fully represent the modelled process. There is also an ethical dilemma with a responsibility for the outcome of ML-based decisions (e.g. a self-driving car accident). In some cases, a simple interpretability of modelling outcomes may not be possible.

A woman discussing Machine Learning solutions with a client

What industries can use Machine Learning?

  • Healthcare
  • E-commerce
  • Entertainment
  • Fintech
  • Others

For our healthcare clients and care providers we offer patient risk identification and virtual assistance. By applying computer vision solutions, we can also assist you with medical image classification and tagging. Manual data entry can be replaced by automated, less error-prone processes.

Product recommendation, customer churn prevention, price optimisation, demand response management—these are just a few things we can do for your business! We also excel in conversational AI and chatbots for automated customer service.

Thanks to our experience with content recommendation systems implemented for music streaming services, we are experts when it comes to boosting endorsement accuracy. Dedicated social media solutions may find their application in post management and analysis. By using NLP we can interpret user emotions and opinions (sentiment analysis), detect fake news and delete offensive comments.

Our experience in the fintech industry can help your business make better credit scoring predictions. We can assist you with product design and development, portfolio management and pricing automation. Additional risks-related solutions may include fraud and unusual transaction detection as well as fraudulent insurance claims and loan applications.

Dynamic pricing, automated customer service, optimal marketing and product development; these are just a few more examples of potential benefits that Machine Learning solutions can bring into your business.

Want to talk about your idea?

Jerzy Biernacki

Hi, I’m Jerzy from Miquido. How can we help you with your project? Fill out the form – we’ll get back to you soon.

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