Machine Learning Development Services
Enhance your product and make informed business decisions using state-of-the-art Machine Learning models
Let’s work together
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 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.
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.
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.
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.
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.
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
Need your idea verified fast?
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!
Let’s work together
Where does Machine Learning excel?
development
delivered
conducted remotely
Machine Learning solutions at Miquido
Patent Office (GovTech)
NLP-based classification of patent applications
Miquido people are truly agile and definitively have a can-do attitude.James Allan To, Chief Commercial Officer, Nextbank Software Inc.
Our tech stack
Data Processing
Python
Apache Spark
AWS Glue
Amazon EMR
Amazon Kinesis
Data storage
BigQuery
Delta Lake
PostgreSQL
Machine Learning
Amazon SageMaker
TensorFlow
Keras
Pandas
NLP
Tesseract
Visualisation
Google Data Studio
Power BI
Custom Machine Learning development services
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!
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.
What industries can use Machine Learning?
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.