Machine Learning Services
We combine PhD-level expertise with a thorough analysis of your business needs to help you choose the right technology for your ML project.
Your Vision, Our Expertise: Tailored Machine Learning Solutions
In today’s competitive market, businesses are leveraging machine learning (ML) to optimise processes, embrace the digital transformation, and identify new revenue streams. Unleash the full potential of data you already have or that can be easily generated with our cutting-edge ML solutions.
Stepping into the ML landscape requires a high level of technical expertise – this is where DAC.digital comes in. As a leading service provider of emerging technologies, we have a team of ML experts who can develop ML solutions tailored to the individual requirements and objectives of your business.
Whether you have a concrete idea already or just a broad vision so far, we can help you identify appropriate data-driven solutions with clear roadmaps for success.
What Can You Do With Machine Learning Models
Classical Machine Learning
- Unsupervised Learning
- Clustering
- Association
- Supervised Learning
- Classification
- Regression
Neural Networks
- Computer Vision
- Image classification, detection etc.
- Object Tracking
- Natural Language Processing
- Large Language Models (LLM)
- Reinforcement Learning
Deep Learning Frameworks:
- Pytorch
- Tensorflow
- Keras
- Apache MXNet
Edge device deployment frameworks:
- TensorRT
- ONNX
- Tensorflow lite
Regardless of the industry you represent, ML can help you solve a number of problems commonly encountered by businesses.
Image Recognition
In the context of ML, image recognition refers to the process of training computer systems to identify and classify objects in images based on patterns, shapes, colours, and textures within visual datasets. If you work with images in large datasets, ML can help you recognise and classify them with accuracy much higher than humans.
Common examples of use cases of this technology include:
- Medical image analysis: algorithms analyse X-rays, MRI scans, or CT scans to help healthcare professionals detect abnormalities and diagnose diseases.
- Security video surveillance: algorithms analyse footage from CCTV cameras to spot suspicious behaviour.
- Retail: image recognition facilitates inventory management by automatically tracking stock levels and identifying products on shelves.
Customer Service Automation
Many companies face challenges handling large numbers of repetitive customer queries, which leads to long response times and large investment into customer service staff training. By leveraging ML, you can implement a chatbot that can respond to customer inquiries in-real time.
Using natural language processing, these chatbots are capable of understanding human speech and meanings of the words. Consequently, they can provide direct responses to questions, guide consumers to relevant resources on your website, or redirect them to human advisors in the event of more complex issues.
ML models can analyse these vast datasets to identify patterns, helping you create detailed customer profiles that consider your clients’ purchasing behaviour, demographics, or browsing habits. Using ML, you can optimise marketing campaigns, identify high-value leads, optimise budget spent on ads, ultimately increasing conversions and ROI on marketing efforts.
Speech Recognition
With ML, computer systems can not only convert spoken language into text, but they can be trained to recognise and adapt to a wide range of accents, pronunciation variations, contexts, and even emotions.
Common applications of ML-powered speech recognition include:
- Customer service: natural language processing (NLP) can understand customer sentiment and understand customers’ questions, giving insights into client satisfaction and supporting customer success agents in providing relevant answers.
- Healthcare: algorithms can transcribe doctor-patient interactions, detect symptoms of medical conditions in speech, and automate initial patient interviews.
- Accessibility: speech recognition technologies help visually and hearing-impaired individuals control home devices through voice commands.
- Education: NLP systems can analyse language use in the classroom to provide insights into how teaching methods are received by students.
- Media and communication: speech recognition systems are instrumental in background noise reduction and real-time translations.
ML models can analyse these vast datasets to identify patterns, helping you create detailed customer profiles that consider your clients’ purchasing behaviour, demographics, or browsing habits. Using ML, you can optimise marketing campaigns, identify high-value leads, optimise budget spent on ads, ultimately increasing conversions and ROI on marketing efforts.
Customer Segmentation
As a business you probably have large amounts of marketing data from various sources such as email campaigns, website analytics, or lead capture software. Inaccurate customer segmentation translates into targeting the wrong audiences and allocating budget to initiatives that do not bring the desired results.
ML models can analyse these vast datasets to identify patterns, helping you create detailed customer profiles that consider your clients’ purchasing behaviour, demographics, or browsing habits. Using ML, you can optimise marketing campaigns, identify high-value leads, optimise budget spent on ads, ultimately increasing conversions and ROI on marketing efforts.
Predictive Maintenance
ML models can estimate equipment condition and predict equipment failures by analysing historical and current usage data and maintenance records. Thanks to that, you can address potential problems before they result in machine failure, significant downtime, and costly repairs. Using ML, you can optimise maintenance schedules, ultimately extending the lifespan of your equipment.
ML models can analyse these vast datasets to identify patterns, helping you create detailed customer profiles that consider your clients’ purchasing behaviour, demographics, or browsing habits. Using ML, you can optimise marketing campaigns, identify high-value leads, optimise budget spent on ads, ultimately increasing conversions and ROI on marketing efforts.
These are just a few key examples of ML applications. If you have an idea on how to deploy ML in your business, we can help you implement it.
Why Deploy ML In Your Business
Cost Reduction
Concerns about the high initial cost of machine learning solutions are valid. However, although this investment may be higher than hiring a new employee, implementing ML allows for the automation of repetitive tasks, completing them more quickly.
Additionally, ML models can analyse data and patterns to optimise resource allocation, reducing waste and unnecessary expenses.
Improved Process Efficiency and Accuracy
ML models perform tasks with high precision, minimising errors and variability that is inherent to operations performed by humans.
Moreover, ML algorithms continuously learn from data patterns, enabling you to steadily improve your processes over time.
Enhanced Capabilities
ML opens doors to your business carrying out new tasks that surpass human abilities.
Additionally, ML models can forecast future market trends with high precision, allowing your business to make informed strategic decisions, stay ahead of competitors, and easily adapt to changing conditions.
From Concept to Reality: What Can DAC.digital Do For You In Terms of ML Implementation
Refining Your Idea And Turning It Into a Concrete Roadmap
Do you have an idea for what you want to do with ML but you are not sure whether the project is feasible? Or you are unsure where to start the implementation? You can leverage the expertise we have at DAC.digital to bridge the gap between an ML project concept and an actionable implementation strategy.
You can explore the topic with our experts, looking at data and technical infrastructure you already have to determine how to best approach your proposed solution. We can also help you define a detailed project roadmap, laying the groundwork for successful execution.
You do not have a specific solution in mind but are intrigued by the potential of ML? Do not worry. Our PhD-level experts are here to analyse your business requirements, helping you understand how ML can benefit your company. They will present you with a Proof of Concept (PoC) to prove that the proposed solution will work and is suitable to your objectives.
Data Preparation
ML models require large amounts of data to train from to be able to return accurate results. There are three scenarios related to data needed for the implementation of ML solutions:
- The client does not have anything – we can build a dedicated solution that can gather the necessary data and label it.
- The client already has labelled data – we can then use their data to train ML models.
- The client has unlabelled data – for it to become a training dataset, images need to undergo data annotation. At DAC.digital we can help with this using either manual or automated processes.
Whether your circumstances require using your existing labelled data, annotating unlabelled data, or creating a custom solution to collect and label new data, you can rely on us to ensure smooth data preparation for your ML project.
Continuous Improvement
Our team of experts will not only design and train an ML model you need, but they will rigorously test it to ensure the solution matches your performance requirements. With their help, you do not have to worry about integrating it with your existing systems and workflows.
Our commitment to long-term cooperation with clients ensures that we can regularly review the chosen approach and adjust the concept if necessary. Such an ongoing collaboration not only guarantees the highest efficiency of the solution but also maximises the ROI.
Case Studies: ML Projects We Have Worked On
At DAC.digital, we are proud to have successfully delivered numerous machine learning solutions for clients from various industries. Each project presented a unique set of challenges that we were able to overcome thanks to the extensive ML skillset of our team and a bespoke approach to every project we work on.
These success stories not only reflect our technical capabilities but also give proof of our commitment to delivering value and fostering lasting partnerships with our clients. By diving into these case studies, you can get an insight into how versatile our machine learning solutions are.
With this in mind, remember that our knowledge and technology are not limited to specific industries. Let us know your ideas and rest assured that we will make them happen.
ML-Powered Eye-Tracking Solution For Real-World Customer Insights
A market research company approached us with the need to develop a gaze estimation system capable of accurately tracking user attention while they browse content on their smartphones in natural environments. They needed a team of experts that could build something entirely from scratch.
The project involved pioneering work in gaze estimation without relying on specialised hardware, an area that is highly innovative and uncharted. What further added to the project’s complexity was a need for precision in various lighting conditions and across different smartphone models.
As we had to overcome a barrier of limited data being available at the start of the project, we began by collecting training and test data through crowd-sourcing platforms and a dedicated web app. This data fueled the creation of an AI-powered computer vision processing pipeline, which accurately detects where the user’s gaze stops on the screen.
The project could not have been completed without applying ML algorithms with our ML experts using deep learning elements at every stage of image processing. And, we are continuously gathering and processing more training data to improve the algorithm’s accuracy even further.
AI And ML Sensor For Detecting OA Disease In Dogs
A startup founded by veterinary experts sought to create machine learning algorithms for a high-tech sensor that could detect dog Osteoarthritis (OA) once placed into a collar.
Our experts were tasked with building both software and hardware aspects of the solution. After cleaning the provided data and fixing pre-existing issues with incorrect data annotation, our experts employed deep learning models and a deep convolutional neural network to recognise patterns in the collected raw data.
Putting ML and AI at core of the project, they aimed to introduce significant improvements to the work and results done by the client’s previous partners. We were able to sort through all the data, extract deep features, and implement AI solutions and deep neural networks to distinguish between dogs with OA symptoms.
Consequently, our team successfully proposed a deep neural network machine learning algorithm to increase the accuracy of classifying OA severity and the diagnostic accuracy of the device and AI models.
What Makes DAC.digital Stand Out As an ML Partner
In an ever-evolving technological landscape, choosing the right partner for your ML project is crucial for its success. Here are key reasons why DAC.digital is your best choice:
PhD-level expertise: our team consists of PhD experts with research backgrounds in machine learning, computer vision, and natural language processing. They bring unparalleled knowledge and innovative thinking to each project, ensuring you get the most advanced solutions.
Proven track record: we have successfully delivered numerous complex ML projects across various industries.
Customised solutions: we understand that every client is unique and we take time to understand their specific requirements and challenges, developing custom machine learning solutions that align with their needs.
Holistic approach: instead of only providing quick fixes to problems, we develop comprehensive solutions that seamlessly integrate into your business operations and existing infrastructure.
Broad expertise: the knowledge and skills of our team span a variety of ML methods and our experts can develop machine learning solutions for a wide range of applications and industries
Buzz words such as Machine Learning and Artificial Intelligence have recently gained momentum in the business world. For businesses looking to deploy such emerging technologies to gain an advantage, it is imperative not to treat them as a supplement but as an integral part of the business processes. This is the same as a good doctor would suggest taking a balanced and nutritious diet instead of supplements. Our team’s main principle is to develop holistic solutions and NOT cut corners, making a vital difference for our clients to achieve their goals.
Our Experts
At DAC.digital, we are proud to have a team of PhD-level experts who bring a wealth of skills and experience to each project. Having extensively explored the topic of ML in their research papers, they can seamlessly turn your ideas into practical machine learning solutions, assisting you at each stage of the development process.
Even if we are approached with a topic that we are not familiar with, we can adapt to it quickly. Having numerous young specialists in the team is an advantage as we are not strictly focused on a specific area and we can easily adjust to new fields. At the same time, in the team, we have many experts and mentors with years of experience who provide their topical expertise whenever needed. I think I will not lie when I say that we can fulfil any project in the fields of deep learning and computer vision.
You can choose to let our scientists develop and manage the ML solution development process from start to finish, or you can decide to enhance your existing team. If you already have the relevant talent but are lacking professionals specialised in specific areas, we can provide you with tech and R&D experts who will complement your team’s abilities.
Meet some of our specialists:
Marek Tatara, PhD
Assistant Professor at Gdańsk University of Technology, AI/ML Expert at M5 Technology, Member of the Polish Society For Measurement, Automatic Control And Robotics. Works on the implementation of both EU-funded and commercial R&D projects from the field of Computer Vision, Machine Learning and Embedded Systems.
Krzysztof Wołk, PhD
NLP Scientist/Technical Project Coordinator. Natural Language Processing Expert with PhD in the field of Artificial Intelligence. Experienced in AI related project management. Constantly developing. Very interested in dialog systems, human computer interaction, multimedia and signal processing.
Stanisław Raczyński, PhD
Distinguished professional with an impressive track record of 17 years in ML/AI and audio DSP research, coupled with 23 years of engineering experience. He has actively contributed to various applied research projects, demonstrating his expertise in signal processing, natural language processing, machine learning, and robotics.
Karol Duzinkiewicz
Senior Computer Vision Researcher at DAC.digital. Seasoned engineer with many years of experience in international tech companies. Currently holds a team leader role in gaze estimation projects developed in the company.
Jacek Niklewski, PhD
Data Scientist at DAC.digital. Involved in projects related to computer vision, sports applications, medical diagnosis, and recommender systems. He graduated from the Computer Science degree in at Gdansk University of Technology. He has a MSc in Investment Management, a PhD in Finance, and a PgCert in Academic Practice in Higher Education at Coventry University.
Jan Glinko
Machine Learning Researcher at DAC.digital. He graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. He is interested in applying synthetic datasets for learning deep neural networks and in learning algorithms to reduce the amount of data required for effective network training.
Artur Skrzynecki
Machine Learning Researcher at DAC.digital. He graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. His areas of interest mainly focus on computer vision tasks, including biomedical data processing and deep neural networks training and evaluation. Apart from that, he also develops towards web development topics.
Cezary Polak
Machine Learning Researcher at DAC.digital. He graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. He is interested in using deep learning in biomedical engineering and also in generating synthetic data as photos and texts.
Michał Ostyk
Computer Vision Engineer at DAC.digital. He has experience in computer vision in agriculture, fast food, sports analytics, and healthcare. He loves researching SOTA and converting it into an MVP in Pytorch. However, he recently delved deeper into MLops.
Michał Affek
Embedded Machine Learning Researcher at DAC.digital. He is currently enrolled in an industrial PhD programme at the Gdansk University of Technology. His main interests are remote sensing (processing done specifically on satellites), machine learning algorithms for edge devices, and parallel computing.
Machine Learning Technologies We Use
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