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ML methods

ML methods

  • Supervised learning
  • Semi-supervised learning
  • Unsupervised learning
  • Reinforcement learning
Neural networks

Neural Networks

  • Tensorflow
  • Tensorflow Lite
  • Pytorch
  • Keras
ML functions

ML functions

  • Classification
  • Detection
  • Segmentation
  • Optimization
ML applications

Applications of ML

  • Computational intelligence
  • Medical diagnostics
  • Space exploration
  • Automotive and logistics

Know the difference between Machine Learning and Artificial Intelligence?

Read about it

Machine Learning.  

Human learning is commonly understood as the long-term change in mental representations and behavior due to experience. Machine Learning (ML) is a branch of Artificial Intelligence and computer science that uses algorithms and data to imitate human learning, gradually becoming better and better. Machine Learning has several benefits over other conventional methods due to its adaptability, ever-improving precision, and accuracy through constant learning. Thus Machine Learning gives businesses an edge to constantly keeping up to date with the market and consumer needs. Today, it has become easier than ever to set up or integrate ML into existing business processes, especially with our services that have been complied with years of R&D.
Interesting fact on Machine Learning for businesses

The popularity of Machine Learning applications in business rose steeply in the second decade of the 21st century and proved to be effective during the COVID-19 pandemic for various sectors. Interestingly, on the one hand, the applications of Machine LearningL have risen, and on the other hand popularity of Machine Learning as a field of study had reduced by up to 30% during the pandemic. How is this significant for businesses? Very! In the next few years, between 2023 to 2025, there could be a further shortfall of human resources with expertise in ML. Hence outsourcing Machine Learning development is and will continue to remain a popular choice among businesses.

How Machine Learning can help your business

According to Forbes, Machine Learning is among ‘the most impressive’ new technological developments. Several aspects of a business can benefit from ML application, such as automation of routine tasks, determining risks more efficiently, enhancing personalization, understanding what consumers want, increasing consumer engagement, more in-depth marketing insights, better market predictions and forecasts, helping to determine the most efficient use of available resources, unstructured data management, and much more. To adopt ML efficiently, businesses should look at SAADE: Scalability, Automation and interaction processes, Algorithms – from basic to advance, Data preparation and management capabilities, and Ensemble modeling. Since Arthur Samuel first introduced the Machine Learning phrase in 1952,  it has come a long way in history. At, we strive to contribute to the legacy of ML through our innovative solutions based on extensive R&D. A conversation with our representative would go a long way for you to discover the best ML solutions for your business.

MLOps (Machine Learning Operations)

MLOps or ML Ops are practices aiming to deploy and sustain ML models in the workflow processes with reliability and efficiency. Deploying a Machine Learning model into the workflow requires a massive amount of data that would prove nearly impossible for a single person to manage or keep track of. MLOps processes come in handy in this scenario, as they can keep track of tweaked parameters, aid in the operation of debugging ML model(s), and help adapt to the changes to data in real-time. They consist of various components such as security, infrastructure management, governance, monitoring, model version control, model serving and pipelining, and model service catalog for all deployed models.

For business, MLOps has a significant role to play in terms of deploying Machine Learning solutions efficiently. According to a 2021 report by DataRobot, over 87% of businesses struggle with the lengthy timelines of model deployment. Moreover, for over 64% of businesses, a single model deployment takes at least a month, if not more. Such staggeringly low efficiencies were observed even with a significant increase in ML budgets of over 86% of organizations in 2021. This scenario is observed because businesses underestimate the complexity and challenges of deploying Machine Learing to the workflow. Thus MLOps is a must for the efficient deployment of Machine Learning for your business.

Our Team of experts.

Developing emerging technology solutions, such as Machine Learning, requires a high level of technical expertise and, at the same time, constant research and development to ensure the best and most advanced outcomes for businesses.  We, DAC.Digital, are a company born out of several R&D projects and has developed into a leading service provider of cutting-edge business solutions. What separates us from others? Our principles and commitment to them, experience with a range of technology stacks, the unique deployment process, transparent communication practices, high business acumen, and our illustrious clientele. 

Our team believes in collaboration with the clients and developing a long-term working partnership, which has yielded growth for us and our clients. Our clients interested in ML solutions can benefit from free workshops of discovery conducted by our in-house experts. They also help identify appropriate solutions and roadmap for achieving your technical and business goals.

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.

Krzysztof Radecki Krzysztof Radecki CTO,


In 2022 the estimated global market size of Machine Learning stands at USD 21.17 billion, which is expected to increase almost TEN times by 2029 and reach USD 209.91 billion.


Compound annual growth in market size till 2029


Increase in GDP


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