Elevate Business with Machine Learning
At Mad Devs, we provide in-depth solutions that leverage state-of-the-art algorithms and data analysis to extract actionable insights and empower you to achieve business goals effectively.
Our services
Mad Devs' Machine Learning department offers a spectrum of cutting-edge services, using AI's potential to elevate your business.
Chatbots
Level up customer interactions with our chatbot solutions. We design and develop conversational AI agents that offer seamless, round-the-clock support, enhancing engagement and user experiences.
Robotics
Delve into the realm of robotics through our expertise. We engineer intelligent systems that seamlessly interact with the physical world, driving automation and efficiency.
Computer vision
Illuminate visual data's potential. Our proficiency in computer vision empowers machines to comprehend and interpret images, unlocking advanced recognition and analysis capabilities.
Natural language processing (NLP)
Bridge the gap between human language and machines. Our NLP solutions enable systems to understand, interpret, and generate human language, fostering enhanced communication and efficiency.
Recommendation systems (RecSys)
Elevate user experiences with our recommendation systems. Our algorithmic designs analyze user preferences, delivering personalized suggestions that enrich engagement.
Consulting
Leverage our ML expertise to make informed decisions. Our consultants provide strategic guidance, ensuring your AI initiatives align with your business objectives.
Tech ML audit
Ensure the health and performance of your ML solutions with our thorough technical audits. We identify bottlenecks, vulnerabilities, and opportunities for optimization.
Helping in the hiring of an ML team
Finding the right talent for your ML endeavors is crucial. We assist in sourcing and building top-tier ML teams, ensuring your projects are in the hands of experts.
Trusted by
Explore the track record of our client success
Here is a compilation of our satisfied clients, where we've effectively realized business objectives.
Tech stack we use:
Python
A versatile, high-level, dynamically-typed language supporting structured, OOP, and functional programming approaches, prioritizing readable code.
PyTorch
An open-source machine learning framework for computer vision and natural language processing that simplifies deep learning tasks.
Sklearn
A Python library for machine learning, offering tools for data analysis, classification, regression, and clustering algorithms.
FastAPI
A high-performance web framework for building APIs with Python, providing automatic interactive documentation and data validation.
PostgreSQL
Cloudwatch
NumPy
Git
Prometeus
Jupyter
Graphic
Circle CI
Ansible
Docker
Our team
Our team of experts is the driving force behind our machine learning excellence.
Aleksandr Bryl
ML Engineer
Roman Panarin
ML Engineer
Nikolai Frolov
ML Engineer
Alina Burykina
ML Engineer
Egor Makartsev
Data Engineer
Petr Moskalev
Data Engineer
Advantages of Mad Devs’ ML
Our Machine Learning department stands out with 4 key advantages that set us apart in delivering transformative AI solutions.
Innovative
solutions
Tailored to your needs
Seamless
integration
Data-driven
insights
Check out our contribution to the ML insights
Insights
Transfer Learning from Large Language Models (LLMs)
In a world where technology is advancing at an unprecedented pace, transfer learning stands out as one of the most promising and influential areas of machine learning. This approach to training models opens doors to faster and more efficient development, as well as flexibility in adapting to diverse tasks. This article will introduce you to transfer learning, technical nuances, applications across various business sectors, and future prospects. Discover how transfer learning is shaping the modern world of technology and the opportunities it unveils for us all.
ML
The Future of Generative AI for Enterprises: Needs, Challenges & Leading Startups
Generative AI has already revolutionized many processes across a wide variety of industries. While a few years ago, the first startups seemed to many to be "toys" for individual users, now more and more such startups are appearing and offering powerful tools for entire enterprises, which have already become an integral part of their working infrastructure.Today, we will take a look at the current state of this market. What are the leading generative development startups, and what is their place on the market? What needs are they helping enterprises to cover? Are there challenges to their implementation? What role does it have for the future of enterprises?All these answers are in our article. Enjoy your reading.Generative AI is a specific field within artificial intelligence that focuses on machines’ capability to create new content, ranging from text and code to voice, images, videos, processes, and even complex 3D structures. Over the past decade, some aspects of Generative AI have made significant strides, but it was the introduction of large language models (LLMs) with easily accessible chat interfaces.
ML
Semi-Supervised Learning Explained: Techniques and Real-World Applications
Artificial intelligence is just starting to resemble what science fiction writers described in their works. But this is already enough to open fundamentally new possibilities for us, which are now becoming an integral part of our life. And all these numerous possibilities are backed by various machine learning algorithms, which process data in different ways, depending on which models they were trained. Today we will dive into semi-supervised learning, how it works, what problems it solves, and what opportunities it provides. But let's remember the difference between supervised and unsupervised learning first.All this is explained in detail in our article, enjoy a beneficial reading.Supervised learning is an approach in machine learning where the model is trained based on labeled data, where each input example corresponds to a known correct label. The model uses this to extract patterns and create connections between input data and their corresponding labels in order to learn how to predict them for new, unlabeled input data.
ML