Machine Learning Services
ML development services
As a leading machine learning service provider, we will show how your business can benefit from implementing ML into your services.
Our ML experts will help you to run quickly an appropriate model and see how it performs in production.
Is your business ready for ML?
And why does your business need a machine learning agency as a partner?
Machine learning is not an outworld thing. In fact, to get an ML algorithm working, you do not have to spend months fine-tuning parameters or assemble terabytes of data.
Instead, you require someone with expertise in Machine learning development to run quickly an appropriate model and see how it performs in production.
Often, random AI models may show decent performance when trained on a huge dataset. This approach will never work if you do not have such amounts of data or require domain-specific performance. The correct choice of the ML model and its parameters may hit the target even when small amounts of data are available. However, to make the right decisions and correctly assess the performance, you need someone with skill and knowledge.
Data Science UA is exactly that machine learning service provider. We can provide you with tailormade machine learning services that will bring you on the level of other machine learning companies worldwide.
There are different types of machine learning. Depending upon the task, you may be restricted to one of them.
In case you have data that you want to train the model on, where target variables are set, supervised machine learning may be the way to go. Classification and regression – the two most popular supervised machine learning tasks. The algorithms will look at these answers and tune the model to match these labels. Another type – unsupervised learning – uses unlabeled data, meaning that you do not have an example of the answer that the algorithm has to provide. Typical unsupervised learning tasks include dimensionality reduction, clustering, anomaly detection, etc.
The third type of machine learning is called reinforcement learning. It resembles the way humans learn through trial and error. Reinforcement learning is used when the problem is not directly related to data, but there are rules under which it has to operate. The model has to analyze the conditions and make appropriate decisions.
What clients and partners say about us
Founder and CEO, Elafris Inc
CEO and Founder, Reply
Michael Korkin, Ph.D.
CTO at Entropix, Inc.
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AI R&D center for US product company
Together with American colleagues, our team creates a solution based on Computer Vision / Machine Learning.
– Reduce injury risks and prevent accidents in steel production with AI.
– Assemble a team of 10 talented engineers in a month amid quarantine.
Beauty and health stores chain (Ukraine)
The largest national retail chain of beauty and health stores, offering more than 30,000 assortment items.
– Up-sell and cross-sell enabling through a recommendation system.
– Clients churn prediction
Solar panels installer (Netherlands)
Rooftop solar panels installation for residential houses.
– Label roof coordinates and types based on satellite images (R&D project).
Odin money (US)
Odin is a global mobile banking app that offers keeping all your bank accounts in one place. Bills and financial milestones track through one integrated experience.
– Create and use ML model for the classification of all transactions.
Industries We Serve
Marketing teams tend to have lots of data about advertising, web analytics, customer behavior, etc. We can fine-tune all data analysis solutions to run like clockwork and free up more of your marketing team’s time to be strategic and effective. Our data science services company uses machine learning to:
– forecast sales;
– recommend products;
– analyze assortment and so on.
2. Retail (E-commerce)
Retail usually accumulates large amounts of data and is eager to use data analytics.
We can help with:
– customer analysis;
– assortment analysis;
– sales forecasts;
– marketing and advertising budgets optimization;
– increase the efficiency of merchandising and supply chain management.
Generation of optimized plans that enable predictive maintenance is one of the key goals for AI in manufacturing, as well it helps in:
– optimizing production lines and logistic chains;
– forecasting revenue;
– determining optimal employee workloads;
– setting up automated systems for monitoring compliance with safety regulations.
When artificial intelligence is working with IoT devices it means that data can be analyzed and decisions can be made without involvement by people. In a broad variety of industries where IoT is implemented, AI can help to identify patterns and detect anomalies in the data that smart devices and sensors transfer (for example, air quality, humidity, temperature, pressure, vibration, sound, and others).
FinTech companies usually work with sensitive information and have high-security standards. We take all necessary precautions to keep their data safe. Data Science UA can assist such businesses in:
– credit scoring;
– recommendation systems for both new and prospective clients.
6. Logistics & Warehouses
The transportation and warehouse industry is data-driven and needs analysis of historical and real-time data performed by intelligent algorithms. So our team can help with:
- traffic management improvements
- warehouse optimization,
- route optimization (“travelling salesman” problem),
- developing optimal loading systems and utilization systems for vehicles;
AI can help insurance companies deliver high-quality service as it has done for major leaders in other industries such as Healthcare, Fintech, etc.
Our data science agency can help to:
- create a more personalized service;
- predict the repair costs from historical data;
- provide a selection of better investments based on risks, preferences, and spending patterns;
- improve claims analysis.
Farmers aim to maximize production and profits using innovative software and data collection and analysis. We can make the analysis of historical and real-time images & data collected from databases, satellites, drones, IoT sensors that can help to:
- increase the yield of farmlands;
- ensure serviceability of farm equipment;
- monitor fields conditions, irrigation, soil moisture, etc;
- predict weather conditions.
Nowadays AI helps to deploy effective cybersecurity technology and allows businesses to solve major cybersecurity challenges: cyberattack, financial loss, or brand reputation damage. We can help cybersecurity teams to:
- analyze patterns in user behaviors and respond to changing behavior;
- identify cyber vulnerabilities and irregularities in the network.
AI is already transforming the healthcare industry—helping patients and hospitals optimize costs and increase care delivery through actionable insights. We can help to:
- manage and analyze data to provide;
- improve preventive care;
- create personalized treatments;
- make optimization of scheduling and bed management;
- detect and analyze patient patterns and correlations for better decision making.
Technologies we leverage
Languages: Python, R, Scala, SQL, C++, etc.
Visualization: Power BI, Tableau, Qlik, Matplotlib, seaborn, ggplot2, plotly, Bokeh
DBMS: Relational (MS SQL, PostgreSQL, MySQL), Non-relational (MongoDB, CouchDB, Cassandra etc.), Distributed (Hadoop etc.)
ML Frameworks: Tensorflow, Scikit-learn, SciPy, etc.
Architectures: On-premise, cloud, hybrid
Algorithms: Supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction, anomaly detection, pattern search), ensembles, reinforcement learning
Fields: Natural Language Processing, Computer Vision, Recommendation systems, Tabular data analysis, Signal Processing
Cloud Platforms: Amazon Web Services, Google Cloud Platform, Microsoft Azure
Why Choosing Data Science UA?
A strong Machine Learning engineering team.
We are deeply integrated into the Ukrainian Data Science community and can find all required domain experts to come up with the best Machine Learning development services;
We’ve worked with various industries before and can think out of the box.
Data Science UA can help with building and implementing ML solutions in different sectors. Besides, our team is not afraid to ask questions and look for information to learn new industry/business better;
Our technical know-how in ML solutions development is solid.
We’ve designed ML software for many businesses. Our company values a T-shaped approach: each specialist should be an expert in a particular domain and have broad knowledge in other spheres. Thus, we are able to provide excellent ML development services, finding solutions to unique problems.
We offer flexible cooperation.
Data Science UA chooses the form of cooperation that will be the most suitable for the project’s needs and goals. You can work with us in team-extension-mode or on a project basis. We can create cross-service projects (like consulting + data analysis & data science + recruitment or any other combination);
We use proprietary technologies.
Our Machine Learning software development company has worked in this domain for years and has developed and refined our own instruments. Now we can utilize some pre-built technologies to develop unique Machine Learning solutions tailored to the needs of your business;
We carry out R&D activities.
Our specialists love challenges and are eager to “do their homework”. We are an ML research company, keeping an eye on the new trends and looking for new ideas and approaches to bolster our services and products.
What is Machine Learning used for?
Machine Learning can be used to solve different business tasks like forecasting, optimizing, and analyzing. To see some real examples have a look at our blog.
What are our Machine Learning services?
With Data Science UA you can expect long-lasting guidance into the world of Machine Learning. Our services range from initial ML consulting and ML software development to assembling a dedicated ML development team to your project and educating your staff.
What are the types of Machine Learning?
There are three main types of Machine Learning: supervised, unsupervised, and reinforcement learning. There are also few hybrid approaches.