Machine Learning

Making interaction with your products human-like

Boosting Your Performance

Machine learning can drastically improve the level of your business diligence. While you focus on your business goals, SOLVVE takes care of the research and technical implementation based on data science and exploratory data analysis. SOLVVE has a dedicated machine learning department of mathematicians and development experts with academic backgrounds run by experienced and tech-savvy project managers and team leaders.

Specialization

Our ML-departments specializes in the following areas:

Experience

SOLVVE experts and engineers have successfully delivered project dealing with:

  • Data forecasting.
  • Facial recognition and identification of individuals in crowded places.
  • Monitoring of human behavior and classifying anomalies online in real time.
  • Detection and classification of images.
  • Extracting data from images.
  • Background removal.
  • Extensive data analysis.

Benefits for your business

The development process at SOLVVE is based on agile methodologies, iteration and continuous delivery, as well as flexible payment schemes with zero financial risks for our clients.
Extensive experience supported by numerous case studies in our portfolio.

Level of experts. Our company has a dedicated ML-department led by project managers with relevant technical education and skills while each of our middle and senior developers is a high-level professional with a deep knowledge of a specific technology stack.

A business approach to maximize deliverables and minimize costs and risks through:

  • In-depth ideation of the product business logic.
  • Assembling a well-fitting team of developers.
  • Choice of technical solutions.
  • Assessment and risk analysis at each development stage.
  • A clear explanation of the client’s involvement in all the processes and preliminary results.
  • Transparent pricing methods and delivery schedules.
  • Guarantees.

Tailoring your custom solutions

Your project will get a personalized technical solution based on our expertise. Here is how we do it at SOLVVE:

  • You provide a detailed description of a task or a project to our team.
  • We study the description and come back to you within 1-2 weeks to set up a call where we will present our preliminary deliverance plan. During the call, we align our general visions on the projects with you and settle on questions that come up.
  • During the next 2-3 days, our business analysts and the development teams deep-dive into technical details of the project delivery. Each of the involved experts offers specific solutions and tools based on their experience and best practices.
  • Our team drafts a detailed document about the most efficient technical way to achieve project goals, including:
    • Types of works and tasks.
    • Steps for completion of each task and why they are necessary.
    • Breakdown of risks and potential roadblocks during the project.
    • List of team members assigned to the project and their skills.
    • Technology stack.
    • Time and budget estimates.
    • Target results.
    • Quality and deliverance guarantees.
  • Finally, we present this document to you in person or via conference call to agree on a cooperation process, including:
    • Approval of team members.
    • Acceptance criteria.
    • Payment schedule.

Why this approach works the best

Time efficiency.

You get a custom solution with a detailed work breakdown for your project right away. We do not waste your time with numerous calls and letters.

Full control and comfort.

For each project we deliver a сustom well-structured action plan explaining why the chosen approach is the most efficient time- and budget-wise.

Transparency.

You always have access to all the information about the project: a full and clear view of who is doing what and how the project is moving along.

Deep expertise.

Each of our developers and managers has a substantial academic background or a proven track record of successfully delivered projects in the field.

Business Domains

Our machine learning department is stuffed to cater to any of your needs in data science, math modeling, and machine learning engineering for your product to ensure the top-level human-like interaction for its users.

Our main domains are:

  • Healthcare
  • Security and Public Safety
  • Fintech
  • Social Platforms
  • eCommerce
  • eSports
  • Education

Technologies and Tools

Our team is versed in different types of testing, test automation and management. We work with advanced tools to accommodate different levels of testing complexity.

  • Languages
  • Libraries and
    frameworks:
  • Development tools:
  • Relational Database Management Systems:
  • Other:

Languages

JavaScript is a programming language that helps to build interactive and dynamic websites
C++ is a powerful programming language to develop operating systems, browsers, adn so on.
.NET is a platform that allows to build all sorts of applications in many different programming languages for web, mobile, desktop and IoT.

Libraries and frameworks:

SpaCy is a industrial-strength natural language processing tool.
SciPy is a scientific library for technical computing.
GluonCV provides deep learning algorithms in computer vision.
Sklearn is the most useful and robust library for machine learning providing efficient tools for statistical modeling including classification, regression, clustering and dimensionality reduction.
Power BI helps to turn unrelated sources of data into coherent, visually immersive, and interactive insights.
Seaborn draws informative statistical graphics.
Matplotlib is a cross-platform, data visualization and graphical plotting library.
BERT - Bidirectional Encoder Representations from Transformers (BERT) is a Transformer-based machine learning technique for natural language processing (NLP) pre-training.
SQLAlchemy is a library that facilitates the communication between Python programs and databases.
Flask is used to build web-applications.

Development tools:

Google Cloud Platform offers services for compute, storage, networking, big data, machine learning and the internet of things.
MS Azure Auto ML allows data scientists, analysts, and developers to efficiently build high scale machine learning models.
Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models.
Colab notebooks allows to combine executable code and rich text in a single document, along with images, HTML, LaTeX and more.
Jupyter notebook is a computational notebook to combine software code, computational output, explanatory text and multimedia resources.
Anaconda is used for scientific computing, e.g. data science, machine learning applications, large-scale data processing, predictive analytics, etc.
PyCharm provides code analysis, a graphical debugger, an integrated unit tester.

Relational Database Management Systems:

MySQL is a database management system used among other things for data warehousing and logging applications.
PostgreSQL is a highly stable database management system used as the primary data store or data warehouse for many web, mobile, geospatial, and analytics applications.
MongoDB is an object-oriented, simple, dynamic, and scalable database.
Cassandra is a database that handles structured, semi-structured, and unstructured data, giving users flexibility with data storage.

Other: