We build custom machine learning apps that will help you govern your data, create forecasts, and automate your processes.
With our consulting, your ML team will work independently but always have a place to get an answer from Python developers, ML experts, and auditors to supervise the results.
Serokell has worked with Python solutions for 5+ years, and we know what is necessary to launch a successful product with Python quickly and painlessly.
Python can be used to build various software in almost every industry, from powerful machine learning models to web solutions for fintech, insurance, e-commerce, or retail.
Python is one of the most popular languages in the world. Its extensive community of experienced developers invests their efforts into language development. These pioneering works can leverage the innovative solution for your case.
In a data-driven world, nearly every company needs to process big data. If you are working on behavioral analysis of your clients, personalized ad campaigns, and AI-based trend predictions, Python is the most popular programming language to implement such projects in. Share your task with expert Python developers and skyrocket your success.
Serokell can work as your dedicated development team for an ML project in Python. Gather an external developers team that will maintain the project, saving you time on hiring, training, and managing the big programmer team that you need only for the development stage.
We constantly check the latest tendencies of Python-based tools and libraries. We can help you determine your needs and compose an optimal solution to let you do your scientific work. Delegate the tasks in which your programmers have a lack of expertise and utilize our knowledge to develop your Saas in Python.
Below, you’ll find the collection of the most powerful Python libraries that are great for tasks in ML, web development, or data analysis. Need help with anything where Python and AI go together? We are happy to assist with your project.
Pandas is an open-source library for data analysis and manipulation. The library is fast, powerful, flexible, easy to use, and offers tools for manipulating numerical tables and time series.
NumPy is an open-source library that enables scientific computing with Python. It adds support for large, multi-dimensional arrays and has a vast collection of high-level math functions. The core of the library is written in C, which allows enjoying the flexibility of Python with the speed of compiled code.
Spark MLlib is a scalable machine learning library. It offers underlying optimization primitives and common learning algorithms and utilities. It is usable in Python, Java, Scala, and R.
PyTorch is an open-source machine learning framework that provides tensor computation with strong GPU acceleration. It helps speed up the whole development process from research prototyping to production deployment.
TensorFlow is an end-to-end open-source machine learning library based on data flow and differentiable programming. It is designed to help develop and train ML models as well as build and deploy AI-powered applications.
TensorBoard is a toolkit that helps measure and visualize data during machine learning experiments. It provides various tracking experiment metrics like loss function and accuracy, visualizing the model graph, and much more.
Django is a high-level open-source web framework that is excellent for developing complex applications. It reduces the time taken for the Python application programming with the help of DRY and MTV architecture patterns.
Flask is a lightweight micro-framework that is ideal for building web applications, from small to large ones. Being a micro-framework, it does not enforce any dependencies or tell the programmers how the project should look like.
FastAPI is a web framework for developing RESTful APIs in Python. It is an intuitive tool that is designed to be easy to use and learn. It works fast, fully supports asynchronous programming, and considerably increases development speed.
Pytest is an open-source framework for testing applications of various sizes, APIs, or databases. It allows writing simple and scalable tests in Python and can run tests in parallel.
An open-source web framework built on the concept of multi-threading, CherryPy handles multiple tasks simultaneously and brilliantly works with the rapid development of web servers and applications. It comes with its own HTTP web server. Self-contained, CherryPy allows developers to run apps within minutes of getting the library.
Falcon is an open-source web framework for building microservices, large-scale app backends, and REST APIs. Extensible and highly reliable, it can compile itself with Cython. The Falcon framework makes backend and microservice developments secure, fast, and easy.
Beautiful Soup is a Python package for extracting data out of HTML and XML files. It is known for saving programmers hours or days of work.
Scikit-learn is a simple and efficient open-source ML library for predictive data analysis. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
NLTK, or Natural Language Toolkit, is a kit of tools and libraries for symbolic and statistical natural language processing (NLP) for English written in Python. This suite of software is suitable for linguists, engineers, researchers, and industry users.
Plotly's Python is an open-source graphing library for creating interactive, publication-quality graphs. It allows creating web-based visualizations that can be displayed in Jupyter notebooks, saved to HTML files, or used as part of pure Python-built web applications using Dash.
Seaborn is a library for statistical data visualization based on matplotlib, another Python library. It is closely integrated with pandas data structures in Python and offers a high-level interface for drawing good-looking and informative statistical graphics.
SciPy is an open-source scientific computation library for statistics, engineering, and technical problems like signal processing. It provides various functions for optimization, interpolation, integration, and algebraic equations.
Python is a general-purpose programming language, meaning you can use it to create various software solutions regardless of specialization. It is one of the fastest-growing languages in the world.
Python is an almost universal programming language and has a wide variety of use cases in different areas. Its versatility and flexibility are among the main reasons why companies choose Python for business project development.
Today most of the machine learning tools and libraries are written in Python. Its data science ecosystem includes many tools for analyzing and visualizing large amounts of data.
Python is supported by an ever-growing community, which constantly develops and tests its various libraries, including those for data visualization and data analysis.
Python allows rapid prototyping, which leads to having more time allocated for marketing and promotion. Regardless of the industry, with Python, you will receive an MVP faster so that you can start testing with users and present the app to investors.
Moreover, Python allows building highly scalable solutions, which is vital to take into account when planning your business growth.