How to start?
To better understand where we are going and what we should achieve, our first step is to conduct preliminary research and identify objectives. We will guide you right where you need to be – ML Software Development is in sync with your goals and business processes. Your products are in our focus and are what matter to us.
What technology to use?
Our machine learning development company is technology agnostic – not ‘one size fits all’. We look at the problems, production, and business processes from all sides and views, selecting the most up-to-date instruments, best suited for the task at hand.
Is technology all that’s needed?
ML software development alone is half the story – our goal is to make new instruments (for example apps or customized software) that slot into your business neatly. To master the endless flows of data we will arm you with not only ML apps and software but also with data culture.
Machine Learning Development
Usually, when talking about developing a tool, service, or product of any kind with machine learning in it, the following phases are defined: Dataset selection, its preparation, machine learning model design and training, and, finally, the operationalization stage. These stages heavily affect the flow of each other and happen in parallel.
The main reason why machine learning software developers require extensive knowledge in many fields is that each stage can significantly improve or decrease the final value of a product. Machine learning developers simultaneously have to be good at statistics, engineering, and, obviously, machine learning itself. Let’s have a look at the machine learning development challenges:
In order to train any ML model, the proper dataset is necessary. To ensure a good start, machine learning developers select the best data possible according to their expertise. Key metrics here are the amount, diversity, and cleanliness of the dataset.
There is no ideal data, so each machine learning project includes a data preparation stage. Quite often, it is one of the most time-consuming stages. In this stage, issues range from text spelling and missing values to category imbalance and data relevance. Machine learning product development during this phase requires real skill and cleverness. Often, ML developers also need some domain knowledge to provide the best results.
ML Model design.
To design a good machine learning model, developers use data insight from the previous stage and business requirements to select and build the model that will solve the problem. Previous experience with similar tasks can significantly improve the output, even when building a model from scratch. Usually, it takes multiple iterations for a model to evolve from a relatively basic one to something a lot more complex.
ML Model training.
Machine learning engineers have to find a balance between learning time and model accuracy. It is possible to overfit the model and testing the training model can also be challenging. Machine learning developers bring their best knowledge and experience to build the best solution, considering future challenges.
Operationalize in production.
The operationalization stage is where machine learning software engineers are of no significant difference compared to classic software development. Depending upon service location and technical requirements, machine learning developers find a way to deploy the model into production. Most of the issues present here a purely technical, like server setup or data access. However, good machine learning development service is to involve continuous machine learning training and testing, which is more than a traditional engineering problem.
Founder and CEO, Elafris Inc
CEO and Founder, Reply
Michael Korkin, Ph.D.
CTO at Entropix, Inc.
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.
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.
Our Machine Learning Development Tech Stack
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?
- Our monthly service fee is relatively low and in the long run, it is very cost-effective cooperation;
- We strive to make our cooperation transparent. After the initial call we send you our business proposal with all details about the salaries, costs, and detailed sample calculations;
- You can choose to work with us in team-extension-mode or on a project basis. The engineers we hire will be 100%-focused on the tasks you provide.
What are the main phases in ML software development?
Generally, any ML project is divided into Dataset selection, Data preparation, ML Model design and Operationalization. Each part has its difficulties, take a look at our blog for more info.
How is ML development different from general Software development?
In General, Machine learning development requires more domain-oriented knowledge, broader expertise in both software development and machine learning itself.
When should I ask for ML Development services?
In most cases, regular software developers do not know how to optimize and run an ML model. Even in the most trivial ML use cases, the help of experienced ML developers may significantly reduce the time and money involved in the project development.