Machine Learning Consulting Services We Provide
Before rushing into the software development process, a few essential nuances have to be considered. Unfortunately, these nuances can often become obstacles that prevent companies from addressing a machine learning consulting company.
Overall, companies follow similar steps to implement machine learning, bringing potential solutions from ideas to functioning software.
The first step, like in most cases, is the problem definition. As a leading machine learning consultant, we believe it is the most crucial step that machine learning consulting can help with.
The key points we can help you to discover with our machine learning consulting services are the following:
When setting the ground for machine learning development, clear metrics have to be set. It is crucial to set not only clear performance metrics but also training and evaluation ones. Without these, no clear goal could be set. And, because metrics vary from the application area, task type, and other variables, it is best to consult with an expert in this field.
Quality and Quantity of Data
Furthermore, to define the task, input data has to be evaluated. It is a common saying that data preparation can take up to 80% of the project development cycle. Indeed, depending upon the quality and quantity of Data, different techniques may come up. If there is not enough data, or it is “dirty” additional steps like data cleaning or feature engineering may have to be introduced in the overall development cycle.
Solving the Problem
Also, often business owners have a broad vision of the expected feature/product. What may seem like an ambitious goal can turn out to be a set of specific engineering tasks. It may even be possible to divide a single idea into many smaller tasks, which may help bring the expected machine learning tool, service, etc., to life faster.
Selecting the Right Models
Additionally, ML consultants can help select and evaluate the specific machine learning model. Often, the particular problem requires particular and accurate model selection. Such things as domain, task type, and business expectations always have significant importance.
Feel free to refer to our 11 challenges in the ML project: Definition stage for more information.
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.
At Data Science UA, we are happy to help you understand potential machine learning use benefits. Expertise from our company can help you plan your time, budget, define project goals and metrics. At Data Science UA, we can provide you with everything necessary for the ML software development stage.
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.
When should I ask for ML consulting services?
You should contact us in case your business is not using ML to its full yet. You may also require our assistance if your solutions require improvement.
What can ML consultants help me with?
At Data Science UA, we are ready to bring all your ideas to life. First, we can consult you about different methods and approaches. And figure out the benefits you can achieve implementing ML solution using your data. Then it is up to you to decide which way to go.
What makes a good ML consultant?
For any ML consultant, it is necessary to have a lot of experience, including domain-specific ones. Then, it is also easy to spot a good consultant based on reviews and other businesses’ feedback.