Our ML engineering services
We offer our ML engineering services at any stage of data maturity and drive innovation.
Build ML services and applications
Onboard onto an ML platform
Train ML models efficiently
Scale machine learning models
Deploy ML services onto IoT devices
Integrate your ML solution across cloud platforms
Automate unstructured data extraction and analysis
Establish a low-code ML environment for your employees
Take your data maturity from Readiness to Strategy with ML engineering
We help businesses at every stage of their data maturity, whether they are just starting or want to dive deeper into ML and AI.
Data Readiness
You know the potential of data science and machine learning, but turning insights into action can be tough, especially if you're just starting your data journey. While your operational systems run, achieving your business goals remains challenging without a dedicated data infrastructure.
- Apply ML where it benefits your business most and win over stakeholders
- Cultivate a data-driven culture
- Build a future-proof data infrastructure
Data Culture
Your organization already has operational systems and solid data infrastructure. You might have even started with first ML practices. Still, you want to jump-start your ML initiatives, productionize your solution and dive deeper into analytics and data science to achieve your business goals more efficiently and effectively.
- Streamline delivery of ML projects with MLOps
- Expand your existing data infrastructure with ML platform capabilities
- Expand analytics for advanced insights and automated decision support
Data-Driven Strategy
Your organization has established its data, and machine learning (ML) infrastructure. This means you are ready to bring on additional improvements, and to consider specialized ML solutions like GenAI. Now, you need to complete an ML project more quickly, enhance a specific component, or adjust your software architecture.
- Drive ML and AI innovation at company scale
- Align ML adoption goals and practices across teams and departments
- Invest in scalable and efficient ML-based systems
Your ML engineering journey with VirtusLab
Approach
We work with you to understand the business domain, the quality and quantity of available data, your existing technical architecture, and to gather requirements.
Outcome
You receive a comprehensive preliminary analysis including the technology choice, possible architectures, a feasibility study and the associated value-proposition.
Approach
Our team presents a course of action or a proof of concept (PoC) for you to explore new opportunities. We design, implement and validate the concept, establishing its proof of value.
Outcome
You receive a proof of value for the ML model, data and/or ML platform or a business strategy.
Approach
We introduce the previously agreed-upon solution, whether it’s an ML model, infrastructure change, or the application of a business strategy.
Ourcome
You receive a solution in the form of an ML system or a data and/or ML platform tailored to your organization.
Approach
We establish an operational process to continue to support and scale the previously delivered solution, while also taking further cost of ownership into consideration.
Outcome
We provide a comprehensive long-term support plan that adapt to future
We meet your needs at every level
Each level begins with a collaborative assessment to identify the problem, define the goals, establish the timeline, and outline the project scope. We guide you through each level and equip you for the next, tailored to your specific needs.
How you benefit from every aspect of ML engineering
Leverage our expertise in developing high-end ML engineering solutions for your business.Data Science
- Collaborating with your existing data science teams
- Collecting requirements and building domain knowledge
- Quick pace of experimentation
- Use of state-of-the-art models and techniques
Machine Learning
- Choice of the best platform and tooling
- Rapid end-to-end integration of ML solutions
- Scalability and cost-effectiveness of model training and inference
MLOps
- End-to-end software development lifecycle
- Model promotion and controlled, frequent deployments
- Real-time performance monitoring
Technologies we use
Open-source technologies
Commercial technologies
Languages
Our ML engineering success stories
1
Reduced analysis time by suggesting the data that needs to be reviewed and reducing manual intervention
2
Enhanced the system's value over time by learning which drift detections are worth alerting
3
Machine learning processes operated without the need for continuous monitoring or manual intervention
Predictive Analytics
Accelerating manual defect detection through ML-enabled feedback loop
1
Captured video streams from multiple cameras and performed live inference on them
2
Supported data scientists to quickly deploy models for increased security and fraud recognition, and greater workforce efficiency
3
Ensured GDPR compliance for data security and retention
Computer Vision
In-store object recognition with ML, edge computing & computer vision
1
Processing real-time data enables our client to train machine learning models based on the latest user behavior trends.
2
The tailored recommendations to customers are now provided within just 15 minutes of receiving data about their behavior, as compared to a week previously.
3
Efficiently processes around 122 million rows of data per day.
Personalization
Real-time personalisation to increase sales opportunities
1
Processing real-time data enables our client to train machine learning models based on the latest user behavior trends.
2
The tailored recommendations to customers are now provided within just 15 minutes of receiving data about their behavior, as compared to a week previously.
3
Efficiently processes around 122 million rows of data per day.
Meet the experts
Zbigniew Królikowski
Staff ML engineer
Zbigniew Skolicki
Prncipal engineer and advisor
Joanna Sendorek
Senior engineer
ML engineering FAQ
What if my organization isn't sufficiently mature for ML engineering yet?
No worries. There are other ways that we can help, including:
- Guiding your organization through the steps to achieve the intended level of maturity especially in terms of your data ecosystem.
- Running a ML engineering workshop to see in detail where you are on this journey, available at a fixed price.
- Helping with identifying some aspects of ML engineering that bring the most benefit without you having to make a leap.
- Building a small scale proof-of-concept targeted at a vertical slice of your business to verify the value proposition.
Can I tackle ML engineering on my own?
Yes, it's certainly possible to build ML engineering and MLOps capabilities on your own. Choosing the best methodology, onboarding onto an end-to-end platform, adapting or implementing necessary tooling, as well as building the correct mindset and culture is all achievable. All of this, however, requires both dedicated capacity as well as extensive software engineering experience in this area. Collaborating with a software engineering partner who has broad industry experience as well as a proven track-record, like VirtusLab, will resolve many if not most of those challenges.
How do you ensure that the model performance seen during offline experiments is maintained through to the production?
Right at the start of the development, we establish the expected targets as well as automated testing procedures. Then those are continuously validated through CI/CD on every single change to the code, and the results are presented through specialized dashboards. We build a deployment method that rolls-out the model gradually and establish QA procedures that are executed before each big release.
What level of involvement do you expect from our organization's leadership and technical teams?
The involvement of your technical teams is crucial throughout the implementation process, as their expertise shapes the business context and ensures that the final product is aligned with the expectations.
Can machine learning models be customized to meet my specific business goals?
Yes, of course. Using an automated feature engineering and model training process, data can be fed into a choice of state-of-the-art models adapted to your specific goals. Moreover, techniques such as fine-tuning, where we use knowledge from a pre-existing model, can greatly enhance performance in a cost-efficient way. In cases of some models, such as large language models, knowledge-retrieval techniques such as retrieval augmented generation (RAG) can give you top performance at minimal cost.