Machine Learning (ML) consulting and engineering services

Continuously generate insights from scalable, efficient, and reliable ML solutions at every stage of your data journey and close the gap between data science and engineering.

Get your free consultation nowGet your free consultation nowGet your free consultation now

Our ML engineering services

We offer our ML Engineering services at any stage of your project lifecycle.

Build ML services and applications

Onboard onto an ML platform

Train ML models more 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 you are just starting or want to implement ML-enabled features across your organization and grow your ML capabilities through a dedicated platform and tooling.

a diagram that shows different stages of data maturity: data readiness, data culture, data-driven strategy

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.

Level	Consulting & Design	Implementation & Deployment	Long-term support
Data Readiness	Assessing current infrastructure, identifying gaps, architecting the solution, and upskilling your team.	Developing a targeted solution that covers an end-to-end slice of your business while enhancing infrastructure, data quality, and processes.	Operating, maintaining, and optimizing your data infrastructure with system health checks and data quality audits.
Data Culture	Fostering a data-centric mindset through workshops and identifying the right technology for advanced data initiatives and cost-optimization possibilities.	Building or optimizing the most suitable data platform to enable ML initiatives and automate processes using MLOps best practices.	Continuously improving the platform, which in turn allows for continuous re-training and optimization of models.
Data-Driven Strategy	Evaluating your setup to identify opportunities for refining or adapting your strategy to evolving business needs.	Migrating cloud providers or platforms, optimizing existing ML infrastructure, and building specialized tooling to address advanced use cases.	Providing ongoing support with increasingly scalable, specialized, and sophisticated models, iterating for optimal efficiency while improving

Your ML engineering journey with VirtusLab

Discovery

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.

Strategy

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.

Implementation

Approach:
We introduce the previously agreed-upon solution, whether it’s an ML model, infrastructure change, or the application of a business strategy.

Outcome:
You receive a solution in the form of an ML system or a data and/or ML platform tailored to your organization.

Continuous improvement

Approach:
We establish an operational process to continue to support and scale the previously delivered solution while also considering further cost of ownership.

Outcome:
We provide a comprehensive long-term support plan that includes future upgrades and enhancements.

How you benefit from every aspect of ML engineering

Leverage our expertise in developing high-end ML engineering solutions for your business.

Data Science

Enabling your existing data science teams
Collecting requirements and building domain knowledge
Running experiments
Evaluating different 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 data quality and model performance monitoring

Our technology expertise

We are tech-agnostic and propose technology that fits your organization and existing infrastructure with a combination of open-source and enterprise software.

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
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

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

Accelerating_manual_defect_detection_through_ML-enabled_feedback_loop_cover

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

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
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

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

In-Store_Object_Recognition_with_ML,_EDGE_Computing_&_Computer_Vision_cover

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

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.

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.

Real-time_personalisation_to_increase_sales_opportunities_cover

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.

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 leadership and 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.