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Machine Learning (ML) engineering services

Continuously generate insights from scalable, efficient and reliable ML solutions at every stage of your data journey.

Our ML engineering services

We offer our ML engineering services at any stage of data maturity and drive innovation.
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Build ML services and applications

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Onboard onto an ML platform

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Train ML models efficiently

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Scale machine learning models

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Deploy ML services onto IoT devices

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Integrate your ML solution across cloud platforms

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Automate unstructured data extraction and analysis

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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

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

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

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

01

Discovery

02

Strategy

03

Implementation

04

Continuous improvement

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

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

TensorFlow
pytorch logo
onnx logo
Skit learn logo
Spark-logo
Flink-logo
Schdules (Airflow, Prefect, Oozie)-logo
Kubernetes-logo
Kubeflow logo
Mlflow-logo
Kafka-logo
Terraform-logo
Hadoop-logo
Hive-logo

Commercial technologies

Azure_Machine_Learning
Dataiku_logo
Databrics-logo
Snowflake-logo
Amazon_Sagemaker logo
Vertex_AI logo
Azure_ai logo
Amazon_Bedrock logo
Gemini logo
Amazon_Redshift logo
Google BigQuery

Languages

python-logo
scala-logo
java-logo
go-logo
R programming language logo

Our ML engineering success stories

  1. 1

    Reduced analysis time by suggesting the data that needs to be reviewed and reducing manual intervention

  2. 2

    Enhanced the system's value over time by learning which drift detections are worth alerting

  3. 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

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  1. 1

    Captured video streams from multiple cameras and performed live inference on them

  2. 2

    Supported data scientists to quickly deploy models for increased security and fraud recognition, and greater workforce efficiency

  3. 3

    Ensured GDPR compliance for data security and retention

Computer Vision

In-store object recognition with ML, edge computing & computer vision

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  1. 1

    Processing real-time data enables our client to train machine learning models based on the latest user behavior trends.

  2. 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. 3

    Efficiently processes around 122 million rows of data per day.

Personalization

Real-time personalisation to increase sales opportunities

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Meet the experts

  • Zbigniew Królikowski

    Zbigniew Królikowski

    Staff ML engineer

  • Zbigniew Skolicki

    Zbigniew Skolicki

    Prncipal engineer and advisor

  • Joanna Sendorek

    Joanna Sendorek

    Senior engineer

ML engineering FAQ

What if my organization isn't sufficiently mature for ML engineering yet?
Can I tackle ML engineering on my own?
How do you ensure that the model performance seen during offline experiments is maintained through to the production?
What level of involvement do you expect from our organization's leadership and technical teams?
Can machine learning models be customized to meet my specific business goals?