ML-OPS as a Service

Companies can outsource model serving and maintenance, model monitoring and data pipeline construction to us.

ML-OPS as a service

what it is?


ML-Ops stands for Machine Learning Model Operationalization Management. It is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. „As a service” means that you can delegate this task to us.

What problems it solves?


Our service solves the following problems: need to hire experts in the Machine Learning space, need to maintain infrastructure, need to decide about the software, and acquire appropriate licenses. 

What Are the benefits for your company?


Thanks to our service, you can benefit from your ML models without worrying about the production deployments. Our experts take care of the design and maintenance of ML pipelines. Your data are securely stored within our cloud infrastructure. 

Our Tech-specialization


The tool of the first choice in model management. We use modern frameworks to monitor models in production.

SQL databases & ETLs

They help us build effective data pipelines. The final goal of these tools is to create training and testing datasets for the ML algorithms.

Microsoft PowerBI

Self-service-BI tools help us monitor the quality of model performance and control the distribution of input variables.

Do you want to benefit from your Machine Learning models?

Leave a number, we'll get back to you to see if we can help you streamline your Machine Learning pipelines.

OUR Cooperation:
model and scalability

Our model of cooperation assumes that we periodically receive raw data from your company and then turn them into reports, visualizations or infographics. We work on a subscription model – you pay a fixed monthly fee for the maintenance and preparation of reports by us.

You can send us your data via email or upload it to our secure FTP server. Depending on your needs, reports can be delivered to you via email, FTP server or using self-service BI tools (e.g. PowerBI or Google Data Studio). If you already have a BI infrastructure, we can also connect to your environment and run processes inside your network. 

Depending on your needs, you will share with us also the definition of your Machine Learning model. We will deploy the model within our infrastructure and run it on a scheduled basis. Together with model results, you will receive also model monitoring report. 

We adapt to the scale of your business. Depending on your needs, you can delegate to us only selected reports, or maintain entire reporting environments – data warehouses, data pipelines, or set of dashboards based on self-service BI tools.

Model serving

Main challenge

You have a machine learning model but you don't know how to deploy it to production. You lack the necessary infrastructure. Building everything in-house can be very time-consuming.

How we can help?

We can deploy your model on our servers. We will serve your model on each batch of your data. If necessary, we can also develop a dedicated API just for you.

Model Monitoring

Main challenge

You have to observe the ML model performance based on live and previously unseen data, such as prediction or recommendation. Your team spends also lots of time checking differences in input data distribution.

How we can help?

We will monitor your model performance and inform you about shifts that should trigger model re-training. Along with the model performance monitoring, we will also monitor shifts in distributions between training data and production data.

Data engineering pipelines

Main challenge

On cyclical basis, you have to prepare data to supply training and testing datasets for the ML algorithms. Data ingestion, exploration and validation takes a lot of time of your team.

How we can help?

We will collect all the neccesary data from all different sources into one and complete dataset. We will also run data profiling to obtain information about the content and structure of the data. You will receive clean datasets along with the reports summarizing the data quality.

Sales process


First free workshop - we will introduce ourselves and find out more about your needs. The meeting will take around 30 minutes.


Second meeting - on this meeting we will present our vision of the target implementation. We will inform you what we need to from you and what are the potential barriers.


Proof of concept or mockups - in certain situations, we will create proof-of-concept so that you can feel the final result.


At the end, we will make you a final offer and if the offer meets your expectations, we will sign the Service-Level-Agreement agreement.

ML-Ops as a service - what are the costs?

The costs of our service are calculated to be profitable even for small and medium enterprises. Among our clients, there are even companies of a few people who, thanks to this service, consciously build their competitive advantage with data and ML models at an early stage of their company’s development. If you would like to know the cost of the service for your business, request a free consultation below and tell us about the situation you are trying to solve.

Schedule free on-line workshop

Ask us any question, just click on the button below and contact one of our specialists.

All your benefits

Secure cloud data storage

Secure FTP servers

Email inboxes with double authentication

Known costs

Experts and competencies

Modern technologies

check our other Services

Reporting as a service

Enjoy the benefits of your ML models. Don't bother with the infrastructure.


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