ML-OPS
as a Service
Seamless Model Serving, Monitoring, and Maintenance
Outsource your model deployment, monitoring, and data pipeline management to our expert team. We handle the infrastructure and upkeep, so you can focus on leveraging insights and driving results.
ML-OPS as a service
what it is?
OUR ANSWER:
ML Ops, or Machine Learning Operationalization, encompasses the practices needed to deploy and manage machine learning models in production with reliability and efficiency. By choosing ML Ops “as a service,” you can delegate these complex tasks to our team of experts. We ensure your models are deployed, monitored, and maintained seamlessly, so you can focus on gaining insights and achieving your business goals without the operational burdens.
What problems it solves?
OUR ANSWER:
Our ML Ops service addresses critical challenges for businesses deploying machine learning models:
- No Need for In-House ML Experts: Access our skilled team without the cost and effort of hiring specialized talent.
- No Infrastructure Maintenance Hassles: We handle all infrastructure needs, allowing you to focus on model performance rather than backend logistics.
- No Software Selection or Integration Worries: We provide and manage the right tools for your models, eliminating the complexity of software decisions.
- No Licensing Headaches: Forget the confusion and costs associated with software licenses—our solutions are fully equipped and ready to deploy.
What Are the benefits for your company?
OUR ANSWER:
With our ML Ops service, you can leverage the full power of your models without the hassle of production deployment.
- Worry-Free Model Deployment: Enjoy the benefits of your ML models while we handle the complexities of production deployment and monitoring.
- Expert Pipeline Design and Maintenance: Our seasoned professionals ensure your ML pipelines are robust, efficient, and tailored to your needs.
- Secure Cloud Infrastructure: Your data is safely managed within our trusted cloud environment, providing peace of mind and reliable access.
Our Tech-specialization
The tool of the first choice in model management. We use modern frameworks to monitor models in production.
They help us build effective data pipelines. The final goal of these tools is to create training and testing datasets for the ML algorithms.
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?
OUR Cooperation: model and scalability
Our cooperation model is designed for ease and adaptability. We regularly receive your raw data and transform it into insightful reports, visualizations, and infographics, all through a subscription model. You pay a fixed monthly fee for comprehensive report preparation and maintenance.
Data transfer is seamless and secure: send your data via email or upload it to our secure FTP server. Reports are delivered to you in the format of your choice—via email, FTP, or through self-service BI tools like PowerBI or Google Data Studio. If you have an existing BI infrastructure, we can integrate directly with your environment and run processes within your network.
Depending on your needs, you will also share 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 also receive a model monitoring report.
Our services scale with your business. Whether you need support with specific reports or require comprehensive management of your reporting environment—including data warehouses, pipelines, and self-service dashboards—we’re here to meet your needs.
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. SCHEDULE THE WORKSHOP NOW >>>
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.
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
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