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Case studies
Here are the cloud-based projects we have successfully completed. See how we helped to create tailored solutions, and met and exceeded our clients' expectations
At Chaos Gears, we deliver a research environment primed for data exploration and model training by data science teams. The production environment is ready for model deployment with ML-aware monitoring included
Our team helps you set the goals, values and essential metrics which allow us to measure success criteria. Your data teams can rely on us to support business problem modeling in terms of machine learning and algorithm prototypes.
With our exploratory data analysis ecosystem, we supply you with tools to validate and tune machine learning models.
Your models operate in a production environment, and we continuously monitor and verify whether they meet key assumptions. From A/B tests and retraining algorithms based on the most recent data, we can help monitor them and identify if they no longer meet the goals.
We provide our clients with professional services at any stage of their analytics projects. By choosing Chaos Gears, you obtain ready-to-work, cost-effective and versatile platforms dedicated to Data Science teams
With auditable, easy-to-search, and, above all, a secure environment to store data, regardless of their size and type, we provide value-added services. These meet all data engineering requirements including data lake, lakehouse, feature store.
Our service provides notebook-based research environments without memory or computing power limitations. All experiments and models are saved and ready for use many months after creation.
Leverage the power of our easy-to-manage, ready-to-train clusters with GPUs, with training controlled across the entire process. Comprehensive monitoring allows us to quickly detect errors during training and debug them easily.
Our flexible methods to implement models can adapt to meet various needs, from the classic autoscaling API, through serverless APIs, to the launching of periodic large-scale jobs. We help to deploy even hundreds of models at a time in a cost-efficient way.
Data quality and integrity are ensured by monitoring and evaluating metrics such as working time, resource utilization, efficiency and reasonableness of model results, or consistency of data distribution.
During the automated model training and deployment process, we use CI/CD tools dedicated to achieving data science goals. New prototypes and models quickly find their way to test and production environments.
The technology market has many products to offer to resolve the data team's daily challenges. At Chaos Gears, we're pragmatic. Based on our experience, we select and use only reliable products with an established position in the market. We use AWS solutions and/or open-source technologies that match your needs
We rely on services from the Amazon SageMaker family. It has dedicated parts for each phase of your data science projects.
We use open-source technologies implemented on the Kubernetes platform on EC2 virtual machines.
Chaos Gears specialists can boast AWS certificates that confirm our proficiency in the ML area and AWS cloud
Starting with a series of initial meetings to get to know the team and the operational problems it faces in the field of data analysis, as well as the key goals it aims to achieve.
Based on the collected information, together we select suitable technologies and create an implementation schedule and the project's long-term roadmap.
We determine a list of required components, prioritize it and define the timeframe and delivery of each component (e.g. register of experiments and models, feature store, research environment based on notebooks, automation and orchestration platform, or model implementation system).
You are guaranteed a high level of cloud environment security by building solid cloud foundations for your operations through the creation and configuration of AWS accounts, network services, budget alerts and access management.
Then, in accordance with the set priorities, we deliver the individual components of the MLOps platform. In the case of unknown or unspecified requirements, we create Proof of Concept solutions first. This allows us to verify if the tool's functionality meets your needs.
Quality control is kept through regular contact with the client's data science team to collect further requirements for the platform. We document the process of implementing components on an ongoing basis, demonstrate the products used and conduct training across the whole process on a regular basis.
Along with the management of your production environment, we provide comprehensive maintenance services ensuring that data analysis teams can work efficiently and effectively.
Tools are updated on an ongoing basis, as well as modifying and optimizing the platform when required.
We also correct any errors that were not detected during the tests.
Here are the cloud-based projects we have successfully completed. See how we helped to create tailored solutions, and met and exceeded our clients' expectations
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