April 24th | Retrieval-augmented generation — what comes after Hello World? | AWS Summit London

Machine Learning Operations

Train, deploy, and confidently operate your machine learning models by leveraging rock-solid, cost-effective, and easy-to-use cloud solutions supported by Chaos Gears' experts.


We offer professional support throughout every stage of your data science project. We help design, build, deliver and maintain cost-effective machine learning platforms, providing any and all services you might need.

Exploratory data analysis

We provide notebook-based research environments without memory or computing power limitations. All experiments and models remain ready for use long after creation.

Model training and fine-tuning

We help you leverage the power of compute clusters with GPUs and dedicated filesystems to train and fine-tune even the largest models.

Model deployment

We deploy adaptable models — from classic autoscaling APIs, through serverless and asynchronous APIs, to scheduled, large-scale one-off jobs. We can deploy hundreds of models at a time, in a cost-efficient way.

Model monitoring

We ensure that models are working as intended by monitoring the reasonableness of their results and consistency of their data distribution — on top of standard metrics like resource utilization, of course.


We use dedicated CI/CD tools to automate training, deployment and monitoring. New prototypes and models quickly find their way to test and production environments.

Large Language Models (LLMs)

We combine top-tier LLMs with a reliable AWS backbone and tools such as LangChain, LLamaindex and vector databases to revolutionize your business processes.


State-of-the-art tech

We are flexible, but reliability is paramount. The solutions we work with are industry-proven and well-established, regardless if AWS-based or open-source.

Amazon SageMaker
AWS Managed service

Cost-optimized MLOps platform for faster implementation

We rely on services from the Amazon SageMaker family, which has parts dedicated to each phase of your data science projects.

  • One of the most mature cloud technologies in the ML area
  • Low solution maintenance costs
  • Rapid platform deployment
  • Does not require a dedicated maintenance team
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Independence from cloud providers

Alternatively, we use open-source technologies such as Kubeflow, MLflow or Seldon Core implemented on the Kubernetes platform in AWS.

  • No vendor lock-in
  • A solution applicable in hybrid environments
  • Highly customizable and extensible for larger organizations
  • Easy migration to another cloud solution in the future
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Solution architecture

We help you set the goals, values and essential success criteria. Your data teams can rely on our support with business problem modeling in terms of machine learning and algorithm prototypes.


Model development

With our exploratory data analysis ecosystem, we supply you with tools to train, test and tune machine learning models in an automated way.


Production cycle

Your models operate in a production environment. We continuously monitor and verify whether they meet key assumptions and goals through A/B tests and retraining algorithms based on the most recent data.


We start 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.

Together, we then proceed to select suitable technologies and create an implementation schedule along with the project's long-term roadmap.

We establish a list of required components, prioritize it and define the timeframe for the delivery of each component (e.g. registry of experiments and models, feature store, research environment based on notebooks, automation and orchestration platform, or model deployment service).

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.


Expert know-how

Our engineers are certified by AWS as experts in their field. In fact, our Data & AI team is lead by a prestigous AWS Machine Learning Hero.

AWS Certified - Machine Learning SpecialtyAWS Certified - Solutions Architect ProfessionalAWS Certified - DevOps Engineer ProfessionalAWS Certified - Security Specialty

Case studies

Find out how we help our clients create tailored, cloud-based solutions that exceed expectations.

Complementary Data & AI services

Generative AI

  • Question answering
  • Content generation
  • Private data sources & Retrieval-Augmented Generation
  • Virtual assistants and chatbots
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Data Engineering

  • Collection and storage
  • Processing and cataloging
  • Access management
  • Automation
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Deploy your own generative AI platform

Make full use of your organization's data without the privacy and security concerns surrounding popular third-party platforms.

Here's how we can help
machine learning insights


Let's talk about your project

We'd love to answer your questions and help you implement efficient machine learning pipelines.

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