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.
MLOps
Train, deploy, and confidently operate machine learning models by leveraging rock-solid, cost-effective, and easy-to-use cloud solutions — with Chaos Gears as your comprehensive MLOps partner.
Solutions
We help design, build, deploy and maintain cost-effective machine learning solutions, providing full-cycle MLOps services.
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
From classic autoscaling APIs, through serverless and asynchronous APIs, to scheduled, large-scale one-off jobs — 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.
Automation
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.
Expert know-how
Our engineers are certified experts in their field. In fact, our Data & AI team is led by a prestigious AWS Machine Learning Hero.
Proven tech
We are flexible, but reliability is paramount. We only work with industry-proven, reliable MLOps solutions.
Amazon SageMakerOpen source
Cost-optimized MLOps with lower time to value
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
Independence from cloud providers
Alternatively, we use open-source technologies such as Kubeflow, MLflow or Seldon Core implemented on Kubernetes 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
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.
Deploy your own GenAI platform
Make full use of your organization's data without the privacy and security concerns of third-party platforms.
Learn how we can helpTuned to your business
In order to deliver AI-powered solutions that perfectly align with your objectives, we work together to uncover GenAI opportunities with the highest ROI. With a reliable foundation in AWS and custom tooling, we can then iterate rapidly to deliver bespoke projects fine-tuned to your needs.
Secure and private
Your data can stay completely in your own environment. Beyond the confidentiality guarantees of Amazon Bedrock, we can restrict model use to vetted, fully open source foundation models and host them on your own AWS account. We can also restrict the project to services and tools that prohibit any third-party access to your data.
