Our offer

We evaluate the necessary MLOps strategies, implement and operate the chosen solution.

→ Strategic consulting and Development of suitable MLOps and AI strategies

→ Deploy, Scale, and optimise performance of ML Models

→ Model deployment, Monitoring and automated retraining

→ Data engineering & Model versioning

→ Security, Compliance, and Disaster recovery for ML workflows

Get started now!

In order to successfully advance your MLOps project, we first prepare a needs and “Maturity Assessment”. In a targeted analysis, we record your specific challenge with regard to necessary MLOps strategies and the current state of technical development in the company (GDPR-compliant). You will receive a report that summarises the problem in the context of your company's stage of development. Based on this, we would be happy to provide you with a service offer.

How we work

Partnerships with leading technology companies are used to the advantage of our customers and to make our services even better. In this way, we improve the customer experience, optimise the productivity of business processes, reduce costs and increase the security of our solutions. Perelyn is a certified Amazon Web Services (AWS) partner and part of the NVIDIA Inception Program. We have repeatedly proven our expertise in terms of technological knowledge and innovative potential.

MLOps is intended to create an efficient workflow between developers and business teams to automate and optimise the entire process of development, deployment, and maintenance.

Experts

“By integrating MLOps, companies can implement their AI projects faster, with improvements in efficiency and product quality, while remaining within the desired cost parameters.”

Tech Stack

MLOps Platforms

AWS SageMaker, Arize, MLFlow, Airflow, Kubeflow, DVC

Deep Learning Frameworks

TensorFlow, PyTorch

Deployment

Cloud (AWS, Azure, GCP), Docker & Kubernetes

Programming Languages

Python, Java, Golang, C++

Relevant insights

Traditional AI
Machine learning and the cloud

The increasing level of automation in manufacturing also requires the automation of material and plant testing with as little human intervention as possible. In order to remain competitive while meeting industry standards, companies strive to achieve both quantity and quality in production without having to make compromises. However, manual quality testing of workpieces usually only allows the analysis of individual samples from a specific product series...

#MLOps
#Machine Learning
#Predictive Maintenance
#Traditional AI
Michael Banf
April 12, 2023
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