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Get your organization ready for Federated Machine Learning


Federated Machine Learning enables companies to make use of distributed data and train better machine learning models while preserving data privacy. It is a paradigm shift in data science, because data from many different entities does not need to be centralized anymore. Instead, machine learning models are trained locally and fused together centrally.

mlx is a Managed Federated Machine Learning solution that enables to scale AI across multiple cross-company systems. It integrates seamlessly in the existing infrastructure and technology in order to develop, manage and deploy machine learning models across distributed data.

To get started with Federated Machine Learning and mlx, it is beneficial to consider the following activities.

Develop an AI-Based Feature and Service Strategy

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Build Federated Machine Learning Skills

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Conduct a Pilot study or a Proof-of-Concept

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Prepare IT Infrastructure and Processes

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Develop an AI-Based Feature and Service Strategy

Learn More
Build Federated Machine Learning Skills

Learn More
Conduct a Pilot study or a Proof-of-Concept

Learn More
Prepare IT Infrastructure and Processes

Learn More

Develop an AI-Based Feature and Service Strategy

Federated Machine Learning can enrich your products with AI-based features or services and help you to scale them across your customers. Companies benefit from developing a clear understanding of what AI-based features and services they want to offer their customers and develop a roadmap.

Questions you need to answer:
How does the AI-based feature or service create value for our customer?
How do we need to design the features or services to be useful and engaging?
What is the business model for the new service?


In this early stage, mapping the customer journey as well as scoping current ideas is helpful. A cost-benefit and feasibility analysis help to map the way forward. This article may also inspire you to design the right feature or service: Why smart companies are giving customers more data.

Get Started Now!

Develop an AI-Based Feature and Service Strategy

Federated Machine Learning can enrich your products with AI-based features or services and help you to scale them across your customers. Companies benefit from developing a clear understanding of what AI-based features and services they want to offer their customers and develop a roadmap.

Questions you need to answer:
How does the AI-based feature or service create value for our customer?
How do we need to design the features or services to be useful and engaging?
What is the business model for the new service?


In this early stage, mapping the customer journey as well as scoping current ideas is helpful. A cost-benefit and feasibility analysis help to map the way forward. This article may also inspire you to design the right feature or service: Why smart companies are giving customers more data.

Get Started Now!

Build Federated Machine Learning Capabilities and Skills

mlx empowers you to develop, manage and deploy machine learning models across decentral systems and data using Federated Machine Learning. Nevertheless, it is helpful to understand the technology to successfully apply and scale it across your company and customers. This understanding includes knowledge of the Federated Machine Learning development process, its different variations, general system design, security aspects and typical applications.

To evaluate your team's training needs you might want to consider the following questions:
Can we define detailed use cases that grasp the possibilities of Federated Machine Learning?
Do we know how to initiate federated training and manage, validate and deploy distributed models?
Do we know how to deal with challenges such as distributed data and data privacy?


To tackle these issues, we can support you by evaluating your team's knowledge, setting up tailored training events and building Federated Machine Learning capabilities.

Start Building Skills Now!

Build Federated Machine Learning Capabilities and Skills

mlx empowers you to develop, manage and deploy machine learning models across decentral systems and data using Federated Machine Learning. Nevertheless, it is helpful to understand the technology to successfully apply and scale it across your company and customers. This understanding includes knowledge of the Federated Machine Learning development process, its different variations, general system design, security aspects and typical applications.

To evaluate your team's training needs you might want to consider the following questions:
Can we define detailed use cases that grasp the possibilities of Federated Machine Learning?
Do we know how to initiate federated training and manage, validate and deploy distributed models?
Do we know how to deal with challenges such as distributed data and data privacy?


To tackle these issues, we can support you by evaluating your team's knowledge, setting up tailored training events and building Federated Machine Learning capabilities.

Start Building Skills Now!

Conduct a Pilot study or a Proof-of-Concept

The Managed Federated Machine Learning solution mlx helps you to reap the value of Federated Machine Learning. A pilot study gives you the opportunity to become familiar with the possibilities of the new technology and get hands-on experience with mlx. A proof-of-concept (PoC) challenges if your ideas are feasible and verifies if it can be developed in reality.

This acid test will let you experience a steep learning curve while receiving a realistic impression whether Federated Machine Learning is feasible for you. Further, it uncovers hidden challenges and generates feedback from various stakeholders.

Getting these insights is key for your success with the technology in the future. Pilot studies and PoCs are highly individualized to your needs and context. Feel free to get in touch with us, if you want to learn more.

Get In Touch!

Conduct a Pilot study or a Proof-of-Concept

The Managed Federated Machine Learning solution mlx helps you to reap the value of Federated Machine Learning. A pilot study gives you the opportunity to become familiar with the possibilities of the new technology and get hands-on experience with mlx. A proof-of-concept (PoC) challenges if your ideas are feasible and verifies if it can be developed in reality.

This acid test will let you experience a steep learning curve while receiving a realistic impression whether Federated Machine Learning is feasible for you. Further, it uncovers hidden challenges and generates feedback from various stakeholders.

Getting these insights is key for your success with the technology in the future. Pilot studies and PoCs are highly individualized to your needs and context. Feel free to get in touch with us, if you want to learn more.

Get In Touch!

Prepare IT Infrastructure and ML Workflow

Federated Machine Learning is a paradigm shift that brings the possibility of decentral AI. mlx is a managed solution that empowers decentral training, management and deployment of machine learning models. Nevertheless, your IT infrastructure and workflows need to be future-proof and support AI-based features and services.

Questions that need to be answered:
Does your current IT infrastructure allow for the secure communication with all involved entities?
Do the decentral entities have the computing power for the training of machine learning models?
Does your current machine learning workflow support new AI-based features and services?


Feel free to get in touch with us, if you have any questions and want to evaluate how your existing IT infrastructure and ML workflow can be adapted in the sense of Federated Machine Learning.

Get Started Now!

Prepare IT Infrastructure and ML Workflow

Federated Machine Learning is a paradigm shift that brings the possibility of decentral AI. mlx is a managed solution that empowers decentral training, management and deployment of machine learning models. Nevertheless, your IT infrastructure and workflows need to be future-proof and support AI-based features and services.

Questions that need to be answered:
Does your current IT infrastructure allow for the secure communication with all involved entities?
Do the decentral entities have the computing power for the training of machine learning models?
Does your current machine learning workflow support new AI-based features and services?


Feel free to get in touch with us, if you have any questions and want to evaluate how your existing IT infrastructure and ML workflow can be adapted in the sense of Federated Machine Learning.

Get Started Now!

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