Apheris Hub

Everything you need to help you understand federated machine learning, collaborative data ecosystems and working with Apheris
Computational governance
Machine learning & AI
Federated learning & analytics
Platform & Technology
Data & analytics
Collaborative data ecosystems
Data science
Case Study
Case Study: Drug Discovery AI Consortium
Foundational AI models for drug discovery have made a huge leap forward over the last 2 years. However, limited availability of high-quality molecular data is a major bottleneck to achieving the next steps forward. The AI Structural Biology Consortium was created to tackle these challenge and unblock the next revolutions in AI-driven drug discovery.

In this case study we explore:

checkmark The challenges around access to high-quality molecular data for Life Sciences
checkmark How the Consortium consisting of major pharmaceutical companies, prestigious model building partners and Apheris as the technology provider was created to tackle this
checkmark The use cases including model benchmarking & fine-tuning
checkmark Leveraging Federation to ensure secure, governed and privacy preserving collaboration between all parties
Demystifying the regulatory landscape
Explore an overview of data privacy, industry, and forthcoming AI regulations, examining their intersections in data usage. Delve into the emerging opportunities and challenges, amidst existing risks, and discover how to navigate this intricate regulatory terrain effectively with Apheris.
Are you ready for the EU AI Act?
Every organization using AI or ML in Europe needs to pay attention - the clock is ticking!
Are you prepared for the upcoming EU AI Act? Read our infographic to learn more!
Mastering the Compliance Challenge in AI
Discover the significance of agility and compliance in today's evolving regulatory landscape and delve into how a federated learning approach, backed by robust governance, can empower organizations to innovatively and securely harness sensitive data for trusted AI solutions.
Building for EU AI Act compliance
In the current data-driven landscape, regulations like the EU AI Act are increasingly important due to the growth of AI and global privacy concerns. The Act provides a legal framework for AI risks and applies to businesses, both within and outside the EU. This short ebook argues that preparing for this regulation is not just about compliance but also about leadership and innovation. It will discuss the Act's risk-based approach, the urgency of preparations, and offer insights on achieving compliance through Apheris.

In this ebook, you will:

checkmark Learn a little about the EU AI Act and why it's needed
checkmark Understand requirements for high-risk systems under the tiered approach of the Act
checkmark Why you shouldn't wait to prepare for the regulation
checkmark Where Apheris fits and how we can help with preparedness
Privacy and security for federated ML and analytics
In a world where data is pivotal, its full potential is often overshadowed by risks. As foundation models proliferate, data emerges as the unique advantage. The challenge? Leveraging it safely.
Our short ebook introduces Apheris' solution for secure computational access to data, connecting federated data sets for ML without the risks of sharing or centralization. A primer for data readiness for federated learning, prioritizing security and privacy.
Navigating the EU AI Act: compliance foundations for a new era
The EU AI Act marks a crucial step in regulating AI, addressing inherent risks and fostering innovation. Applicable to entities within and outside the EU, this article delves into the EU AI Act's structure, the urgency of preparation, and offers insights into achieving compliance.
Case Study
Enabling federated research for life sciences
A leading data aggregator in healthcare research partnered with Apheris to provide life sciences customers with granular access to patient data while ensuring GDPR compliance. Apheris enabled fully federated machine learning, optimizing the model training process and enhancing customer satisfaction by addressing long-standing GDPR issues. This partnership illustrates Apheris's potential to unlock data assets securely and efficiently.
Case Study
Customers do more with data through programmatic access for compliant ML workloads
An industry-leading data-as-a-service (DaaS) provider, operating globally and facing increasing demand for machine learning (ML) capabilities, partnered with Apheris to provide programmatic access to data while ensuring GDPR compliance. Apheris's non-movement of data and computational access governance led to several benefits, including quicker insights for their customers, reduced time and cost on regulatory compliance processes, and increased revenue by offering programmatic access for compliant ML as an added product feature.
Guide: Intro to Federated ML and analytics with Apheris
Discover the true potential of federated machine learning and analytics, implement it seamlessly with Apheris, and ensure data privacy and security while building cutting-edge ML models.

In this comprehensive guide, you'll explore:
checkmark What is federated ML and analytics and why does it matter?
checkmark Key stakeholders and practical considerations for successful implementation
checkmark Balancing privacy and security requirements in federated machine learning
checkmark Uncovering new opportunities and use cases for federated ML and analytics
checkmark Putting federated ML into practice and maximizing data value
Ebook: Machine Learning & the Benefits of Working with Federated Data
A detailed look into how data-hungry organizations no longer need to rely on data sharing for collaboration, and can preserve privacy and IP while working across boundaries.

Big or small, organizations the world over are waking up to the value of federated data. In this ebook, we explore:

checkmark Federated vs centralized
checkmark Examples of federated learning in industry
checkmark Common misconceptions around federated learning
checkmark Unlocking the power of federated learning
checkmark The Apheris approach to federated ML and analytics
White Paper
Privacy and Security Whitepaper
Privacy and security considerations are an important part of adopting a federated infrastructure for machine learning and analytics. This whitepaper outlines the various security techniques and privacy controls used by Apheris to safely collaborate and build data applications and AI across organizational and geographical boundaries.

In this whitepaper you will learn about:

checkmark The privacy and security landscape, and how Apheris operates within it
checkmark Defining attack vectors and threat models as the basis for a privacy and security strategy
checkmark Using the 5 Safes as a framework for privacy and security in federated ML and analytics
checkmark Practical advice for making the move to a federated data infrastructure
E-book - Federated Data Ecosystems in Pharma & Healthcare
Breakthroughs in healthcare are faster and more reliable with federated data ecosystems. By processing patient data without risking its integrity, data collaboration is safer and more effective than data sharing.
This e-book highlights real-world examples and explains how to implement a federated data ecosystem in pharma and healthcare:

checkmark Benefits of federated data ecosystems in pharma and healthcare
checkmark Examples in drug discovery, clinical trials and commercial growth
checkmark How data collaboration compares with data sharing
checkmark How to get started with federated data ecosystems
Infographic: What are Collaborative Data Ecosystems?
The world's biggest challenges are going to be solved in Collaborative Data Ecosystems.
Learn how in our free-to-access infographic!
Guide to Collaborative Data Ecosystems
Data collaborations are required if we're going to solve our biggest challenges. This guide discusses challenges with the status quo and provides answers in working with federated data and breaking down barriers while establishing a collaborative data ecosystem.

In this ebook we explore:

checkmark What is a Collaborative Data Ecosystem
checkmark Challenges with data sharing and why this is not the same as data collaboration
checkmark The concept of complementary data value
checkmark Establishing a Collaborative Data Ecosystem and working with federated data
checkmark Unlocking data's potential with Apheris
Infographic: What are Collaborative Data Ecosystems?
The world's biggest challenges are going to be solved in Collaborative Data Ecosystems.

Learn about the most important facts in our infographic!

checkmark What are Collaborative Data Ecosystems
checkmark The three most important roles in Collaborative Data Ecosystems
checkmark The types of capabilities organizations need today to collaborate securely on data
checkmark Why collaboration and deeper complexity of data sharing gives organizations a competitive advantage
Case Study
Case Study: Pharma
Learn how to leverage federated and sensitive data at scale and how you can collaborate with your partners to advance and evolve the practice of life sciences.

In this case study you will learn:

checkmark How two pharma companies use protein-ligand interaction data and AI models to predict target binding of novel compounds
checkmark How a pharma company fueld their drug discovery activities by leveraging molecule data from their suppliers
checkmark How an AI-focussed health tech company developed AI models to improve decision-making processes for cancer treatments
Case Study
Case Study: Healthcare
We need to make biomedical data easily accessible and securely usable, to find new medicines and diagnostics, and to establish data-based insights that evolve the practice of medicine.

In this case study you will learn:

checkmark How a MedTech company built up the capability for reliable and strategic data access to thousands of annotated MRI scans from several hospitals
checkmark How a data science team in a pharma company collaborated with hospitals and institutions to securely leverage cancer patient data for AI
checkmark How a genomics company analyzed genomics data from laboratories at scale
Case Study
Case Study: Manufacturing
Learn how multiple companies across a manufacturing value chain have securely collaborated on data while protecting the IP of any party, data, or model.

In this case study you will learn:

checkmark How a coating supplier increased output quality to consistently meet the OEM's targets
checkmark How an Industrial Solution Provider developed AI-features for its immersion heaters and implemented them at his customers' environment
checkmark How several automotive suppliers for high-performance plastics parts collaboratively developed AI models to predict material behavior
White Paper
Federated Learning on Vertically Distributed Healthcare Data
Joining complementary data sets creates stronger machine learning capabilities. But valuable data containing intellectual property cannot be shared and must adhere to regulatory frameworks. Healthcare companies can use federated learning to work with decentralized data, allowing secure collaboration without sharing data.

In this white paper, you will learn:

checkmark About vertically distributed data
checkmark Workflow of privacy-preserving data science
checkmark Deep-dive into privacy-preserving record linkage
checkmark Deep-dive into federated and privacy-preserving analytics
White Paper
Privacy-preserving Data Science on QSAR models
This case study shows the effectiveness of Apheris’ platform and services for data science on data that is not directly accessible and distributed. Federated learning decentralizes learning algorithms to access multiple data sets, maintaining privacy. Without pooling sensitive data, new insights can be uncovered securely.

In this white paper, you will learn:

checkmark How federated learning, privacy testing and other tools that enable data science on not directly accessible and distributed data
checkmark Sowcase of federated learning, privacy testing and other tools that enable data science practices on not directly accessible and distributed data, at the example of QSAR models.
White Paper
Privacy-preserving Data Ecosystems in Support of Drug Discovery
In recent years, Deep Learning has gained considerable traction in many fields, but none more so than in the realm of drug discovery. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.

In this white paper, you will learn:

checkmark How you can preserve intellectual property while generating value from sensitive data
checkmark Deep dive into federated and secure QSAR models
checkmark How Apheris prevents uncontrolled data sharing
checkmark How you can pave the way to precision medicine with federated and secure QSAR model training
Buyer's Guide to Secure Data Collaboration
Secure data collaborations with multiple parties are the next source of getting a competitive edge. However, only a few data leaders know what to look for to get projects going. This buyer's guide will help you evaluate platforms and highlight considerations for AI and analytics across organizational boundaries.

In this guide you will learn:

checkmark What is a Platform for Secure Data Collaboration
checkmark Why do you need it today
checkmark What should you look out for when choosing a platform
White Paper
Beyond MLOps - How Secure Data Collaboration Unlocks the Next Frontier of AI Innovation
DevOps and MLOps are common methodologies in every company that wants to become software and data science driven by weaving AI into the core fabric of their business. Read what is required to securely collaborate with partners on data and AI at scale.

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