Quantori blog

January 10, 2024

QFlow for Managing Machine Learning Projects in Life Sciences

In recent years, the rapid growth of AI technologies has resulted in more organizations adopting machine learning techniques to solve complex business problems. However, the process of building and deploying ML models at scale can be challenging, involving numerous complex steps such as data preparation, feature engineering, model training, and deployment.

QFlow is a tool designed to address these challenges. Like popular frameworks such as Kedro, Flyte, and SageMaker, QFlow provides an opinionated structure to develop, maintain, and scale ML projects. It is useful in setting up pipelines to do the prework that makes the data ready for additional uses in multimodal machine learning applications.

In the following White Paper, Alexander Knop, Quantori Principal Mathematician, describes the key features and benefits of QFlow, along with examples and best practices. To provide a glimpse of how QFlow can accelerate your life science project, we’ve outlined its key advantages below:

1. Code Quality and Reproducibility

Using QFlow for project development instead of notebooks (like Jupyter) enables proper testing of the code with standard Python tools.
QFlow allows fixing the project structure to simplify the onboarding of new developers.

2. Automation of Common ML Tasks

QFlow automates various common ML tasks such as creating dashboards or UI for scientists, saving development efforts.

3. Team Collaboration

QFlow enables easy tracking of experiments, making it simpler for teams to collaborate.
Its Python-based domain-specific language (DSL) is more readable and easier to understand for non-technical stakeholders
and allows organizing multiple steps into workflows in a readable way.

4. Flexibility and Customization

QFlow allows deployment of unique tools specified for processing life science data.
QFlow enables the organization of data to simplify migration and addition of new datasets.

5. Scalability

The framework enables easy scaling of projects from their initial requirements and smooth adaptation to changes.

In the White Paper, you will also find a typical workflow using QFlow, along with technical details for defining and executing pipelines. To delve deeper into QFlow and its advantages, click here.

Data Science

Do you have any thoughts or questions?

We are looking forward to discussing this article with you. Fill out this form or reach out to contact@quantori.com

Please note that by submitting this form, you consent to Quantori processing your personal data as outlined in Data Privacy Policy

This site is protected by reCAPTCHA Enterprise and the Google Privacy Policy and Terms of Service apply

Artificial Intelligence

Revolutionizing Drug Discovery with Multimodal Data and AI: A Deep Dive into Use Cases

In the rapidly evolving landscape of drug discovery, the fusion of multimodal data with artificial intelligence (AI) and machine learning (ML) technologies is setting the stage for groundbreaking advancements. This synergy is not only expediting traditional processes but also paving the way for innovative approaches to complex, man-power intensive challenges. Here, we delve into four transformative use cases, illustrating the power of this integration in revolutionizing drug discovery.

Data Science

Approaches to Multi-Omics Data Integration

Multi-omics analysis is expected to significantly impact biomedical research, leading to a new era of personalized medicine with improved diagnostic and treatment options for patients. This perspective comes from the fact that biological processes, like disease progression, involve complex interactions of biomolecules across various omic layers.

Sustainability

Embracing ESG Principles as a Stakeholder in Life Science and Healthcare

In today's fast-paced world, sustainability is not just another catchword – it is now one of the main global priorities. Companies across industries are taking a closer look at their practices to ensure they align with ESG - Environmental, Social, and Governance - principles.

Data Science

How to Dig up More Data for Your Medical Research

No matter how hard you try to progress your research, without enough data, you’ll probably get stuck at some point. This is particularly crucial in the healthcare sector. To extract therapeutic insights from medical records, researchers typically need to work with many hundreds to thousands of patient cases, gathering and processing data from various sources. When the initial evidence at their disposal is limited, the conclusions may become distorted and biased.

Data Science

RWD and RWE Part 2: Challenges and Solutions

We have described RWD and RWD in our previous blog. There is strong promise and increasing use of Real World Evidence to accelerate data-driven decisions, improving not only the clinical trial experience, but healthcare in general. But, can we believe Real World Evidence?