From lab to market: How MLOps and clinical trials share similarities in terms of the process

This article is mainly for those of you who are new to the field of Data Science, especially from an expertise on the processes of the Pharmaceutical world! Why I write this article for such a focused group of people? Head out to my About section to know more!

For those non-pharmaceutical audience, I've got you covered. Please read this article to understand how clinical trial works, so that you can also take part in this journey!

Now, gear up! Because it is story time!

Once upon a time, in the world of Machine Learning, there was a growing realization that the development process of ML models was not as simple as traditional software development. The process of building, deploying and maintaining ML models required a different approach, one that would ensure the models were not only accurate but also reliable and compliant. This approach was called MLOps.

Sounds familiar?

Here is a short blast from the past about Clinical Trials:

The history of clinical trials can be traced back to the 18th century, when James Lind, a British naval surgeon, conducted the first controlled clinical trial to test the effectiveness of different treatments for scurvy. He found that citrus fruits were effective in treating the disease and his work laid the foundation for the modern clinical trial. In the 19th and early 20th centuries, advances in medical research led to the development of more sophisticated clinical trials, including the use of randomized controlled trials and the introduction of placebo controls. The 20th century also saw the development of ethical guidelines for clinical trials, such as the Declaration of Helsinki, which established principles for the protection of human subjects in medical research. Today, clinical trials continue to play a crucial role in the development of new treatments and the advancement of medical knowledge.

Stage 1 : Define your goals and objectives

Clinical Trials

Clinical trial planning refers to the process of designing, organizing, and implementing a clinical trial to test the safety and efficacy of a new medical treatment or device. This includes tasks such as identifying and recruiting appropriate study participants, establishing inclusion and exclusion criteria, determining the appropriate sample size, and developing a study protocol. For example, a pharmaceutical company may conduct a clinical trial to test a new drug for the treatment of cancer, and the goals and objectives of this trial would be to determine whether the drug is safe and effective for treating the disease.

MLOps

Machine learning goals and objectives refer to the specific targets or outcomes that an organization or individual aims to achieve through the use of ML techniques. These goals may include tasks such as image or speech recognition, natural language processing, or predictive modeling. For example, a company may use ML to improve its customer service by developing a chatbot that can understand and respond to customer inquiries in natural language

Stage 2: Setting up the infrastructure

Clinical Trials

Setting up the infrastructure for a clinical trial typically involves organizing and coordinating the various aspects of the trial, including recruiting and enrolling participants, collecting and managing data, and monitoring the safety and efficacy of the treatment being tested. This process may involve several different organizations, including the sponsor of the trial, the institutional review board (IRB) responsible for overseeing the trial, and the clinical trial site where the trial is being conducted. The infrastructure may include tools such as electronic data capture systems for data collection, and safety monitoring software to ensure the safety of the trial participants.

MLOps

The infrastructure typically includes a combination of hardware and software resources that are used to develop, test, deploy, and monitor machine learning models. This may include resources such as cloud computing services, data storage systems, version control systems, and monitoring tools. For example, an organization may use Amazon Web Services (AWS) for cloud computing, Git for version control, and Prometheus for monitoring. Additionally, MLOps teams may also use tools such as Kubernetes or Docker to manage the deployment and scaling of their models.

Stage 3: Development & Testing

Clinical Trials

There is no development once the clinical trial process begins;