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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.


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.


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; whereas the concept of testing is where its more analogous to that of MLOps. However one can associate the drug development process with the development phase of MLOps, where several drugs are tested via simulations and on a trial-and-error basis.

During this phase, the trial is conducted according to a protocol, which is a detailed plan that specifies how the trial will be conducted, including the inclusion/exclusion criteria, the interventions, the study population, and the outcome measures.

The testing phase is focused on assessing the safety and efficacy of the treatment or intervention being tested. This includes tasks such as randomly assigning participants to treatment and control groups, collecting and analyzing data on the outcomes of the treatment, and comparing the outcomes of the treatment group to those of the control group.


The development and testing phases involve the use of various machine learning (ML) algorithms and techniques to build and evaluate models. This includes tasks such as selecting and preparing data, training and evaluating models, and fine-tuning and optimizing model performance. During this phase, MLOps teams use tools such as Jupyter Notebook, Python libraries (such as TensorFlow and scikit-learn), and version control systems (such as Git) to develop, test and iterate on the models. Additionally, MLOps teams may also use techniques such as cross-validation, hyperparameter tuning, and A/B testing to improve the performance and generalization of the models.

Stage 4: Deployment

Clinical Trials

Post Phase 3, marketing a drug in clinical trials involves the process of taking a drug that has been tested in a clinical trial and making it available for use by patients. This process typically involves several stages, including obtaining regulatory approval for the drug, obtaining reimbursement for the drug from payers, and marketing the drug to healthcare providers and patients. The process of marketing a drug in clinical trials is closely regulated by government agencies such as the Food and Drug Administration (FDA) in the United States, and the European Medicines Agency (EMA) in Europe.


It involves the process of taking the machine learning models that have been developed and tested and putting them into production. This includes tasks such as building and packaging the models, deploying the models to a production environment, and monitoring the performance of the models in production. During this phase, MLOps teams may use tools such as Kubernetes or Docker to manage the deployment and scaling of their models, and monitoring tools such as Prometheus or Datadog to track the performance of the models.

Stage 5: Monitoring & Maintenance

Clinical Trials

Post-market surveillance in the pharmaceutical industry refers to the ongoing monitoring of the safety and efficacy of drugs after they have been approved and marketed. This process is typically carried out by regulatory agencies such as the Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) in Europe. Post-market surveillance activities may include monitoring adverse event reports, conducting observational studies, and monitoring prescribing patterns. The goal of post-market surveillance is to identify and address any safety concerns or efficacy issues that may arise after a drug has been approved and marketed.


Monitoring refers to the process of observing and tracking the performance of machine learning (ML) models in production. This includes tasks such as collecting and analyzing data on the performance of the models, such as accuracy, precision, recall, F1-score, and identifying and addressing any issues that may arise. MLOps teams may use monitoring tools such as Prometheus or Datadog to track the performance of the models, and they may also use techniques such as A/B testing to track performance over time.


Just as the clinical trial process is regulated by strict guidelines, MLOps also follows strict guidelines to ensure the models are compliant with industry regulations. This includes ensuring that the models do not discriminate against certain groups of people, and that personal data is kept confidential.

In conclusion, MLOps is more analogous to the clinical trial process than one might think. Both processes involve developing, testing and deploying new products, with a focus on ensuring they are reliable, accurate, and compliant. By understanding the similarities between the two, organizations can apply the best practices from the clinical trial process to their MLOps process, resulting in better-performing and more reliable models.

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