The advancements and research in big data and AI have led healthcare to increasingly adopt machine learning and predictive algorithms in their workflows. This field offers numerous opportunities for data scientists to explore ideas, conduct experiments, and develop new knowledge and models.
However, it is well-known that only a small fraction, approximately 10%, of these models actually make it to production where they can impact real-life business problems and processes. This lopsided ratio is difficult to change due to regulations, guardrails, biases, and a lack of mature testing frameworks. Nevertheless, data science teams face a new set of challenges when dealing with the models that do reach production. These challenges include:
In the past, when software engineering went through maturity cycles to address scalability, reliability, and maintainability, DevOps emerged as a framework of solutions. DevOps brought a set of practices for developing, testing, deploying, and operating large-scale software systems. It resulted in shorter development cycles, increased deployment velocity, and auditable, dependable system releases.
Likewise, MLOps acts as a framework of best practices and solutions at the intersection of DevOps, data engineering, and machine learning. While it shares similarities with DevOps in concept, MLOps differs in execution. ML systems are inherently experimental and involve more complex components to build and operate.
Integrating machine learning into administrative applications and clinical workflows in the healthcare industry presents unique challenges, including data privacy, data drift, regulatory compliance, and inherent bias. At Innovaccer, we recognize these challenges and have customized our MLOps approach to address each of them. Our goal is to ensure ease of model deployment, model interpretability, bias mitigation, and seamless integration in multi-client environments. We have developed an end-to-end ML platform that standardizes the key aspects of managing machine learning/deep learning models during the production phase.
Our platform's primary objective is to enable data scientists to concentrate on model development, without worrying about allocating infra resources, deploying and monitoring models, scheduling jobs, or engaging in redundant engineering activities.
Fig. 1: Our MLOps platform at Innovaccer performs over 80% of the tasks during the post-model build phase, freeing data scientists to optimize their workflows.
To simplify the MLOps process, we introduced a deployment template that data scientists can use to populate their model training and inference codes, as well as standardize their pipelines. This template reduces the burden of MLOps, simplifies replication in multiple client environments, and significantly reduces deployment time and cost.
Fig. 2: The process flow illustrates Innovaccer's MLOps workflow and task segregation between data scientists and MLOps engineering.
Innovaccer’s ML Platform: Accelerating Innovation, Enhancing Collaboration, and Improving Patient Outcomes
Our ML Platform expedites model deployment and iteration by integrating ML with DevOps practices. Streamlined deployment pipelines, automated updates, and containerization techniques ensure that insights from advanced models transition swiftly into real-world healthcare scenarios. By streamlining the process, we significantly reduce the time-to-production from three weeks to just three days.
Fig. 3: The Innovaccer ML Platform reduces engineering overhead for model deployment by 80%.
Our ML platform fosters collaboration between data scientists and engineering teams, combining DevOps principles and ML deployment best practices. This collaboration aligns innovative model designs with operational realities, resulting in feasible and efficient solutions for the healthcare ecosystem.
The Innovaccer ML Platform directly impacts patient outcomes by enabling data-driven decisions. Using MLOps, we extract actionable insights from vast healthcare datasets, providing evidence-based information to medical professionals. This empowers them to make informed decisions, enhance patient care, diagnose illnesses, predict health trends, and personalize treatment plans. Our solutions optimize interventions and care programs, leading to improved patient outcomes.
Innovaccer's Commitment to AI-Enabled Healthcare Innovation
Innovaccer is a leader in data-driven healthcare and understands that MLOps is an ongoing journey that must adapt to the evolving technological needs of the healthcare industry. Our ML Platform, which includes MLOps, exemplifies our commitment to pushing the boundaries of AI-enabled healthcare innovation while maintaining the highest standards of data privacy and clinical efficacy.
We have also launched our AI suite, Sara, which offers self-conversational tools to improve patient outcomes, strengthen financial performance, and engage providers and consumers. If you want to accelerate AI adoption in your HIT systems, schedule a demo with our experts to start your AI-led healthcare journey.