Provectus Delivers MLOps Platform on AWS for Global Healthcare Leader

• Analytics & Modeling - Machine Learning
• Platform as a Service (PaaS) - Application Development Platforms

• Cement
• Pharmaceuticals

• Maintenance
• Product Research & Development

• Construction Management
• Infrastructure Inspection

• Data Science Services
• Training

The client, a global healthcare leader based in the United States, was looking to accelerate and scale the adoption of AI/ML across its organization. The company generates substantial amounts of data from various sources, including customer interactions, sales transactions, social media activity, and product usage. However, without a robust Machine Learning Operations (MLOps) platform, it was a challenge for the organization to effectively scale and manage their AI/ML workflows. This resulted in inefficiencies, increased costs, and slower time-to-market for new products. The client’s data scientists and ML engineers were looking for ways to simplify the deployment of AI/ML into production environments, particularly when using MLOps practices and the Amazon SageMaker suite of services. The client was transitioning from legacy infrastructure, but its engineers could not access and discover the unified and integrated workloads quickly and efficiently enough to meet the company’s vision for AI transformation.

The client is a global healthcare leader based in the United States, operating in the Consumer Health, Medical Device, and Pharmaceutical domains. Established in the late 1800s, the company employs over 130,000 workers, worldwide. The company is well known in the consumer health market, with an ongoing commitment to innovation, social responsibility, and improving global health. Like many enterprises, the company generates substantial amounts of data from various sources, including customer interactions, sales transactions, social media activity, and product usage. AI/ML applications are able to transform this data into actionable insights.

Provectus, an AI-consulting company, was chosen as a strategic partner to assist the client’s engineering team in building and scaling the platform, and in implementing and productionalizing a selection of AI/ML applications. Provectus used its MLOps Platform as a foundation for the client’s solution. An AI/ML application was implemented and selected for productionalization on the MLOps Platform. The AI/ML project template, which included the components for experimentation, an ML model training and inference, and CI/CD, was prepared as a blueprint for future projects. Provectus provided comprehensive documentation for the client to onboard its data scientists and ML engineers. The Provectus team began AI transformation with the implementation of an MLOps platform and the development of an AI/ML solution for its next purchase prediction. This approach enabled them to showcase the platform’s features and benefits in a real-world scenario.

• With assistance from Provectus, the client was able to start using their new MLOps platform in a matter of months, to optimize and streamline the operationalization of AI/ML projects. The MLOps platform and accompanying templates offered the client’s ML engineering team a convenient foundation for initiating the productionalization of new AI/ML projects and onboarding existing AI initiatives. The introduction of these innovations significantly accelerated the time-to-market value of the client’s AI/ML projects. The comprehensive documentation of the MLOps platform, seed code implementation and deployment, as well as AI/ML project onboarding guides prepared by Provectus, significantly reduced the entry barrier to understanding existing solutions and the development of new AI/ML solutions and projects. These deliverables from Provectus have empowered the client to develop and deploy future AI/ML applications more rapidly, efficiently, and at a larger scale. The delivered solution facilitates data-driven decision-making, leading to greater business success and a competitive advantage in the market.

• The amount of manual work required to set up infrastructure for AI/ML projects was significantly reduced or nearly eliminated.

• The costs of Proof of Concept development were reduced by minimizing the need for additional personnel such as software and DevOps engineers.

• The infrastructure approval process for AI/ML project development was expedited due to the standardization provided by the MLOps platform.