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Successfully Rolling Out Enterprise-wide MLOps

Successfully Rolling Out Enterprise-wide ML Ops

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Organizations are becoming adept at launching projects that test their ability to use data,
analytics, Machine Learning (ML), and Artificial Intelligence (AI). Data scientists tinker with
data sets and analytical models, providing their organizations with the ability to understand
trends, test decisions, identify new opportunities, sharpen marketing programs and shape
recruitment strategies. These highly-trained data science teams can build sophisticated
systems. They can identify missing data values. And they know when their models are going
awry. However, data scientists often fail when rolling out and propagating their systems for
use by teams across the organization.

The failure can be attributed to several reasons. For example, a home insurance
organization’s data science team may be using property prices that are not relevant anymore.
In production, this model will fail because the data sets required are different. Further, the
data used in the lab may be limited. The model may become difficult to scale or degrade with
time in real-life applications. Or an organization may feel the process of organization-wide
adoption involves multiple teams, which can become challenging to manage. Every large
organization has experienced the pain of moving projects from data labs into practical
enterprise environments. Before the transition to enterprise-wide usage, there are many
challenges to overcome.

To successfully productionize and roll out stable enterprise-wide MLOps, an organization
should establish standards. These standards could include the data infrastructure required
for the ML lifecycle, data engineering methodologies, ML model engineering, testing, code
library/ scalability, model governance, security, tools for MLOps teams, etc.

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Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indicationsUtilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes