Mastering the Last Mile, Leveraging ModelOps for Seamless Model Deployment and Value Creation

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The consensus is growing that operationalizing models, rather than developing them, is today's most significant hurdle for data science. Production deployment techniques are generally ad-hoc. Application integration, model monitoring and tuning, and workflow management are often afterthoughts. This phase, often called the "last mile" of analytics, is where data science meets production and where organizational value is created—or lost.

Achieving the goal of a model-driven business that deploys and iterates models at scale requires something that only a few companies have: ModelOps.

"Only about 50% of models are ever put into production." - SAS

Introduction to ModelOps

ModelOps is a holistic approach to quickly and iteratively progressing models through the analytics lifecycle. ModelOps focuses on the application development community's DevOps approach. While DevOps emphasizes application development, ModelOps is about getting models from the lab to validation, testing, and deployment as quickly as possible while ensuring quality results.

ModelOps encompasses culture, processes, and technology, enabling seamless, efficient, and continuous development and deployment of models. It ensures analytics delivers on its promise by facilitating frequent, regular deployments from the data science team to production.

In the race to derive value from AI models, ModelOps is a critical methodology that only a few organizations are utilizing. Businesses increasingly rely on machine learning (ML) models to turn massive volumes of data into actionable insights, leveraging vast amounts of unstructured data to identify patterns for predictive purposes.

"ModelOps industrializes the use of models in your organization so you can reap the full benefit of AI."

Benefits of ModelOps

With ModelOps, enterprises can:

• Deploy new or updated models more quickly
• Monitor model performance across platforms
• Govern models to ensure compliance with regulatory and business risk requirements
• Automate ModelOps processes to drive lean, agile, and best engineering practices

Key Considerations for ModelOps

As part of your ModelOps efforts, it's crucial to accurately assess the data sources and variables used by your models to answer questions such as:

• What data sources will you use?
• Are you comfortable telling your customers a decision was made based on this data?
• Are you addressing model bias?
• How frequently are data fields edited?
• Can you replicate feature engineering in your production environment?

However, model development and deployment are complex, with only about 50% of models ever reaching production. Moreover, those that do often take at least three months to be deployment-ready, resulting in high operational costs and slower time-to-value. All models degrade over time, and without regular attention, performance suffers. Analytical model performance involves multiple variables, including model construction, data quality, fine-tuning, frequent updates, and retraining.

Common Challenges Addressed by ModelOps

1. Time to Deploy: ModelModel development and deployment can be lengthy. Identify your organization's cycle time, set benchmarks, and measure improvements. Break the process into steps, then measure and compare projects to identify best/worst practices. A model management solution could automate some activities.
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2. Degradation:Model bias can significantly impact performance. Creating a robust approach to model stewardship within your organization, where everyone takes ownership of model integrity, can resolve these issues before they affect your bottom line.

The Importance of Embracing ModelOps

Leveraging predictive models, big data, and increased computational power will continue to be a competitive advantage for smart organizations. Those who fail to embrace ModelOps will face growing challenges in scaling their analytics and fall behind the competition.

How eBlocks Software Can Help You

At EBlocks Software, we understand the challenges of producing models in an enterprise environment. We build cross-functional, multidisciplinary teams to create high-quality, scalable solutions for your toughest challenges. Our approach combines agile, lean, and continuous delivery methodologies to deliver smart solutions, building quality into the delivery process. Our partnerships with AWS, DataRobot, and the Wits Institute of Data Science enable us to serve you holistically.

We provide an end-to-end solution, giving you peace of mind that a trusted technology partner is meeting your objectives. Reach out to us to learn more by visiting our website www.eblocks.co.za or sending us an email at sales@eblocks.co.za.

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