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The Domino Effect: How Neglecting Production Early Derails AI Development

Part 1 in our Practitioners Guide Series. Download the full ebook here.

Shockingly, only 53% of AI projects progress from prototype to production, with a mere 10% achieving ROI. That’s a startling statistic, especially considering the high investments companies are making in AI technology.

Your primary goal with AI is likely to develop models that provide value, whether it’s solving a business problem, improving efficiency, or enhancing user experience. If only a fraction of your projects progress from prototype to production and achieve significant ROI, it means a substantial portion of your efforts may go to waste.

So what is causing this gap between initial prototype and successful implementation?

One key reason is the lack of focus on production early in the project. Many teams spend most of their time and resources on developing and fine-tuning the AI model, neglecting to consider how it will be deployed and integrated into production systems. This lack of planning and attention to production can create a domino effect, ultimately derailing the entire AI project. 

Let’s take a closer look at some of the ways this can happen.

Failure to Evaluate Production Needs

Too often, teams get caught up in the excitement of developing an AI model and neglect to evaluate the practical considerations for production. Questions like “How will this model be deployed?” and “What infrastructure is needed for optimal performance?” are often overlooked until it’s too late.

Transitioning ML models from development to production can be challenging, often taking weeks or months, the fact that often comes as a surprise  to many teams.  Processes like model deployment, validation and checking, monitoring and observability are all critical pieces that need to be carefully planned and considered from the beginning stages of a project.

Without foresight and planning, teams may find themselves struggling to integrate the AI model into production systems, leading to delays and ultimately, failure.

Key points:

  • Limited consideration of production needs during development contributes to this low success rate.
  • Early-stage focus on model development and data engineering often overlooks downstream infrastructure and deployment expertise.

Siloed And Complex Process

The teams that develop the models and those responsible for deploying and integrating them into production systems are often two different groups with different backgrounds, skill sets, and priorities.

Moreover, AI teams often operate under resource constraints, including limited time and budget. Without involvement from production experts early on,  production considerations may be deprioritized in favor of focusing on model development, leading to neglect of critical aspects that are essential for project success in the long run. 

In such cases, potential challenges related to deployment and scalability go unnoticed until later stages, leading to significant setbacks, delays, errors, and ultimately, failure of the entire project.

To add even more complexity, AI development often follows an iterative process, with models being refined and improved over time. However, if production considerations are not revisited and refined iteratively throughout the development cycle, teams may encounter difficulties when deploying models into real-world environments.

One way of solving these issues is by equipping data scientists with the tools and knowledge necessary for model deployment, organizations can streamline the transition from development to production.

This approach not only reduces the bottleneck typically experienced by AI engineering teams but also fosters a more collaborative environment. As a bonus, data scientists gain a better understanding of the production landscape, which then leads to more efficient and scalable models.

Key points:

  • Empowering data scientists to deploy models independently can alleviate pressure on AI engineering staff.
  • Leveraging existing organizational knowledge and aligning with business stakeholders can enhance the efficiency and success of AI initiatives.
  • Neglecting iterative refinement of production considerations can lead to significant setbacks and failures in project deployment.

Understanding the Domino Effect of Neglecting Production

Imagine a line of dominoes meticulously set up, each one reliant on the other for stability and progression. Similarly, in AI development, neglecting production considerations early-on can set off a chain reaction of setbacks, hindering the entire project’s trajectory.

  • Scalability Challenges: AI models optimized solely for development environments may struggle to scale effectively when deployed into production. Neglecting production considerations early-on can result in models that are inefficient or resource-intensive, impeding scalability and hindering performance.
  • Performance Issues: Production environments often have different requirements and constraints compared to development environments. Failure to address these differences can lead to performance issues such as increased latency or reduced throughput, undermining the user experience and overall effectiveness of the AI solution.
  • Resource Drain: Without early consideration of production needs, AI teams may find themselves grappling with unforeseen resource requirements or operational complexities during deployment. This can drain valuable time, effort, and resources that could have been allocated more efficiently with proper planning.
  • Team Frustration and Demotivation: As challenges stemming from neglecting AI production early-on mount, team morale may suffer. Frustration and demotivation can set in as team members grapple with unexpected obstacles and setbacks, leading to decreased productivity and engagement.
  • Low Success Rate of Projects: As we discussed, neglecting production considerations early in the development cycle significantly impacts the success rate of AI projects. Without proper planning for deployment and scalability, projects may fail to progress beyond the prototype stage, resulting in wasted resources and efforts.

Mitigating the Domino Effect with LowOps Production Platforms

To avoid the potential domino effect of neglecting production considerations, companies can leverage LowOps AI production platforms, such as Wallaroo.AI. These platforms provide a high level of automation and scalability, requiring minimal manual intervention from developers or operations teams.

Key features of LowOps production platforms include:

  1. Automated Deployment: These platforms automate the deployment process, reducing the risk of human error and increasing efficiency. This ensures smooth deployment of AI models into production environments.
  2. Scalability: With built-in scalability features, LowOps platforms can handle increased demand for AI solutions without sacrificing performance or user experience. This enables companies to seamlessly scale their AI projects as needed.
  3. Monitoring and Maintenance: LowOps platforms offer robust monitoring and maintenance capabilities, allowing teams to quickly identify and fix any issues that may arise during deployment. This minimizes downtime and reduces the need for manual intervention.
  4. Cost-Efficiency: By automating many operational tasks involved in deploying AI models, LowOps platforms help reduce costs associated with production environments. This frees up resources that can be allocated towards further innovation and development.
  5. Early Collaboration: Foster collaboration between AI development and production teams from the project’s inception. By involving production experts early-on, potential challenges can be identified and addressed proactively, minimizing the risk of future setbacks.
  6. Holistic Approach: Take a holistic view of AI development, considering not only the technical aspects but also the operational and deployment requirements from the outset. This ensures that the entire AI lifecycle, from development to deployment and beyond, is accounted for and optimized.
  7. Iterative Improvement: Embrace an iterative approach to AI development that prioritizes continuous improvement and adaptation. By regularly revisiting and refining production considerations throughout the development cycle, teams can mitigate risks and respond effectively to evolving requirements.

The Bottom Line

AI is not just about the algorithms – it’s also about how those algorithms are deployed, managed and scaled in production environments. With the right approach, organizations can ensure that their AI initiatives deliver real value to their business and customers. So don’t neglect your production considerations, embrace them from day one! 

Reach out if your team is looking to start getting models into production, or if you already have a few models in production and are looking to scale.

Download the Full Practitioners Guide Ebook

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