Breaking the Barriers to AI Adoption: ML Deployment

Many businesses encounter significant obstacles when it comes to deploying and operationalising their machine learning (ML) models. 

Despite investing time, resources, and effort into developing these models, they often fail to deliver the expected results. 

Which can be a frustrating and demoralising exercise. 

It ends up leaving organisations unable to fully capitalise on the potential of their ML initiatives.

The reasons for ML model deployment failure are diverse and complex

According to a variety of sources, many organisations struggle with the development, deployment and scalability of their ML models:

  1. 77% of organisations face challenges in operationalising ML models and integrating them into their existing business processes.
  2. 56% of organisations cite deploying ML models into production as one of their biggest challenges.
  3. Through 2022, 85% of AI projects failed to deliver the desired results due to the lack of operationalisation.

And some of the most common challenges include:

  • Poor performance 
  • Scalability issues
  • Lack of robust pipelines
  • Difficulties in integrating models into existing systems

These obstacles hinder organisations from reaping the benefits of ML, resulting in wasted time, resources, and missed opportunities for optimisation and automation.

Poor Performance:

ML models can suffer from reduced performance due to suboptimal coding and constantly evolving data profiles. 

This requires monitoring the online performance of the model, tracking summary statistics of the data, and sending notifications or rolling back when values deviate from expectations.

Actively monitoring the quality of the model in production can help detect performance degradation and model staleness.

Scalability Issues:

According to DataRobot, when trying to scale a monolithic architecture, three significant problems arise: volume, variety, and versioning.

A pipelining architecture can help address these issues by allowing the use of different parts of the workflow when needed and caching or storing reusable results.

However, the changing nature of ML models means that current CPU architectures may not provide the most optimal methods of execution. Which requires the development of new architectures for more efficient execution. 

Lack of Robust Pipelines:

A lack of robust pipelines can result from having a manual, data-scientist-driven process that may be sufficient when models are rarely changed or trained. But can become problematic when models need to adapt to changes in the environment or data.

To address these challenges, MLOps practices for Continuous Integration/Continuous Deployment (CI/CD) and Continuous Training (CT) can help by deploying an ML training pipeline that enables rapid testing, building, and deployment of new implementations of the ML pipeline.

Difficulties Integrating Models into Existing Systems:

Integration risks can arise when there is a lack of standardised technology stacks, making integration a challenge.

Workflow automation is necessary for different teams to integrate into the workflow system and test. Integrating ML models into existing systems can also be challenging due to team skills, experimental nature of ML development, testing requirements, deployment complexities, and production challenges.

To overcome these challenges, organisations need to establish a standardised process for taking a model to production and ensure that the ML experimentation framework is flexible and adaptable to changes in the business environment.

Addressing these challenges requires a combination of robust pipelines, monitoring, effective integration, and scalable architectures. 

By implementing MLOps practices and focusing on continuous improvement, organisations can enhance their ML model deployment success and overcome common obstacles.

How To Get Your ML Models To Soar

At Teraflow, we recognise the value and need for expert guidance to ensure the successful deployment of your ML models. Our solution lies in leveraging the expertise of our skilled ML engineers to help you overcome these challenges.

And ensure that your ML models take flight.

Our team of ML experts collaborates closely with your organisation, understanding your specific objectives and requirements. We work to understand your existing workflows, identify bottlenecks, and design robust ML pipelines tailored to your needs. 

Overcome the hurdles that impede ML model deployment. Our team of experts are waiting to help you harness the full potential of ML, transform your business operations and unlock the competitive advantages that AI offers.

Don’t let deployment challenges hold you back from reaping the benefits of ML.

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