Breaking the Barriers to AI Adoption: Overcoming Deployment Challenges

Do your machine learning (ML) models continue to fail? 

Do you, like many other businesses, encounter obstacles in deploying and operationalising your ML models?

And even though you’re 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. 

But you’re not alone in this struggle. 

According to The 2023 AI and Machine Learning Report, there are some common challenges preventing AI/ML success: 

  • 67% A shortage of skilled talent 
  • 61% Algorithm/model failure  
  • 54% The lack of technological infrastructure to support it 

It leaves your business unable to fully capitalise on the potential of those ML initiatives.

ML Model Deployment Failure Is Diverse and Complex

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

  1. A study by NewVantage Partners found that 77% of organisations face challenges in operationalising ML models and integrating them into their existing business processes.
  2. A report by Algorithmia reveals that 56% of organisations cite deploying ML models into production as one of their biggest challenges.

And some of the most common challenges include:

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

Poor Performance:

Sometimes ML models don’t perform quite as well as they do in a test environment. 

Organisations often find themselves at a dead-end when deploying in real-world scenarios compared to their performance during development and testing phases. 

And this challenge can occur due to a variety of reasons. Differences in data distribution, changes in data patterns over time, or the presence of biases in the data can all affect the performance of the model.

To help remedy this issue requires:

  • Monitoring the online performance of the model
  • Tracking summary statistics of the data
  • 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:

ML models often need to handle large volumes of data and process it efficiently. 

And so scaling them to handle growing data sets and increasing workloads can be a significant challenge. Which requires careful consideration of computational resources, optimisation of algorithms, and designing scalable architectures.

Organisations must ensure that their ML models can handle increased data volumes without sacrificing performance or incurring excessive costs.

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

Volume: when deploying multiple versions of the same model, you have to run the whole workflow twice, even though the first steps of ingestion and preparation are exactly identical.

Variety: when you expand your model portfolio, you’ll have to copy and paste code from the beginning stages of the workflow, which is inefficient and a bad sign in software development.

Versioning: when you change the configuration of a data source or other commonly used part of your workflow, you’ll have to manually update all of the scripts, which is time consuming and creates room for error.”

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.

Lack of Robust Pipelines:

Deploying ML models involves multiple stages. 

There’s data preprocessing, feature engineering, model training, and model deployment that all need to be considered.

And so constructing robust and efficient pipelines that handle these stages seamlessly is crucial for successful deployment and scaling. 

Having a manual, data-scientist-driven process that might work when models are rarely changed or trained, but can become problematic when models need to adapt to changes in the environment or data. Resulting in a lack of robust pipelines.

Organisations need to invest in building flexible and reliable pipelines that can handle data ingestion, processing, and model deployment efficiently, ensuring end-to-end automation and reproducibility.

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:

Integrating ML models into existing organisational systems can be a complex task.

ML models often need to interact with various data sources, databases, APIs, and other software components. However, compatibility issues, data format inconsistencies, and integration complexities can arise during this process. 

Organisations need to carefully plan and execute the integration of ML models into their existing systems to ensure smooth operation and avoid disruptions.

Workflow automation is necessary for different teams to integrate into the workflow system and implement testing. 

Integrating ML models into existing systems can also be challenging due to:

  • Team skills
  • Experimental nature of ML development
  • Testing requirements
  • Deployment complexities
  • Production challenges

Overcoming These Challenges

Organisations need to:

  • Establish a standardised process for taking a model to production
  • Ensure that the ML experimentation framework is flexible and adaptable to changes in the business environment.

As these obstacles hinder businesses from reaping the benefits of ML, there remains a constant battle with wasted time, resources, and missed opportunities for optimisation and automation.

Addressing these challenges requires a combination of approaches and specialised expertise.

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

We understand the value of an effective ML pipeline and the need for expert guidance to ensure the successful deployment of your models. 

That’s why we help you leverage the expertise of our top-tier ML engineers to help you conquer these challenges and ensure that your ML models take flight.

Overcome the hurdles that bring turbulence to your 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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