How to Prepare an AI Model For Marketplace Readiness and Success
Data scientists and machine learning engineers possess the unmatched expertise to develop sophisticated AI use cases. These models promise extraordinary results, including their ability to mirror aspects of human intelligence and add value to many industries’ applications.
However, after the model has been developed and it is time to bring it to the production stage, new rules apply. There is a significant difference between the research tools and approach used in model development and model deployment, which is the process of optimizing a deep learning model to prepare it for the marketplace performance.
The phases of AI deployment require most scientists and engineers to rely on additional engineering support from highly effective machine learning and deep learning AI specialists.
What Are the Phases of Machine Learning Model Deployment?
There are three phases of model deployment:
Model conversion
Model monitoring
Continuous improvement
When an AI model moves from research and development to production, engineers often experience challenges that require additional support. A model may run effectively on a robust computing platform; however, as a model moves closer to its destination as a product, it first requires optimizations for speed and size, which are typically addressed during the first phase of model deployment, called model conversion.
Model conversion is the process of converting an AI model from the various development frameworks that scientists and engineers use, to a framework that allows the model to work efficiently using any available hardware acceleration, this is typically useful on small form factor devices such as a phone, but it is also possible in on-prem or cloud applications. For example, an engineer may find that their use case operates effectively, but processing time is far too long. During model conversion, a team with expertise in this phase provides tools to ensure that a model’s processing time is nearly instantaneous.
Although a model may now run effectively for users, the work of deployment is not over yet. An AI model, whether it is used for generative AI or other deep learning use cases, must be monitored to establish its effectiveness over time during a process called model monitoring.
A model’s precision may experience drift as new variables come into play that affect its output. To use a general example, ChatGPT’s training only allows this model to provide information from its training, which is limited to content on webpages that were written prior to 2022. ChatGPT’s model requires monitoring and retraining to provide users with more accurate information after some months.
Much like any tech product, engineers will release new versions that improve upon the last. Today’s technology requires that engineers monitor their AI product’s use in real time and receive feedback that informs next steps to ensure the model continues to add value for users. This is called continuous improvement.
A model must be continuously updated to suit users’ needs and deliver quality performance. An engineering team doesn’t wait until a model is “perfect,” as it will always require updates. For example, ChatGPT continually adds new features and improvements without having to pull the program from the marketplace. In software, this process is called development operations (DevOps). Machine learning has adopted DevOps as MLOps, which is used to describe the continuous improvement process that an AI model must undergo for performance enhancement, even after marketplace deployment.
These phases don’t occur only once to solidify a model’s success. Deep learning development and deployment is an iterative process. An AI model may require further development to firmly establish its viability. Therefore, all deep learning models, including generative AI, exist in a cycle of development and deployment. Although your model is currently being leveraged by users, it should be continually monitored and further updated, so the next iteration is more successful than the last.
How Do Machine Learning Engineers Find Model Deployment Expertise?
Experience with model deployment is not the same set of skills that data scientists require for machine learning and deep learning. AI development engineers often seek out the support of a team that specializes in deployment since it requires a specialized knowledge at platform level.
Scientists and engineers who have developed a model and need support for deployment should look for these characteristics in a deployment team:
Experience with running an deep learning model on the leading system-on-chip hardware
Experience with deployment tools, including those used for model conversion, such as TensorRT and DeepStream
Experience with model optimization techniques such as pruning and quantization, among others.
Service offerings that include MLOps for continuous integration and deployment
A commitment to continuous improvement throughout the phases of deployment can help minimize risk and add value for customers. Evolution is an expected part of working with AI technology, and failing to embrace it could result in a missed opportunity for widespread adoption.
To learn more about the basics of AI, including machine learning and deep learning, read these articles that clarify essential topics in this growing field:
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