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7 Key Components for Successful Model Deployment

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Stuart Swartz
artificial intelligence machine learning computing data into understanding through barriers

Artificial Intelligence (AI) and Machine Learning (ML) have never been hotter, and that trend shows little sign of slowing down in the foreseeable future.

The emergence of OpenAI and other generative AI technologies has further whetted the appetite of organizations everywhere, and immense opportunity still exists in predictive analytics, deep learning, and computer vision use cases.

The AI market size is expected to surpass $500 billion in 2023 and is expected to reach $2.5 trillion in 2032, with a 19% Compound Annual Growth Rate during that time. Despite the continued investments being made, many organizations continue to struggle to achieve business value from AI/ML. According to MIT Sloan Management Review, only one in ten companies achieves significant financial benefit from implementing AI.  

A key impediment is that a production AI/ML solution that achieves business objectives requires far more than the models themselves. 

A model is to AI/ML as a CPU is to a personal computer. Although they are the heart of the system, a model won’t work in production without ways to reliably deploy, invoke, and monitor the models, just as a personal computer will not work without a power supply, motherboard, and memory.

Google estimates that AI/ML models account for only 5% or less of the overall code in a production system.

Failure to account for that level of complexity results in numerous challenges in getting models out the door and into production efficiently, with Gartner reporting that only 53% of AI/ML projects make it from prototype to production.  

Diagram from Google for Developers on Production ML Systems
Diagram from Google for Developers on Production ML Systems

Key components for successful model deployment

Although the terminology may differ from person to person, these seven components are critical to putting models successfully into production and realizing true business value. Leverage the questions paired with each component to explore your current capability to move the model from idea to production and to achieve business value.

Data ingestion

All analytics use cases require one thing… data.

  • How quickly do we need the data? Is daily sufficient, or do we need hourly or even real-time?
  • Is change data capture (CDC) something we need to account for?
  • Is there a secure and scalable connection between where the data originates and where it is headed?

Data transformation

Data ingested often does not conform to a format that is easily usable for analytics workloads.

  • How are we formatting and storing the data to support analytics use cases?

  • Do the transformations need to be batch-based or near real-time?

Data validation

Having quality data is just as important as having data in the first place.

  • Do we have built-in checks and processes in place to monitor when data ingestion or transformation returns results that are not expected? 
  • If there is an issue with ingestion or transformation, how are the right people informed?

Feature engineering

Generating features is a time-intensive process. A repeatable process to convert raw data into desired features to use as inputs to train a predictive analytics model is necessary to create good models.

  • Is there a repeatable process to extract meaningful features from raw data or from other features? 
  • Can features be selected for use when training a model?

Model serving infrastructure

Models need to generate predictions, and those predictions need to be provided to some downstream process to realize a desired business outcome.

  • Is batch-based invocation sufficient, or is there a need for an endpoint that can generate inferences in real time? 
  • How are predictions provided to downstream systems or processes? 

MLOps​

Reliably moving models into production and monitoring for abnormalities is key to ongoing operations and maintenance of production models.

  • When a model is ready to use, how is it deployed out to production?
  • What processes are in place to detect model drift and potentially trigger a training job to retrain the model?

Orchestration​

Getting models from idea to production requires a wide variety of steps and tasks. Meaningfully scaling the usage of predictive analytics requires the automated execution of many different steps.

  • From ingestion to providing model inferences to downstream use cases, how is the end-to-end workload executed?
  • What automation is in place to ensure little to no manual intervention?

Getting models to production is key to realizing business value

If your organization is looking to begin its AI/ML journey or has struggled to realize business value from its AI/ML investments, Pariveda can help you navigate the way.

If you’re looking to use AWS, Pariveda has an AWS Data Engineering Accelerator that includes ready-to-deploy templates and can move a predictive analytics use case from idea to production in less than twelve weeks. For a broader approach to becoming a more data-driven organization, check out Pariveda’s Modern Data Enterprise framework.

Stuart Swartz Profile Picture
By Steven Muschler
Manager
Chicago Office
Stuart Swartz is an experienced leader guiding teams in implementing complex cloud-based solutions in a breadth of problem spaces across a range of industries. Stuart's work experience includes clients from industries spanning health insurance, consumer energy, financial services, and fleet management. Stuart's sharp technical acumen, combined with years of experience, enables Stuart to provide clients with valuable solutions tailored to their specific needs.
By Stuart Swartz
Manager
Chicago Office
Stuart Swartz is an experienced leader guiding teams in implementing complex cloud-based solutions in a breadth of problem spaces across a range of industries. Stuart's work experience includes clients from industries spanning health insurance, consumer energy, financial services, and fleet management.

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