Machine learning: Accelerating Your Model Deployment – Part 1

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The value of data is tremendous and any business model requires data to drive decisions and make projections for future growth and performance. Business analytics has traditionally been reactive, guiding decisions in response to past performance. Leading companies have begun to utilize machine learning and artificial intelligence to learn from this data and harness it for predictive analytics. This shift, however, comes with significant challenges.

According to IDC, almost 30% of AI and ML initiatives fail. The primary culprits behind this failure are poor quality data, low experience, and challenging operationalization. Moreover, a large amount of time is expended as a result of repeatedly training ML models with fresh data through the development cycle, due to data quality degradation over time. Hence, ML models aren’t just difficult to develop, they can also be time-consuming.

Let’s explore the challenges presented when developing ML models and how Rackspace Technology’s Model Factory framework presents a solution that simplifies and accelerates the process and helps you overcome these challenges.

Machine Learning Challenges 

The most challenging aspect of machine learning is the process of operationalizing developed machine learning models which accurately and rapidly generate insights to serve business needs. Some of the most prominent hurdles to this are:

  • Inefficient coordination in lifecycle management between operations teams and machine learning engineers. According to Gartner, 60% of models don’t make it to production due to this disconnect.
  • A high degree of model sprawl, which is a complex situation where multiple models are run simultaneously across different environments, with different datasets and hyperparameters. Keeping track of all these models and their associatives can be challenging.
  • Models may be developed quickly, however, the process of deployment can often take months. Organizations lack defined frameworks for data preparation, model training, deployment, and monitoring along with strong governance and security controls, limiting time to value.
  • The DevOps model for application development doesn’t work with ML models because the standardized linear approach is made redundant by the need for retraining across a model lifecycle with fresh datasets, as data ages and becomes less usable.

The ML model lifecycle is fairly complex, starting with data ingestion, transformation, and validation so that it fits the needs of the initiative. A model is then developed and validated, followed by training. Depending on the length of development time, training may need to be performed repeatedly as a model moves across development, testing, and deployment environments. After training, the model is set into production where it begins serving business objectives. Through this stage, the model’s performance is logged and monitored to ensure suitability.

Acceleration from Model Development to Deployment


Rapidly Build Models with Amazon SageMaker 

Amazon SageMaker, a machine learning platform on AWS, offers a more comprehensive set of capabilities towards rapidly developing, training, and running ML models in the cloud or at the edge. The Amazon SageMaker stack comes packaged with models for AI services such as computer vision, speech, and recommendation engine capabilities as well as models for ML services that help you deploy deep learning capabilities. Plus, it supports leading ML frameworks, interfaces, and infrastructure options.

The Rackspace Technology Model Factory Framework 

In addition to employing the right toolsets, such as the Amazon SageMaker stack, significant improvements in machine learning model deployment can only be achieved if organizations consider improving the efficiency of the lifecycle management of these models across the teams that work on them. Different teams across organizations prefer different sets of tooling and frameworks, which can introduce lag through a model lifecycle. An open and modular solution, agnostic of the platform, tooling, or ML framework, allows for easy tailoring and integration into proven AWS solutions. This solution can mitigate this challenge while allowing teams to utilize the tools they are comfortable with.

In part 2 of this series, we will take a look at Rackspace Technology’s Model Factory Framework, which aims to provide such a solution, further accelerating the time to ML model deployment in production. If you’d like to see the Model Factory Framework in action and get a deeper look into how you can incorporate it into your ML initiatives, watch our on-demand webinar.

Are you interested in employing machine learning or artificial intelligence capabilities on AWS to derive insights from your organizational data? Get in touch with our data engineering and analytics experts today!

Hidden layer

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