Machine learning: Accelerating your model deployment—Part one
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 use machine learning (ML) and artificial intelligence (AI) to learn from this data and harness it for predictive analytics. This shift, however, comes with significant challenges.
Overview
According to International Data Corporation (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, companies expend a large amount of time because they 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, but 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.
ML challenges
The most challenging aspect of ML is operationalizing developed ML models that accurately and rapidly generate insights to serve business needs. Some of the most prominent hurdles to this include the following:
- 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 run simultaneously across different environments with different datasets and hyperparameters. Keeping track of all these models and their associations can be challenging.
- Limited time to value ratio. You can develop models quickly, but 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.
- A DevOps model with excessive retraining. The DevOps model for application development doesn’t work with ML models because the need for retraining across a model lifecycle makes the standardized linear approach redundant. As data ages and becomes less usable, you need to use fresh datasets and train again and again.
The ML model lifecycle is fairly complex, starting with data ingestion, transformation, and validation to fit the needs of the initiative. You then develop, validate, and train a model. Depending on the length of development time, you might need to perform training repeatedly as a model moves across development, testing, and deployment environments. After training, you set the model into production, where it begins serving business objectives. Through this stage, you need to log and monitor the model’s performance to ensure suitability.
Acceleration from model development to deployment
So, how do you ramp up the process 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 SageMaker stack comes packaged with models for AI services such as computer vision, speech, recommendation engine capabilities, and models for ML services that help you deploy deep learning capabilities. Plus, it supports leading ML frameworks, interfaces, and infrastructure options.
Use the Rackspace Technology Model Factory Framework
In addition to employing the right toolsets, such as the SageMaker stack, you can only achieve significant improvements in ML model deployment if you consider improving the efficiency of these models' lifecycle management across the teams that work on them. Different teams across organizations prefer different sets of tooling and frameworks, introducing 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 use the tools they are comfortable with.
Next steps
Part 2 of this series explores 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!
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