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.
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.
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:
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.
So, how do you ramp up the process from model development to deployment?
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.
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.
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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