This whitepaper surveys the challenges inherent in Machine Learning
deployment, how emerging trends of Microservices and Serverless
architecture can help, and why an AI Layer might be a great fit for your
Many companies today are struggling to answer a simple question: how should they deploy
machine learning models at scale?
72% of business leaders have indicated that they view AI as a business
More and more frequently, companies are creating internal initiatives for garnering a
competitive advantage using AI and ML. A number of developing trends are driving this
increased usability of Machine Learning in the coming years:
- Wide availability of data storage and compute power
- Improved and robust open source tools and frameworks
- A growing appreciation for algorithmic decision making.
Designing and deploying Machine Learning at scale is challenging, no matter the size of
your team. Data Scientists are simply not trained in the often overwhelmingly complex
discipline of deployment, or turning their models into scalable applications. The intricacies
of load balancing, event handling, and container management are a segment of the
Machine Learning pipeline in of themselves, and there’s no straightforward playbook for
how to make them work together.
If you want to see meaningful ROI on your Machine Learning investments and build a
competitive advantage this year, you’ll first need to solve this last mile deployment problem.