Locust

"Just as athletes can't win without a sophisticated mixture of strategy, form, attitude, tactics, and speed, performance engineering requires a good collection of metrics and tools to deliver the desired business results."-- The current trend of leveraging the powers of ML in business has made data scientists and engineers design innovative solutions/services and one such service have been Model As A Service (MaaS). We have used many of these services without the knowledge of how it was built or served on web, some examples include data visualization, facial recognition, natural language processing, predictive analytics and more. In short, MaaS encapsulates all the complex data, model training & evaluation, deployment, etc, and lets customers consume it for their purpose. As simple as it feels to use these services, there are many challenges in building such a service e.g.: how do we maintain the service?