August 2019CIOAPPLICATIONS.COM9Companies should build out machine learning capabilities with the end in mind, understanding the use cases and strategic objectives of ML deployment, and build a robust data pipeline with collaboration between data scientists, data engineers and ITon enterprise specific data is often the only way to create real competitive advantages. Moreover, that data is often collected and stored on-prem. Also, the hidden cost in running a public-cloud based machine learning environment can be quite high and hard to manage, when multiple data scientists are using the resources or when models need to be retrained and continuously trained to improve accuracy in real production environment. When it comes to inferencing, it's even more common to see it being deployed on-prem due to latency concerns. When the rubber meets the road for a full scale ML deployment, we see companies adopting hybrid cloud approach. Hence, to speed up ML deployment and ensure success, IT needs to ensure a smooth experience between public-cloud and on-prem environment for data scientists and engineers. One thing is to make sure that the ML software and platforms running on-cloud can also run on the on-prem IT environment. The other task is to create an ML-as-a-service experience on-prem. The first step is to create a multi-tenancy GPU-as-a-service environment through virtualization, so data scientists and data engineers can require dedicated ML infra resources, while it's shared and managed by IT efficiently. One other mistake people repeatedly make in ML deployments is the lack of an end-to-end data pipeline view. Instead, when talking about machine learning, especially deep learning, they often focus on one part of ML infrastructure only ­ the training infrastructure. Machine learning requires an end to end data pipeline, from data collection, data processing, to training, evaluation, deployment, and recollection. Operationalizing machine learning requires data engineers, data scientists and IT to look at how the data would come in and stored, where and how it will be cleaned, where and how it will be trained and where and how it will be deployed. If data comes from existing infrastructure, e.g., big data clusters, mission-critical workloads, it is critical to ensure that special deep learning training infrastructure is well integrated with existing infrastructure and can be managed as part of the standard data center infrastructure. Besides, Machine learning deployment often requires compute to follow where the data is. In many use cases, for example, in retail and manufacturing, the data center is no longer centered, it's distributed. Ability to manage the edge servers is critical for operational efficiency and success. Finally, companies should build out machine learning capabilities with the end in mind, understanding the use cases and strategic objectives of ML deployment, and build a robust data pipeline with collaboration between data scientists, data engineers and IT. Your organization's evolution is ready to begin! Zongjie Diao
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