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Equinix 2021 Predictions: 3 Trends That Will Drive Artificial Intelligence Architectures

vmblog 2021 prediction series 

Industry executives and experts share their predictions for 2021.  Read them in this 13th annual VMblog.com series exclusive.

3 Trends That Will Drive Artificial Intelligence Architectures

By Kaladhar Voruganti and Doron Hendel of Equinix

There's no doubt that business adoption of artificial intelligence (AI) is accelerating and will continue to do so into the new year. Challenges remain, however, as companies look to deploy AI prototypes at scale. Whereas a proof-of-concept in a lab or public cloud may only use a few data sources, a typical AI/Analytics application in production will use many external data sources. And with these data sources generating increasingly larger data sets at the digital edge, important decisions need to be made around operationalizing where compute power is placed, how data will be curated and governed and how AI models will be trained and improved, including who to partner with for data sharing. In order to solve these challenges and meet changing company needs, we have identified three key trends that are increasingly driving AI architectures:

Trend 1: Data sharing is essential for AI model accuracy

AI algorithms are only as good as the data used to build them and usually need additional external data sources for more precision and contextual awareness. For example, an AI model built to predict the spread of COVID-19 in a densely populated city like Singapore won't work well for a large, rural area in the U.S. Additional local data such as climate, demographics, testing status, healthcare system and more must be applied to the AI model for it to provide more accurate predictions. Data sharing between organizations is essential for this to work well but it can be challenging due to data governance and privacy concerns.  Confidentiality requirements vary depending on what data is shared, and this is leading to different types of data sharing models such as the three examples shown below.

 

Bring data to compute: This data sharing model, currently the most common form of data sharing, is typically used for non-sensitive data. In this model, data providers send their data to a public data marketplace in the cloud for sharing with data consumers. Some enterprises are employing a hybrid architecture where they store their data in a cloud neutral location like Platform Equinix® and move it into the appropriate cloud on demand for relevant AI processing or data sharing with partners.

Bring compute to data: For more sensitive data such as patient or transaction information, enterprises are hesitant to let the raw data ever leave their premises. For example, hospitals want to share information with each other to build more accurate AI models, but, for confidentiality reasons, they do not want to share raw data with individual patient records. In these cases, AI processing is done where the raw data resides. After the raw data is processed, only the resulting insights, anonymized meta-data or  AI models are shared.

 

Federated learning also helps to ensure that the raw data is kept in the same location it was generated in for data compliance. Many countries have enacted or are in the process of enacting data residency laws that require data to be kept in a particular geographic location. In these cases, enterprises have to do their AI processing within a particular country and geography. 

Bring data and compute to a neutral location: In some cases, the data providers do not want to share their raw data and data consumers do not want to share their AI algorithms. Consortium based data marketplaces within a vendor-neutral global interconnection platform like Platform Equinix® make it easy for enterprises to buy and sell data/algorithms securely and compliantly, as well as build their AI models.  In many instances data is already being exchanged at the network level between different providers and enterprises at a neutral interconnection hub like Platform Equinix. Thus, this is the optimal place to also perform data exchange at the higher level for AI model training.

Trend 2 -Innovative public cloud AI models and services are driving hybrid multicloud architecture

Today organizations across many sectors are not in a position to build AI models from scratch. Instead they want to augment existing AI models with their own contextual data to create new models. Because these pre-built AI models require a vast amount of data and compute to train, they are generally only offered by major cloud service providers (CSPs). Enterprises want to leverage these sophisticated AI algorithms/models in the clouds for processing tasks such as image/video recognition, natural language translation, etc. while maintaining control over their data. Most enterprises will also want to use AI models and services from different clouds for maximum innovation and to avoid vendor lock-in. This is driving a need for distributed, hybrid multicloud infrastructure for AI data processing as shown below.

 

Platform Equinix provides connectivity to over 2,900 cloud and IT service providers across 55+ metros on a single interconnection platform, enabling enterprises to easily deploy hybrid multicloud architectures with the provider of their choice. And with Equinix Cloud Exchange Fabric® (ECX Fabric®), businesses can easily establish secure, high-speed software-defined connectivity to other locations, partners or businesses with minutes via a self-serve portal. This includes storage as a service (from partners) which enterprises can use to facilitate this hybrid AI model. In addition, with the acquisition of Packet, Equinix also now provides automated bare metal compute as a service which enables enterprises to anonymize their data before moving it to the public clouds for processing.

Trend 3 - Growing data volumes, latency, cost and regulatory considerations are shifting AI data management and processing to a "cloud-out and edge-in" architecture

Data is growing exponentially everywhere, including the edge. For example, a connected car can generate up to 3 terabytes plus data a day, while a smart factory can generate 250x that much data a day.Idea AI processing is moving to the edge for cost, latency and compliance reasons. As shown in the figure below, there are different types of edges which impact where AI processing is placed.

Cloud-Out and Edge-In are two key processing phenomena with respect to how AI is moving from a centralized model to a distributed model.

 

Cloud-out means some AI processing is moving out to the edge: Both AI training and inference operations are moving from the centralized cloud to the edge as follows: 

AI inferencing is moving to the edge:  Many real-time applications such as video surveillance, augmented/virtual reality (AR/VR) or multiplayer gaming cannot tolerate the latency of sending requests to an AI model in the core clouds for a response. For these use cases, AI inference needs to happen at the device edge or the micro edge. In many markets, existing Equinix data centers can provide a round trip latency of < 5ms, and thus, can host these AI inference use cases. Many video surveillance and smart store shopping use cases need round trip network latency between 15-20ms. Equinix data centers are perfectly suited for hosting these AI inference use cases.

AI training is moving to the metro edge: As more data is generated at the edge and IoT datasets are becoming larger, companies do not want to backhaul this data over costly, slow, high-latency networks to a core cloud for AI model training. Also certain types of data must be kept on-premises for data privacy or residency. Over 132 countries have already enacted or are in the process of adopting data privacy/residency laws.[ii] This is ideally suited for federated AI model training techniques to train AI models at the edge (bring compute to data) and then aggregate these local (potentially suboptimal) AI models at a core data center to build better global AI models. Federated learning also helps to ensure that the raw data is kept in the same location it was generated in for data compliance. And moving analytics closer to the edge improves performance and cost efficiency.

Edge-in means deep learning and model training is moving in from the far edge to the metro edge:

AI Inference is moving to the metro edge: Latency sensitive AI inference cannot take place in a public cloud. In many cases, inference operations can take place at either of device, micro or metro edges. However, for cost reasons and data fusion reasons, it is beneficial to move up the edge hierarchy (edge-in). For example, smart cameras can do AI inference at the device level, but these devices can be costly. Alternatively, regular cameras could be used if the AI inference processing is moved higher up in the edge hierarchy to a micro or a metro edge (depending upon the latency requirements). And, except for life critical operations that require less than five milliseconds (5ms) round trip latency, this should satisfy real-time latency requirements at more optimal cost points. Furthermore, in many use cases, data from additional external sources and databases needs to be fused to improve model accuracy. Due to their compute and storage resource requirements, these additional data sources often cannot be easily hosted at device or micro edges. Furthermore, with the emergence of 5G networking technology, more processing can be moved from the devices to the micro and metro edges due to lower latencies and better bandwidth.

AI Training at the metro edge:  Hardware that does the AI model training has high power requirements (30-40KW for fully loaded rack), so it cannot be hosted at the micro edge. Furthermore, most private data centers are also not equipped to handle beyond 10-15 KW per rack, so AI training hardware typically needs to be hosted at a colocation data center. It is also beneficial to colocate AI training hardware at an interconnection rich data center like Platform Equinix  (as shown in the figure below) due to 1) high speed connectivity to multiple clouds and networks, 2) a global footprint so that you can adhere to data residency requirements and 3) a dynamic global ecosystem of nearly 10,000 companies. In many cases the external company that has the required data already has their footprint at Equinix.

 

AI adoption has been growing and will continue to accelerate as companies enter 2021. Therefore, it is important to understand how to most effectively manage data using AI technology in order to provide business solutions.

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About the Author

Kaladhar Voruganti 

Kaladhar Voruganti currently works on distributed AI and Blockchain architectures as Vice President of Technology Innovation and Senior Fellow at Equinix. He previously worked at IBM Research and NetApp CTO office on large-scale distributed systems. He obtained his bachelor’s degree in Computer Engineering and PhD in Computing Science from University of Alberta, Canada. To date, he has 70 patents either filed or issued.

Doron Hendel

Doron Hendel currently works on connected/autonomous Vehicles, IoT and AI business initiatives as a member of the Global Business Development organization at Equinix. He previously worked on IoT, AI/ML, wireless networking technologies and distributed compute architectures.

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[ i ] Cisco, Connected Car - The Driven Hour, Feb 2019; IBM, Smart Factory, The average factory generates 1 TB of production data daily.

[ ii ] SSRN, Greenleaf, Graham, Global Tables of Data Privacy Laws and Bills (6th Ed January 2019). (2019) Supplement to 157 Privacy Laws & Business International Report (PLBIR), Feb 2019.
Published Friday, December 11, 2020 7:44 AM by David Marshall
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