AI solutions improve efficiency, cut costs, and reduce complexity. But technology rarely works in isolation. Artificial Intelligence (AI) is only as effective as its underlying infrastructure and data.

Understand the Use of Cloud in AI

Building an AI infrastructure requires a re-look on the conventional notions of storage, networking, and data. AI makes huge demands on data storage and processing. For instance, an AI model to detect tumours requires training the system with thousands of radiology reports. The input data comes in different forms, and require accommodation in a single repository. 

AI algorithms make better decisions when exposed to more and more data. The model works in a scalable neural network. Conventional CPU based computing is inadequate for such tasks. 

AI tasks require high-speed GPUs with parallel processing capabilities. But most enterprises do not find it viable to invest in such GPUs. The practical solution is the scalable and cost-effective computing power in the cloud. The cloud offers virtual machines with powerful GPUs and infinite storage. Various cloud providers offer batch processing, orchestration and server-less computing to automate Machine Learning tasks. Infrastructure-as-a-Service (IaaS) handles predictive analytics with ease. These resources come with the scalability, latency and reliability that AI demands.

Understand the Volume and Nature of Data

Provisioning for huge data storage or advanced processing is not enough. Unmindful provisioning creates capacity problems or causes waste. 

Understand the specific nature of the requirements. AI systems taking real-time decisions need powerful and high-performance processing capabilities, whereas AI systems gathering data for offline analysis have lesser demands. For instance, educational institutions building large-scale data lakes for decision-making primarily need storage capacity. But financial enterprises making real-time trading decisions also need a powerful flash solution and advanced processing capabilities.

Prepare the Data

The efficacy of AI depends on data scrubbing, to remove rubbish data. If the data feeding the AI system is inaccurate or full of flab, the insights will be inaccurate. Many enterprises now use automated data cleansing algorithms for data cleansing.

The huge volumes of data generated by sensors, devices, and other external sources raise complex issues. Data scientists grapple with questions on which data to keep and discard. Modern data platforms such as Hadoop and Spark organize unstructured data well. But no one-size-fits-all solutions exist.

Do Not Ignore Data Access and Control

Most enterprises rely on a complex, hybrid environment with on-premise and cloud-based services. Data resides in scattered locations, consumed by individuals, reports, external applications, and devices. Maintaining relevant controls is difficult when many people and devices pull data. Traditional approaches do not offer the flexibility and robustness demanded by the AI ecosystem.

Successful AI requires effective data access management. Intent-based networking offers an effective solution. It understands the underlying network configuration and integrations and automates network administration. The system anticipates network demands and security threats in real-time and makes automated responses.

Optimize the Architecture

Many enterprises replicate their incumbent on-premise data architecture for the cloud. Such an approach is comfortable but does not unlock the capabilities of the cloud or the demands of AI. It also causes server performance bottlenecks when implementing AI.

AI requires modern hardware platforms designed for compute-intensive workloads. But throwing in new hardware is not the solution always. A creative architecture with high bandwidth, low latency, and proper configuration fix many issues. Core Hadoop and streaming software optimize the set-up.

Good cloud-enabled data architecture offers elasticity. It allows ready scale-up when the system requires more computing horsepower. AI remains a non-starter if users cannot apply a formula to a large data set without first checking server capacity.

Shape up an architecture that ingests all data types and enable computations on demand. A good AI compliant architecture:

• address different shapes and granularity of data. It processes data from transactions, logs, spatial information, sensors and social media seamlessly.
• consumes different data structures in different time dimensions, especially real-time.
• brings in data to multiple consumption points fast.
• feeds real-time time-series data for smart-home appliances, health devices and self-driving cars.

Test the AI frameworks for optimization with the hardware and make suitable amends.

Ensure Security and Compliance

Security is always a big issue in computing. Cloud AI is no different.

Apart from the standard security precautions,

• Use machine intelligence-powered anomaly detection and embedded business logic to identify threats. Automate actions for known events.
• Invest in encryption to secure transfer of data.

AI data may have to meet regulatory requirements such as PCI-DSS or HIPAA, depending on the location and nature of business. Cloud providers provide real-time event configuration management. It issues instant alerts and can even cut off machines not meeting security or compliance requirements. But the onus is on the enterprise to understand their compliance obligations and set appropriate standards.

Further reading: 5 Big Network Security Breaches in 2019 and What’s in Store for 2020

The effectiveness of enterprise AI depends on the robustness of its cloud infrastructure. Apt cloud infrastructure for compute-intensive workloads is the basic prerequisite. Mastery over the underlying architecture helps.