CIOInsider India Magazine
Key Strategies for an Effective AI-Cloud Integration
Naresh possesses over two decades of experience in the software industry with proven mettle in the areas of enterprise cloud and hybrid infrastructure verticals.
CIO Insider recently got a chance to interact with Naresh, wherein he shared his thoughts on the current cloud & AI technology landscape, and how integrating these two technologies can be beneficial to the enterprises. Read on to know more about this exciting topic -
What are some of the common challenges organizations face while integrating AI solutions onto their cloud platforms?
Of late, we are witnessing widespread adoption of artificial intelligence by organizations of all sizes across almost every industry. Due to the humongous benefits that AI offers, the majority of companies are very much eager to integrate AI into their existing within the shortest time frame possible. Here, cloud technology acts as the key enabler for businesses by enabling them quickly onboarding any AI software or application while simultaneously facilitating agility and scalability.
Some challenges include:
• Since most of a company's data is stored on the cloud, it is also seen as a potential target for cyber criminals, making it extremely crucial for companies to have the necessary cybersecurity measures in place. As a result, there are numerous data security related frameworks that every organization must adhere to in terms of collecting and handling user data.
• The integration of AI with public cloud infrastructure is another major challenge. However, there is severe dearth of skilled technical manpower in the country to effectively cater to the industry demand. Thus, companies are now investing heavily on training programs to upskill their existing workforce and make them capable to effectively handle AI integrated cloud systems.
• The procurement of the huge computing power that is required for these systems to process large amounts of data is another tough nut to crack for most organizations, especially when using data to predict future trends or derive insights from analyzing historical data.
Throw some light on the major security practices organizations must implement while deploying AI solutions on the cloud.
While training a LLM requiring humongous amounts of data to be fed into a system, organizations must make sure to
anonymize data so that no user information is fed into the system. Since LLMs utilize the data stored on cloud for its training and analysis purpose, the first step that companies must take is to ensure that no sensitive personal information related to their customers is stored on cloud. Secondly, it is crucial for the companies to encrypt the data to ensure that it is secure both in storage and during transit. Also, companies must have stringent access control mechanisms in place and implement a 'Zero Trust' policy across all their systems and applications. Additionally, integrating automation technologies for niche processes such as periodic data backups, software updation, antivirus patch updates and many others will help organizations immensely in strengthening their overall data security posture.
Briefly explain the role of cloud-based data lakes in supporting the training of AI models.
While training an AI model, the more data you feed into the system, the better its outcomes will be. Since data is collected from multiples sources, it cannot be fed into the LLM on a real time basis and has to be stored elsewhere. This is where data lakes come into the picture. Business owners prefer getting the desired outcomes without having to build a data lake on premise since it is an extremely difficult task. Thus, business nowadays are heavily relying on public cloud data lakes to optimize their processes, ensure data security and privacy, and train the AI models with ease. Thus, I feel keeping data lakes on cloud while training AI algorithms is critical for the success of any LLM.
Being compliant with regulatory frameworks such as GDPR and CCPA enables the enterprises to train their AI models to provide highly effective outputs while simultaneously avoiding bias.
Since AI models provide insights and recommendations based on data, it is paramount for organization to ensure the quality of that data. This is because if the data being fed into the AI model is biased, the results being provided by the algorithm are rendered useless and ineffective, resulting in the company taking wrong decisions and incurring losses. A key strategy that businesses can adopt in eliminating bias in data is to gather data from diverse sources. Additionally, being compliant with regulatory frameworks such as GDPR and CCPA enables the enterprises to train their AI models to provide highly effective outputs while simultaneously avoiding bias.
Also, any inference that is provided by the AI model post analyzing the data must not considered as final. Companies must have stringent validation mechanisms in place to verify the inferences provided by the AI algorithms. Most importantly, AI algorithms must only be used to derive insights or recommendations and not be allowed to take decisions on its own, as it is incapable of taking the 'emotion' aspect into consideration. Thus, a perfect balance of AI integration and human intervention is very much necessary for organizations while using cloud-based AI services.
How can organizations leverage the combination of cloud computing and AI to become more sustainable in their daily functions?
The massive computing power that is required for training the AI models results in an enormous carbon footprint. Cloud-based AI models enable businesses to put multiple transactions through multiple servers so that the power consumption is uniformly distributed among all servers, thus reducing the workload. Also, ensuring the supply of real-time data to the AI algorithms through the cloud enables manufacturing companies to scale-up/down operation with ease and save a lot of resources such as power, water, raw materials and many others, thus contributing significantly towards environmental sustainability. Further, integrating data collected through IoT sensors enables AI algorithms to analyze weather data and accordingly take necessary actions to be sustainable. The perfect example for this is the AI algorithm deciding whether to use water for a particular task by analyzing the data weather data collected through sensors and ascertaining the chances of rainfall.
