Future Proofing your AI Strategy with Scalable AI Infrastructure

A research study by Tata Consultancy Services, one of the largest IT Service providers, reveals that more than 8/10 C-suite leaders have already deployed Artificial Intelligence (AI). According to Fortune Business Insights 2024 survey, AI’s market size is to rise from $621 billion in 2024 to $2,740 billion by 2032. AI is now seen as one of the key strategies, organisations are eyeing to achieve their business objectives and ambitions.

The rapid evolution of AI has led to a substantial increase in the demand for computational resources and marked the importance of the underlying layer that hosts such AI platforms and models. Compute limitations (availability & cost efficiency) still stays one of the biggest challenges to overcome while deploying an AI model.

AI infrastructure refers to hardware and software environment designed to develop, deploy, and execute the AI workloads. What makes AI infrastructure special is its high performance and scalable characteristics. Scalable AI Infrastructure is crucial for global businesses, as it ensures that their AI systems can handle growing computational demands. Many big players like Nvidia and intel are investing heavily to build chips that can run complex AI models. Recently, NVIDIA had launched a new Blackwell GPU that can enable organisations to build and run real-time generative AI on trillion-parameter large language models at up to 25x less cost and energy consumption than its predecessor.

Irrespective of the type of organisation or industry, the core AI infrastructure components remains the same. At a high level, it includes computation resources, data management & storage, data processing frameworks and Machine learning (ML) frameworks.

AI applications require specialised hardware such as central processing unit (CPU) and graphics processing unit (GPU) to cater to high computational needs. AI applications also require a large amount of data to provide predictions and analysis. This historic or real-time data are stored in a data management system which performs tasks like data ingestion, data processing, and data analytics. Data processing frameworks are critical for handling and transforming data efficiently before it can be used for model training and inference. ML frameworks provide necessary tools, libraries, and interfaces to develop, train and deploy AI models.

Deploying an AI infrastructure is a complex process that requires careful consideration of various factors to ensure optimal performance, scalability, compatibility, and cost effectiveness. AI models and their datasets are meant to drastically grow with time, and the underlying AI infrastructure should be scalable enough to support these models. The infrastructure should also be modular and upgradable to cater to emerging trends in the AI world.

Any AI model involves huge chunks of data that need to be ingested and processed. Model training is also a crucial piece in the overall AI deployment. The machine learning algorithms should be able to process the enormous data sets swiftly, leading to faster model training and inference. The core AI infrastructure should be built considering the existing technology stack of the organisations. This ensures smoother integrations with the vast amount of historical and real-time data, hence making the model training method more matured.

Every AI use case is different and may require specific hardware, software, data management and integration capabilities. For example, a complex machine learning model like linear regression would require less computational power compared to the deep learning models like convolutional neural networks, which need powerful GPUs or TPUs. Real time applications like autonomous driving, real time fraud detection requires ultra-low latency and high performing infrastructure. Any batch processing tasks can still tolerate higher latencies and may use less powerful infrastructure.

Many organisations are not sure where to begin their AI adoption journey. According to “the 2023 State of AI Infrastructure Survey”, 54% of respondents had highlighted that they are facing infrastructure related challenges while developing and deploying their AI models. Weak infrastructure and cloud security controls can impact the integrity of the AI operating environment. A critical question before deploying AI models is to ask if the existing IT infrastructure and data ecosystems can support the AI technologies.

To overcome the above challenges, the organisations can follow the below 3-phased approach to discover the current technology estate and assess the open-source AI platforms/ frameworks to recommend the best-fit technology.

To ensure a comprehensive AI implementation, conduct a current state discovery focused on specific AI use cases and business capability. Then assess the existing technology and infrastructure to identify gaps, evaluating components such as compute resources, storage solutions, data processing frameworks, security measures, data flow, application architecture, and integrations. Finally, analyse the current programming languages and frameworks to understand integration requirements.

To determine the technology suitability for specific AI use cases, research and evaluate both enterprise and open-source AI technology options like Microsoft OpenAI, AWS Bedrock, PyTorch, and TensorFlow. Then engage in discussions with these AI technology partners to assess factors such as scalability, performance, security, and integration compatibility. Additionally, evaluate large language models (LLMs), the type and volume of data available for training, and the application architecture.

For target state recommendation, shortlist the best-fit AI technology or service stack based on the feasibility assessments. This includes recommending appropriate AI models and frameworks, suitable data storage solutions, necessary virtual machines, or containers (GPU, CPUs) and programming languages & LLM models. Finally, define a future roadmap for implementing the recommended AI technology, outlining deployment and integration strategies.

It may not be the right business case for organisations who don’t have deep pockets or the required skillset to build the AI infrastructure, LLM models, deep learning frameworks or the machine learning libraries from scratch. Cloud service providers like AWS, Azure & Google Cloud offers fully managed AI services and AI trained models that may reduce organisation’s initial investments. To name a few, there is Amazon Bedrock, Amazon SageMaker, AWS Deep Learning AMIs from AWS; Azure Machine Learning, Azure Cognitive Services, Azure Databricks from Microsoft Azure and Google Vertex AI, BigQuery ML, TensorFlow on Google Cloud from Google Cloud.

Artificial Intelligence has been with us since last few years and has shown a tremendous growth year on year. With technology getting more matured and AI becoming the crucial link for businesses to grow multi-folds, all the components of an AI model/ platform need to be addressed by organisations. They must engage in thorough planning, starting with a comprehensive discovery of their current state to understand existing capabilities and gaps. This groundwork enables informed decisions when selecting the appropriate AI technology stack, ensuring the infrastructure is robust, scalable, future proof, and aligned with the specific needs of their AI use cases.

Authors: Dishant Nagpal