AI is everywhere! To make an AI system work, run and provide us assistance, we need to provide the fuel for AI. To ensure that you have a working AI system, first, you must ensure that your data workflows run smoothly and are scalable for the high demand of the AI components. The challenge is not just to provide access to all your data (in a cheap data store). The real challenge is to modernise and optimise the entire lifecycle of your data, from ingestion to storage and processing. Ultimately, you need to ensure that you have a system with good performance and controlled (or less) bottlenecks. Vendors like Microsoft Azure provide a set of tools, cloud services, and data infrastructure designed to complement AI solutions.
One of the first challenges that needs to be addressed is
data storage. As we know, AI systems require access to a large amount of data
in different formats (structured and unstructured). A storage strategy that can
manage the payload generated by AI applications is required to avoid
performance issues and bottlenecks. The lack of a unified access layer can lead
to data inconsistency and latency. This is a common challenge for companies
with datasets in multiple locations that must be exposed to the AI system. For
this purpose, a solution like Microsoft Fabric can be successfully used to
unify a scalable storage solution for large datasets, with a unified data
governance layer under the same roof.
Another challenge is related to ETL/ELT—more or less data
ingestion and data transformation. Inside the organisation, the data is
fragmented across different sources. AI models require high-quality, clean data
that can be used by the models. To achieve this, services like Azure Data
Factory or Data Factory of Microsoft Fabric can be used to move, transform, and
aggregate all required data in one common repository.
Once the data is securely stored, the next challenge is
ensuring seamless data ingestion and transformation. AI models rely on clean,
high-quality data, yet raw data is often fragmented across disparate sources.
Azure Data Factory enables organisations to automate data movement and
transformation, integrating with various storage solutions such as Azure SQL
Database, Azure Cosmos DB, and external SaaS applications. Organisations can
ensure their AI models are trained on consistent and reliable data by setting
up data pipelines that cleanse, standardise, and aggregate information in near
real-time.
Another critical aspect of optimising data workflows is the
processing layer, where AI models extract insights from vast datasets.
Traditional data warehouses often struggle with handling AI data workloads'
high-volume and high-velocity nature. Microsoft Fabric, a unified analytics
platform, helps overcome these challenges by providing an integrated
environment where data engineers and AI developers can collaborate efficiently.
By combining the power of Synapse Data Warehouse with Spark-based big data
analytics, organisations can run AI-driven queries at scale without performance
degradation.
Leveraging real-time data streaming and event-driven
architectures further enhances the efficiency of AI workflows. Many AI
applications, such as fraud detection and predictive maintenance, require
continuous data ingestion and real-time inference. Azure Stream Analytics and
Azure Event Hubs allow businesses to process streaming data from IoT devices,
transactional systems, and web applications with minimal latency. This
capability ensures that AI models always work with the latest data, enabling
faster and more accurate decision-making.
Finally, organisations must optimise AI workloads for performance and cost efficiency. Running large-scale AI workloads on Azure requires balancing on-demand resources with reserved capacity to avoid overspending. Azure Cost Management helps track and optimise expenses by analysing resource utilisation patterns and recommending cost-saving measures such as Spot VMs for non-critical workloads.
Optimising data workflows for AI is not just about improving
storage and processing speeds. It requires a holistic approach integrating
storage efficiency, seamless data pipelines, scalable processing, and cost optimisation.
By leveraging Microsoft Azure’s AI and data services, businesses can create a
robust, AI-ready infrastructure that accelerates innovation while ensuring
operational sustainability.
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