By Ocient Staff
Artificial intelligence (AI) and machine learning (ML) are rapidly moving out of the realm of hype and excitement over what’s possible — and into the world of practical applications and realized value. But as more organizations work on bringing AI and ML applications to life, they’re encountering technical limitations in their legacy infrastructures. Fortunately, as proven applications of AI and ML advance, key requirements and best practices are emerging that can help organizations understand and build out a future-ready data infrastructure to capture the vast opportunities of the promised AI/ML transformation.
This blog will provide an overview of those advancing use cases, technical requirements, and best practices, including:
- Defining the hierarchy of AI, machine learning, and deep learning.
- Outlining the unique technical requirements and challenges around the data warehousing and data processing infrastructure needed to enable both AI and ML applications at hyperscale.
- Identifying the common roadblocks and limitations of legacy data infrastructure.
- Showing how Ocient is helping some of the most innovative organizations across sectors to build future-ready infrastructures and tailored solutions to accelerate AI and ML transformation.
AI and ML: A comparison for data professionals
While the terms can at times be used in ways that make them seem synonymous, they can best be understood in terms of hierarchy:
AI and ML are often used together in data analytics, though they serve distinct roles. Here’s an overview of the key differences and their relevance to data practitioners:
Top AI/ML use cases in data analytics — 4 key verticals
More and more organizations across industries are using AI and ML to optimize operations, reduce costs, and enhance decision-making capabilities. Here are some of the most developed and proven AI and ML applications across four key verticals:
Telecommunications
AdTech
Automotive
Government
In each of these industries, AI and ML not only enhance operational efficiency but also drive business growth by making complex data more actionable. These technologies allow organizations to better predict outcomes, optimize resources, and respond proactively to emerging challenges.
AI and ML are the engine — but business data is the fuel for differentiation
While much of the buzz around AI and ML today centers on “name brand” tools like Open AI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, or IBM’s Watson, the biggest determiner of success with AI and ML won’t be which tool a business uses. In fact, competing businesses will increasingly be using the same third-party AI and ML tools as they contend for the same objectives. And any AI or ML tool is only as “smart” as the breadth, depth, and accuracy of the data it has access to. That makes the data pipelines fueling the AI/ML engines the most critical aspect of the business transformation.
Ocient is helping clients across several industries to build a competitive advantage through fast, flexible, and scalable data pipelines. Our data warehousing and data processing solutions are providing the future-ready foundation that enables organizations to connect unique data streams to AI/ML tools to enable new kinds of automation, new types of insights, and entirely new products, services, and business models.
Key technical requirements of building an AI/ML data pipeline
What do better data pipelines look like? Here are a few of the key technical demands that organizations will need to meet in order to buildseamless data pipelines to fuel their AI/ML engines:
Data processing requirements
AI/ML applications require handling diverse data types — including structured, semi-structured, and unstructured data. This data often comes from various sources and must undergo extensive preprocessing, cleaning, transformation, and feature engineering before it can be used. This is a complex but fundamental step in ensuring the data is accurate, usable, and ready for downstream analysis.
Compute requirements
The computational demands for AI/ML applications can vary widely — but are generally much higher than the basic data analytics organizations are familiar with. Complex models, such as those used for image recognition, natural language processing, and real-time decision-making, require significant compute power. Additionally, model training, particularly for machine learning, is highly resource-intensive and can fluctuate based on the complexity of the algorithms and the size of the datasets. Organizations must build out the necessary compute infrastructure that can effectively support these workloads — and do it in an energy-efficient and cost-effective way.
AI/ML applications need a robust data storage system capable of handling large-scale datasets across various formats in order to run effectively. This means organizations need to build out hyperscale data management solutions that support the efficient storage, retrieval, and rapid access of vast amounts of data — all while maintaining data quality and data security.
Flexible & scalable infrastructure
Each of the above technical demands point to the need to adopt flexible and scalable infrastructure to support AI/ML at scale. Whether cloud-based, on-premises, or hybrid, the infrastructure should be capable of supporting large-scale data processing, model training, and real-time analytics. Scalability is essential to handle growing data volumes and increasingly complex models, while flexibility is required to accommodate a wide range of AI/ML tools and technologies. Moreover, this modernized infrastructure should be architected with an eye to future needs, recognizing that the scale and speed of AI/ML demands will only accelerating in the coming years.
Can legacy data warehouses and data processing solutions handle AI & ML?
No, traditional data warehouses were designed for basic historical reporting and querying. Even data warehouses built for more modern business intelligence (BI) and analytics were never designed for or intended to support AI and ML workloads.
Using legacy data warehousing and processing solutions for AI and ML workloads presents five critical challenges:
- Siloed technology and data: The difficulty of manually integrating systems and aggregating siloed data leads many AI and ML projects to stall — or see costs rapidly grow out of control. Moreover, relying on manual integration or aggregation means that data is often outdated before it ever gets to the AI or ML models.
- Low processing speed: Many legacy infrastructures can’t keep up with the volume and velocity of data flowing into AI and ML tools. Not only does this mean stakeholders can’t rely on real-time insights from the tools, but also the AI or ML models are getting old input data, leading to unreliable outputs.
- High processing costs: The relative inefficiencies of processing large and complex data sets through legacy infrastructures drives costs way up. Excessive costs stop many AI and ML projects from getting the buy-in to scale up. In other cases, the high operational costs overwhelm the promised business value of the AI or ML application.
- Poor scalability: In another common “failure to launch” scenario, an AI or ML proof-of-concept shows meaningful value. But the organization recognizes that its legacy infrastructure simply cannot handle these applications at scale.
- High energy demands: Legacy data warehouses and processing solutions are often energy-inefficient, consuming significant power to process large datasets. This not only drives operational costs higher but also conflicts with sustainability goals, making it harder for organizations to scale AI and ML applications in an environmentally responsible way.
Best practices for AI and ML: Building your data warehousing and data processing infrastructure
Ocient is helping enterprise organizations across a range of sectors to build future-ready data warehousing and data processing infrastructures that will enable tomorrow’s transformative AI and ML applications. Through our real-world experience and proven success, we’ve identified a set of six best practices:
- Simplify data management environments: Consolidate technologies and integrate data wherever possible to ensure you’re pulling together relevant data — automatically and efficiently.
- Ensure real-time access to fresh, reliable data: Put the tools in place to enable real-time data processing, but also make sure you have the advanced data quality management capabilities to protect the quality of your data at scale to ensure reliably accurate outputs for decision-making.
- Optimize cost- and energy-efficient data processing and prep: Eliminate redundancies in your data processing tools and look to consolidate data processing with the most energy-efficient solution. This focus on energy efficiency will help drive down the cost of large scale data processing to make development more practical and drive the profitability of your AI and ML applications.
- Build AI/ML data pipelines with scalability in mind: Design your data pipelines so that they can handle the full volume and velocity of both current and future data flows — without performance degradation. This ensures you can readily and cost-effectively scale up and expand your AI or ML application once operational.
- Consider time-to-market when developing ML applications: Look to integrate ML capabilities directly within your data warehousing solution to accelerate development and speed time-to-market and time-to-value for your ML application.
- Build with data security and compliance in mind from the start: Make sure your data warehousing and processing systems have robust, modern data security measures in place so you can confidently protect your systems and data today — and adapt to evolving regulatory requirements.
Ocient is building AI- and ML-ready infrastructure to enable leading innovators
Ocient is already several steps down the path of defining and building the future-ready infrastructure needed to enable tomorrow’s AI and ML applications. The Ocient Hyperscale Data Warehouse™ provides a single platform for centralized data management and warehousing that’s built for complex AI and ML applications. The Ocient architecture enables real-time processing at full scale — without losing the richness or reliability of data. Ocient’s approach to data processing also drives cost and energy efficiency to bring advanced AI and ML applications into practical reach for more organizations.
OcientML: A faster path to ML business value
At Ocient, we’ve already helped some of the most innovative organizations in the world to build out future-ready infrastructures, designing versatile and scalable pipelines that fuel advanced data analytics, AI, and ML. Now, we’ve developed a focused product, OcientML, that offers both a pre-built infrastructure that covers the essential demands of AI and ML, and a set of highly customizable features that can be easily tailored to specific applications. OcientML is the first out-of-the-box data warehousing solution built to simplify the path to building and running transformative ML applications in your organization.
You can learn more about OcientML here.