Infrastructure Design and Optimization
Our Managed Infrastructure services ensure your organization's data and machine learning operations are supported by scalable, reliable cloud infrastructure. We can set up and manage public and private cloud platforms like AWS, Azure, and Google Cloud, tailored to your specific needs. Additionally, we provide robust data storage solutions, including data lakes, warehouses, and databases, to ensure your data is efficiently managed and accessible.
How is AI Infrastructure Different?
AI infrastructure differs significantly from normal business infrastructure in its focus on specialized components and capabilities tailored to support AI and machine learning workloads. Unlike traditional infrastructure, AI infrastructure requires high-performance computing resources like GPUs and TPUs for rapid data processing and model training. It also involves advanced data storage and management systems capable of handling large volumes of diverse data. Additionally, AI infrastructure incorporates specialized frameworks for data processing and machine learning, as well as MLOps platforms to automate and streamline the machine learning lifecycle, ensuring efficiency, scalability, and flexibility.
Data Storage and Management
Essential for storing, organizing, and retrieving vast amounts of training and validation data. This involves databases, data warehouses, or data lakes, either on-premise or cloud-based.
Compute Resources
Specialized hardware like GPUs or TPUs are crucial for computationally intensive tasks, with many organizations leveraging scalable cloud-based resources for flexibility and cost-effectiveness.
MLOps Platforms
Automate and streamline the machine learning lifecycle, including version control, automated pipelines, performance tracking, and facilitating collaboration among different roles.
Networking
Efficient data flow and communication within AI infrastructure require high-bandwidth, low-latency networks for rapid dataset transfer and seamless integration of distributed resources. Reliable networking is essential to maintain the performance and scalability of AI applications, whether on-premise or cloud-based.
Machine Learning Frameworks
These provide tools and libraries for designing, training, and validating models, often supporting GPU or TPU acceleration for faster computations. Machine learning frameworks streamline the development process, offering functionalities like automatic differentiation, optimization, and neural network layer integration.
Data Processing Frameworks
These frameworks are necessary for cleaning, transforming, and structuring large datasets, allowing for distributed processing to speed up tasks. They enable efficient handling of complex data transformations and support the processing of vast amounts of data across multiple nodes, significantly enhancing overall data processing efficiency.
Core Components of AI Infrastructure Design
Core AI Infrastructure Design encompasses several critical components. Data Storage and Management ensures secure, organized handling of vast datasets through various storage solutions, both on-premise and cloud-based. Compute Resources, such as GPUs and TPUs, provide the necessary power for intensive tasks, while Data Processing Frameworks handle large-scale data transformation and structuring. Machine Learning Frameworks offer tools for model development and validation, and MLOps Platforms automate the lifecycle of these models. Additionally, robust Networking infrastructure supports efficient data transfer and resource integration.
Why Helix?
Streamline Your AI Journey with a partner who understands your industry inside out. Helix provides specialized industry expertise, tailored AI solutions, and advisory services to help you realize your vision. Our consultancy, backed by proven use cases and domain knowledge, empowers you to address unique challenges and seize opportunities with artificial intelligence. Together with our strategic partners, we'll develop a roadmap aligned with your objectives, integrating prioritized use cases for impactful AI adoption across your business.
Let's Build Together
Helix understands AI and IT technology, has deep domain expertise, and can stitch together a strategic roadmap that combines your organization’s unique corporate objectives, existing infrastructure investments, and integrates prioritized use cases to help your business adopt AI with a purpose and impact.
For more information about the Helix Center for Applied AI and Robotics, contact your CNXN Helix Pro Account Manager or drop us a line at AI@Connection.com