Time to read: 6 minutes | Published: March 10
AI stack What is an AI stack?
An AI stack refers to the collection of tools, technologies, and frameworks that work together to build, train, deploy, and manage AI applications. It encompasses everything from data processing and machine learning frameworks to cloud services and hardware infrastructure, enabling developers and organizations to effectively create and scale AI solutions.
Examples of products within the AI stack:
TensorFlow – An open-source machine learning framework that allows developers to build and train deep learning models.
AWS Sagemaker – A cloud service provided by Amazon Web Services that simplifies the process of building, training, and deploying machine learning models at scale.
- Overview of an AI stack
- Infrastructure layer
- Data management layer
- Inference and deployment layer
- Application layer
- Partner with HPE
What goes into an AI stack?
Here's a high-level breakdown of the different layers within the AI stack:
- Data collection and preparation: This is the foundation of the AI stack. It involves gathering raw data from various sources and cleaning, organizing, and preparing it for use in AI models. Tools and platforms at this layer help automate data pipelines and ensure data quality.
- Data storage and management: This layer handles the storage, organization, and accessibility of massive datasets. Solutions here often include databases, data lakes, and cloud storage services that enable efficient data retrieval and management.
- Model development and training: At this layer, developers create and train AI models using machine learning frameworks and libraries. Tools in this category, such as TensorFlow and PyTorch, allow data scientists to experiment, train, and fine-tune their models using structured and unstructured data.
- Model deployment and serving: This layer involves taking trained models and deploying them to production so they can be used in real-time applications. Platforms and services here focus on scaling, monitoring, and managing the performance of models, such as AWS Sagemaker or Kubernetes-based solutions.
- Infrastructure and compute: This is the backbone that powers the AI stack. It includes the hardware (e.g., GPUs, TPUs) and cloud services that provide the computational power needed for training complex models and running AI applications at scale.
- Monitoring and optimization: Once models are in production, this layer ensures they perform efficiently and consistently. Monitoring tools track metrics, detect anomalies, and identify when a model needs retraining. Optimization solutions also adjust resources and fine-tune models for maximum performance.
- User interfaces and integration: The final layer is where AI systems connect with users and other business systems. This includes APIs, dashboards, and software tools that make the AI outputs accessible and actionable for decision-making and operational use.
Each layer of the AI stack plays a crucial role in building a robust and scalable AI ecosystem, enabling businesses to leverage AI effectively from data collection to end-user integration. We will go further into detail in what each step does.
What is infrastructure is needed for an AI stack?
To achieve proficiency in the infrastructure layer for running AI models in-house, businesses need to follow several critical steps:
In-house AI infrastructure setup:
- Hardware acquisition: Businesses need to invest in high-performance servers and processing units like Proliant Servers or Cray Products, which offer robust computational power. GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units) are also essential to accelerate the training and deployment of complex AI models.
- Storage solutions: Large-scale data storage is necessary to handle the vast amounts of data required for training AI models. This includes setting up data lakes or high-capacity, fast-access storage systems.
- Network capabilities: A strong, high-speed network infrastructure is needed to ensure seamless data transfer between storage and computing units. This helps maintain the efficiency and speed of AI processes.
- Power and cooling systems: High-performance hardware requires significant power and generates heat, so businesses need a reliable power supply and advanced cooling systems to prevent overheating and ensure consistent performance.
- IT expertise and management: Skilled IT teams are essential to set up, manage, and maintain the infrastructure, handle troubleshooting, optimize performance, and implement security measures.
- Security protocols: Protecting sensitive data and maintaining secure operations are paramount. Businesses should implement comprehensive cybersecurity measures, such as firewalls, encryption, and access control policies.
Alternatives to in-house infrastructure:
For businesses that lack the capital or resources to build and maintain in-house infrastructure, alternative solutions include:
- Cloud computing:
- Cloud AI services: Providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offer scalable, on-demand computing resources. These services allow businesses to rent processing power, storage, and AI tools without the need for upfront infrastructure investments.
- Benefits: Scalability, lower initial costs, ease of setup, and access to advanced AI services.
- Considerations: Recurring operational expenses, dependency on internet connectivity, and data privacy concerns.
- Renting data center space:
- Colocation services: Businesses can rent space in data centers to host their own servers and storage systems. This allows them to manage their AI infrastructure without building and maintaining physical facilities.
- Benefits: Access to power, cooling, security, and reliable network connections provided by the data center.
- Considerations: Requires initial investment in hardware and IT expertise to manage servers, plus ongoing rental and maintenance fees.
Each approach comes with its own set of advantages and trade-offs, and businesses should evaluate their budget, data privacy requirements, and scalability needs when deciding between in-house infrastructure, cloud computing, or data center rental solutions.
What data management is needed for an AI stack?
To achieve proficiency in the data management layer of the AI stack, businesses need to focus on building a robust system for collecting, organizing, storing, and processing data. This ensures that AI models have access to high-quality data for training and inference.
In-House data management setup:
- Centralized data storage: Businesses need systems to handle large volumes of data efficiently. Solutions like HPE Ezmeral Data Fabric can be implemented for seamless data storage, access, and management. This platform provides scalable data storage and ensures data is available and reliable for AI model training and analytics.
- Data integration and pipelines: Establishing data pipelines that can pull in data from various sources (e.g., databases, IoT devices, cloud storage) is essential. This ensures data can be processed and moved seamlessly across the infrastructure. The HPE Ezmeral Data Fabric supports data integration capabilities that allow for unified data access across hybrid environments.
- Data processing tools: These tools help prepare data by cleaning, normalizing, and formatting it for AI models. For instance, Apache Spark and Hadoop are popular open-source data processing frameworks that enable distributed processing of large datasets.
- Data security and compliance: With increasing regulations, businesses need to ensure their data management systems comply with data privacy laws (e.g., GDPR, CCPA). Security measures, such as data encryption and access control, should be integrated to protect sensitive information.
- Scalability and performance: The data layer should be capable of scaling as data needs grow. HPE’s data solutions are designed to scale with business requirements, but alternative technologies like Databricks (built on Apache Spark) also provide scalable data processing and machine learning capabilities.
Alternatives for the data management layer:
For businesses that are unable or prefer not to handle data management in-house, there are cloud-based and third-party solutions available:
- Cloud data management services:
- Amazon S3 & AWS Glue: These services provide scalable cloud storage and data integration tools, enabling efficient data collection, preparation, and management.
- Microsoft Azure Data Lake Storage: Offers a secure and scalable data lake solution with high availability and integration with other Azure services for data processing and analysis.
- Google Cloud BigQuery: A fully managed data warehouse that supports real-time data analysis and integrates well with various Google Cloud AI tools.
- Third-party data management platforms:
- Snowflake: A data warehousing solution that provides real-time data sharing and scaling capabilities, making it a strong option for managing big data across organizations.
- Cloudera Data Platform: A hybrid and multi-cloud data platform that offers data engineering, data warehousing, and machine learning services with a focus on big data solutions.
Hybrid solutions:
HPE Ezmeral Data Fabric can be combined with cloud solutions for a hybrid approach, giving businesses the flexibility to manage some data in-house while leveraging cloud resources as needed. This can optimize both cost and performance for large-scale AI projects.
Key points for IT decision-makers and c-level executives:
- Data reliability: Ensure that data management solutions provide high reliability and availability to support continuous AI operations.
- Cost management: Evaluate the long-term costs of in-house versus cloud-based data management, including storage, processing, and compliance.
- Integration capability: Choose solutions that integrate easily with existing IT infrastructure and AI tools to maximize productivity and efficiency.
By using solutions like HPE Ezmeral Data Fabric and exploring complementary or alternative products such as Snowflake or Databricks, businesses can build a strong, scalable data management layer tailored to their specific AI requirements.
What is need at the inference and deployment layer?
To achieve proficiency in the Inference and deployment layer of the AI stack, businesses need an efficient setup that ensures AI models are deployed and perform optimally in real-time. This layer is where trained models are integrated into applications and used to make predictions or decisions, impacting end-user interactions and business processes.
In-house inference and deployment setup:
- High-performance servers: To run and deploy AI models effectively, businesses need powerful servers that can handle the computational demands of real-time inference. HPE ProLiant Servers and other HPE servers are ideal solutions, offering reliable, scalable, and high-performance hardware. These servers are optimized for AI workloads and can handle the heavy lifting required for deploying complex models, ensuring low-latency predictions.
- Scalable deployment frameworks: Ensuring the ability to deploy models across different environments (e.g., on-premises, cloud, edge) is essential. HPE infrastructure supports containerization and orchestration tools such as Kubernetes and Docker, enabling seamless scaling and management of model deployments.
- Load balancing and high availability: To maintain service reliability, load balancing ensures that AI applications distribute inference requests across multiple servers. High-availability configurations supported by HPE ProLiant Servers help prevent service downtime, keeping AI applications running smoothly.
- Monitoring and performance management: Continuous monitoring of deployed models is critical to maintaining inference accuracy and efficiency. HPE servers come with built-in management tools that track performance metrics, detect anomalies, and help optimize resource utilization. Additionally, AI-specific monitoring tools such as Prometheus and Grafana can be integrated for comprehensive oversight.
- Security and compliance: The deployment layer must have robust security protocols to safeguard data and model integrity. HPE servers offer enterprise-grade security features, including encrypted data transfers and role-based access controls, ensuring that deployed AI models adhere to industry standards and regulations.
Alternatives for the inference and deployment layer:
For businesses that prefer cloud-based or outsourced solutions for model inference and deployment, there are several options available:
- Cloud-based inference solutions:
- AWS Sagemaker Inference: Provides scalable infrastructure for deploying models with low-latency endpoints, allowing businesses to leverage pre-built services and tools for seamless integration.
- Google Cloud AI Platform Prediction: Offers serverless options for deploying trained models, making it easier to scale up or down based on demand while ensuring high performance.
- Azure Machine Learning Managed Endpoints: Enables quick and secure deployment of models with built-in scaling, monitoring, and governance features.
- Managed inference platforms:
- NVIDIA Triton Inference Server: An open-source solution that simplifies the deployment of AI models, optimizing GPU and CPU performance. It supports multiple models and frameworks, enhancing flexibility for deployment strategies.
- MLflow: A platform that manages the end-to-end machine learning lifecycle, including model deployment, versioning, and tracking. It can be integrated with HPE servers for streamlined operations.
- Edge deployment solutions:
- HPE Edgeline Converged Edge Systems: For businesses looking to deploy AI models at the edge, HPE Edgeline Systems provide powerful computing at the edge, reducing latency and improving response times for real-time applications like IoT and autonomous systems.
- TensorFlow Lite: Optimized for deploying AI models on mobile and edge devices, enabling AI capabilities directly on-device for faster inference and reduced reliance on centralized infrastructure.
Key points for IT decision-makers and c-level executives:
- Latency and performance: Ensure that your inference setup can handle real-time processing needs. HPE ProLiant Servers offer the power to meet high-performance requirements.
- Scalability: Consider if your organization’s current infrastructure can scale to handle increased inference demands or if cloud-based solutions are more practical for growth.
- Security: Verify that the deployment environment meets the necessary data protection and compliance standards.
- Edge capabilities: For use cases requiring rapid responses and low latency, evaluate whether deploying models at the edge with HPE Edgeline or similar systems fits your strategy.
By using HPE ProLiant Servers or other HPE servers, businesses can build a robust, secure, and scalable inference and deployment environment that supports a wide range of AI applications, from simple model hosting to advanced, distributed deployments.
What is needed at the application layer?
To achieve proficiency in the application layer of the AI stack, businesses need solutions that allow them to integrate AI capabilities seamlessly into their products and services. This layer represents the user-facing side of AI, where outputs from models are transformed into actionable insights, user interactions, or automated processes that deliver value to end users.
In-house application layer setup:
- Custom AI solutions and development: The application layer involves developing custom applications that leverage the power of AI models. HPE’s Gen AI Implementation Services offer businesses the expertise and resources needed to integrate generative AI models and other advanced AI functionalities into their applications. These services help tailor AI implementations to specific business needs, ensuring that solutions are not only powerful but also aligned with business objectives.
- User Interfaces (UI) and User Experience (UX): For AI applications to be effective, they need intuitive interfaces that enable end users to interact with AI outputs easily. Development teams can build dashboards, web applications, or mobile apps that display AI insights in an actionable format. HPE’s AI services include consultation and support to design interfaces that facilitate smooth user interaction and maximize the effectiveness of AI-driven insights.
- APIs for integration: Businesses often use APIs to integrate AI functionalities into existing systems and workflows. HPE’s AI services can assist in creating custom APIs for seamless integration, allowing AI models to communicate with other enterprise software or data platforms.
- AI-driven automation: Automating business processes is a key use of the application layer. HPE’s AI solutions can be leveraged to build applications that automate repetitive tasks, optimize operations, and improve decision-making processes. This can include everything from customer service chatbots to automated fraud detection systems.
- Customization and personalization: AI applications at this layer often focus on personalizing user experiences, such as providing tailored recommendations, dynamic content, and adaptive user interfaces. Businesses can work with HPE Gen AI Implementation Services to build and deploy applications that make personalized AI-driven interactions possible.
- Alternatives for the application layer:
For businesses that are looking for third-party solutions or additional tools to enhance their AI capabilities, the following options are noteworthy:
AI-powered SaaS platforms:
- Salesforce Einstein: Integrates AI capabilities within customer relationship management (CRM) tools to provide predictive analytics, customer insights, and automated workflows.
- IBM Watson AI Services: Offers a range of AI capabilities, from natural language processing (NLP) to machine learning, which can be integrated into enterprise applications to enhance user experiences and streamline operations.
AI application frameworks:
- Microsoft Azure Cognitive Services: Provides a suite of APIs and tools that allow businesses to embed AI capabilities like computer vision, speech recognition, and language understanding into their applications.
- Google Cloud AI: Offers pre-trained models and tools like Dialogflow for building conversational AI interfaces, as well as APIs for vision, translation, and data analysis.
No-code and low-code AI platforms:
- DataRobot: Enables organizations to build and deploy AI applications without extensive coding, making AI more accessible to business users and accelerating time to market.
- H2O.ai: A platform that supports rapid development and deployment of AI applications with minimal coding, perfect for businesses looking for a straightforward way to integrate AI.
Key points for IT decision-makers and c-level executives:
- Time to market: HPE’s Gen AI Implementation Services can expedite the development and deployment of AI-powered applications, ensuring businesses gain a competitive edge faster.
- Scalability and customization: Ensure that the chosen AI solutions offer flexibility to scale and adapt as business needs evolve.
- Integration capabilities: Evaluate whether the AI solutions integrate smoothly with existing enterprise systems for a cohesive technology stack.
- User-centric design: Prioritize tools and services that help design AI applications with user experience in mind, enhancing adoption and effectiveness.
By leveraging HPE’s Gen AI Implementation Services and other HPE AI solutions, businesses can create robust applications that fully harness the power of their AI models. These services guide companies through the process of developing, rolling out, and maintaining AI applications that deliver impactful results and drive strategic goals.
Partner with HPE
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