Overcome the Challenges of AI Deployment
Enterprise organizations are racing to be among the first to deploy new, AI-enhanced capabilities that will help them stand out from the competition, maximize profitability and efficiency, and enhance their customer experience.
But as many solution architects, developers, and tech leaders are discovering, taking an enterprise AI project from proof of concept to production is no easy task. Enterprises face challenges throughout their journey—including building scalable, right-sized AI infrastructure, protecting privacy and security, and optimizing solution development time.
To help you solve these challenges and steer clear of other emerging AI pitfalls, this article shares essential considerations, insights, and tips for building enterprise AI solutions. We’ll also spotlight some of the Intel® hardware and software that can help enhance your results.
Whether you’re building from the ground up, creating a solution from prebuilt components, or purchasing a ready-made offering, here’s how you can streamline your enterprise AI efforts.
Don’t Invest in Specialized Hardware If You Don’t Need To
Among many technologists, there’s a misconception that AI workloads require a GPU or specialized discrete AI accelerator. Simply put, this isn’t true. This mistaken belief can lead organizations to assume they need to make a sizable hardware investment to enable AI, and they deem AI initiatives too costly for their business.
For example, classical machine learning workloads do not typically see much benefit from a discrete accelerator, making CPUs a highly efficient choice for algorithms such as logistic regression, decision trees, and linear regression.
Before investing energy and resources in advanced equipment such as GPUs (including licenses), be sure to investigate what your workload actually demands. You may be able to support AI workloads in the data center and at the edge using Intel® Xeon® Scalable processors. These processors come equipped with built-in AI engines that allow you to greatly accelerate AI workloads, both training and inference.
Two AI engines included in Intel® Xeon® Scalable processors allow you to deliver strong AI performance using a CPU-only architecture:
- Intel® Advanced Matrix Extensions (Intel® AMX): Maximize performance for deep learning training and inference workloads that rely on matrix math, including natural language processing, recommendation systems, and image recognition.
- Intel® Advanced Vector Extensions 512 (Intel® AVX-512): Accelerate AI, analytics, scientific simulations, financial simulations, and other compute-intensive tasks that involve vector-based computations.
Running AI workloads solely on Intel® Xeon® Scalable processors can help enterprises save on hardware costs while improving energy efficiency. At the same time, developers benefit by avoiding the complexity of specialized hardware and minimizing the need for code or application changes. Your organization is likely already invested in Intel® Xeon® Scalable processors, making adopting them for AI an easier choice.
While Intel offers a full range of extreme-performance AI solutions, we suggest most companies start with Intel® Xeon® Scalable processors. They can be used from edge to cloud to help maximize your enterprise AI efforts.
Intel is also working with major enterprise infrastructure platform providers such as RedHat and VMware to maximize AI flexibility and efficiency while accelerating performance from edge to cloud.
For Demanding Workloads, Think beyond the H100
While Intel® Xeon® Scalable processors can handle low- and medium-complexity tasks, your use case may involve demanding workloads that require an additional layer of specialized hardware. If your AI use case is demanding, you’ll face the need for a powerful discrete accelerator, often in the form of either a GPU or a purpose-built AI processor.
Of course, performance and efficiency are the name of the game here, and Intel® technologies offer a strong advantage compared to our competitors. For example, Intel® Gaudi 3 AI accelerators deliver:
- 1.5x faster time to train than NV H100 on average1
- 1.5x faster inference than NV H100 on average2
- 1.4x higher inference power efficiency than NV H100 on average3
You can review in-depth performance statistics for Intel® AI data center products here.
Software Tools Are Your AI Secret Weapon
Whether you’re building an AI solution from the ground up or hoping to create a solution leveraging a set of prebuilt elements, you’re likely aware that the software development and model training elements of AI are just as important as hardware. This is especially important as you strive to support the heterogeneous architectures that most enterprise AI initiatives will involve.
To help you accelerate time to value, Intel offers a broad portfolio of purpose-built software tools, reference kits, optimized frameworks and libraries, and prebuilt solution elements. Using our AI development resources, you can enhance developer productivity, optimize performance, and increase deployment flexibility for your AI capabilities.
We offer a range of tools that can have a major impact on your enterprise AI initiative, including:
- OpenVINO™ toolkit, which dramatically simplifies AI inferencing deployment with write once, deploy anywhere capabilities.
- AI Tools from Intel (formerly known as Intel® AI Analytics Toolkit), which gives data scientists, AI developers, and researchers familiar Python tools and frameworks to accelerate end-to-end data science and analytics pipelines on Intel® architecture.
- Drop-in AI framework optimizations, which can help unlock performance gains for popular deep learning and machine learning frameworks with minimal efforts.
Additionally, we offer a robust set of training and reference kits for critical enterprise AI use cases:
Be sure to also check out our full suite of AI reference kits built in collaboration with Accenture.
In addition to providing a robust portfolio of software tools, Intel plays an important role in the Linux Foundation Open Platform for Enterprise AI (OPEA). Here, we’re helping to develop an ecosystem orchestration framework to efficiently integrate generative AI technologies and workflows, leading to quicker adoption and better business value with collaborative development. Our contributions include a set of reference implementations with frameworks for:
- A chatbot on Intel® Xeon® Scalable processors and Intel® Gaudi® AI Accelerators
- Document summarization using Intel® Gaudi® AI Accelerators
- Visual Question Answering (VQA) on Intel® Gaudi® AI Accelerators
- A copilot designed for code generation in Visual Studio Code on Intel® Gaudi®AI Accelerators
Customize a Foundational Model to Accelerate Your Initiative
Every AI use case is unique, and many will require some degree of model training. The good news is that you don’t need to start from scratch.
Open source models, also known as foundational models, provide a starting point for your AI capabilities. These models can be customized and fine-tuned to fit the specific needs of your AI solution. Overall, the process of fine-tuning a foundational model is simpler and faster than building from scratch. The fine-tuning approach can save you valuable time as you race against the competition.
Intel® Xeon® Scalable processors are a great platform for model fine-tuning, allowing AI developers to:
- Achieve up to 10x higher PyTorch real-time inference and training performance with built-in Intel® Advanced Matrix Extension (Intel® AMX) accelerators4.
- Fine-tune a natural language processing (NLP) model, such as DistilBERT, in less than four minutes, which can reduce or eliminate the need for a dedicated accelerator.5
- Reduce prompt latency on the Llama 2 large language model (LLM) in conjunction with DeepSpeed, a deep learning optimization package.
Common foundational models to be aware of across major AI use cases include:
- Computer vision: CLIP and YOLO
- Generative AI: ChatGPT and Llama 2
- Natural language processing: ChatGPT, Llama 2, BERT
Learn more about the process of customizing open source models—also known as transfer learning. Also be sure to check out our developer resources built in collaboration with Hugging Face.
Expedite Generative AI Model Customization with Retrieval-Augmented Generation
Those pursuing generative AI applications can also take advantage of RAG to realize a faster path to value from AI.
In the RAG methodology, foundational large language models are connected to knowledge bases—often company-specific, proprietary data—to inject relevant context and information. Taking this approach, you can achieve customized AI capabilities while avoiding the need for additional model training, which can reduce your initiative’s overall costs and complexity.
Learn more about how to implement RAG in this article.
Turn Your Experts into Data Scientists
Harnessing the expertise of your team and translating it into intelligent AI capabilities is a sizable hurdle for many enterprise organizations—especially if your team isn’t technically inclined or comfortable with data science.
For computer vision applications, Intel offers the Intel® Geti platform, a simpler way for non-data scientists to help train models. With easy labeling and annotation, the Intel® Geti platform makes it easy to tap into your team’s expertise to create better, more accurate computer vision solutions. Full support for model exporting, retraining, and hyperparameter optimization means you can use the Intel® Geti platform as an end-to-end solution for critical computer vision use cases such as anomaly detection, classification, and object detection.
Apply Confidential Computing for Security and Compliance
Security and regulatory compliance are critical concerns for enterprises navigating the new AI era, especially when multiple sets of sensitive data need to be brought together during model training.
To help you protect sensitive data and workloads, regardless of where they’re running, Intel offers a suite of confidential computing capabilities as key features of Intel® Xeon® Scalable processors. These technologies are designed to protect data in use with isolation, encryption and control, and verification capabilities to help you unlock new enterprise AI opportunities.
Our confidential computing portfolio includes:
- Intel® Software Guard Extensions (Intel® SGX): Unlock new opportunities for business collaboration and insights—even with sensitive or regulated data.
- Intel® Trust Domain Extensions (Intel® TDX): Increase confidentiality at the VM level, enhance privacy, and gain control over your data.
- Intel® Tiber™ Trust Services (formerly known as Intel® Trust Authority): Take confidential computing to the next level with a zero trust attestation SaaS that verifies the trustworthiness of compute assets at the network, edge, and in the cloud.
Assess Your End User AI PC Fleet
If your AI use case involves end users within your organization running AI workloads locally, you’ll want to assess your fleet for AI readiness. AI workloads introduce new demands on the daily-use laptops and desktops of your team—and poor performance on these machines can compromise your upstream investment in AI capabilities.
To help streamline your AI PC investment, we offer the Intel® Core™ Ultra processor, which incorporates three different compute engines into a single package to supercharge AI performance for end users. This includes an integrated neural processing unit that can handle sustained, heavily used AI workloads at low power for greater efficiency. Intel has also worked with over 100 ISV partners on more than 300 AI-accelerated features to enhance PC experiences across audio effects, content creation, gaming, security, streaming, video collaboration, and more.
Bring AI Everywhere with Intel
The pressure is on to realize enterprise innovation via AI capabilities. Intel is ready to help you make it happen quickly and efficiently. We constantly work with industry-leading enterprise organizations to help them unlock the AI capabilities they need with maximum efficiency, performance, and security.
We also work with the ISVs, OEMs, SIs, AI specialists, and cloud providers that enterprises rely on to help make their AI transformation possible. Our scalable systems strategy—built on components from Intel and our ecosystem partners—makes it easy for enterprises to adopt AI. When you choose our platform, you benefit from years of practical experience making AI a reality for innovative organizations worldwide. Our broad, deep partner ecosystem ensures the openness and interoperability you need to deliver AI results today and tomorrow.
As you continue to explore AI possibilities, keep in mind that you and your team can experiment with many of the Intel® technologies discussed in this article using the Intel® Tiber® Developer Cloud.
You can also browse offerings from our ecosystem of AI partners in the Intel® Partner Showcase.