In 2030, AI will likely contribute around $15.7 trillion to the global economy. Organizations that invest significantly in AI and leverage practices that accelerate and scale AI development have been shown to gain the highest ROI from AI and the most significant competitive edge.
But developing AI solutions is complex – from data engineering and model training to deployment and scale. In addition, AI use cases and workloads are only increasing and diversifying throughout recommendation, vision, and speech systems. As a result, software tools that streamline and fast-track AI development have become more pivotal to the developer journey.
I’m Ronald van Loon, an Intel Ambassador. As an industry analyst for over two decades, Intel has given me insights into what organizations are experiencing as they strive to resolve common AI roadblocks and optimize AI workflows.
Modern organizations must provide their AI developers with the tools, data, and resources needed to succeed at every stage of the AI workflow. This ensures that AI projects are effective and developers are engaged, motivated, and equipped to address existing and new challenges – including business outcome challenges, data science challenges, and challenges adapting to changing AI trends.
An extensive, open software ecosystem, like Intel’s AI-optimized software, is the central ingredient to supporting an efficient, performance-driven AI developer journey that enables faster time-to-solution, effective and scalable deployment, and opens up new opportunities for innovation.
According to Deloitte, AI deployments are up significantly this year. 79% of respondents say they fully deployed three or more types of AI compared to just 62% in 2021.
However, deriving actionable insights expeditiously and effectively via AI solutions is a top obstacle for organizations regardless of industry. Therefore, organizations must invest significantly in skills, integration, and explainability to derive insights that lead to effective decision-making.
Infrastructure (including data science systems and training clusters), data availability, model architecture, and ethics are challenges for developers building AI systems and creating effective AI models. AI software creators need the capabilities to overcome these common roadblocks stalling productivity and AI implementation.
Choosing and optimizing the right model architecture can be complex because AI models are computationally intensive and require specialized hardware. Concurrently, there’s challenges surrounding decision-making accuracy since this depends on the explainability of AI systems to create responsible, trusted, ethical AI models.
Organizations must enable AI creators and developers to overcome industry technology and AI challenges with a development and deployment ecosystem and access to AI-optimized hardware.
An open ecosystem enables AI better than anything else because it provides access to a large and diverse pool of resources, data and expertise that can be leveraged to drastically improve AI algorithms and applications. These resources enable developers to collaborate, share code, and build on each other’s work, encouraging interdisciplinary collaboration and innovation – accelerating progress and potentially leading to breakthroughs in AI research and the development of new applications that can solve complex problems.
An open ecosystem also promotes transparency and accountability in AI development, which is essential to build trust, ensure ethical practices, and identify any possible biases and errors that can arise in AI systems. Furthermore, an open ecosystem allows developers to customize and tailor their AI solutions to specific use cases, which can help to improve performance and accuracy. With access to open-source code and tools, developers can modify algorithms and models to better suit their needs and test new approaches to AI development.
For example, the BMW Group uses automated image processing to detect defects in production and quality control because machine vision is much faster and more accurate than manual human examiners.
The production staff is relieved, and their AI application performs the more demanding inspection tasks. As a result, BMW can get closer to realizing its vision of democratizing AI inferencing and utilizing a no-code solution so that data scientists, machine learning specialists, and everyday employees and business users can use AI workloads for quality control.
Data scientists and employees in manufacturing need to enhance their AI workloads by enabling capabilities on a desktop PC. BMW created an application using an open-source toolkit that enhances machine learning and deep learning model development.
Using the Intel Distribution of the OpenVINO toolkit, which uses deep learning frameworks, The BMW Group have created APIs and tools so anyone can use an AI application for object recognition in the future on their PC.
Another company, EXOR International, a leading producer of industrial automation equipment, wanted to better utilize their manufacturing data to overcome supply chain disruption constraints and further their journey towards industry 4.0 and 5G smart factory innovation.
EXOR wanted to accelerate manufacturing digitization and connectivity to facilitate machine learning automation, converge technologies like AI, data, and IIoT, and use an open-ended industry standard platform to enhance interoperability between systems and machines.
EXOR used Intel technology in its smart factory and its products by accessing a complete solution, including infrastructure hardware, software, and IP libraries. Intel’s open industry standard technologies, like accelerators, graphics, next-gen CPUs, and open-source software, has enabled EXOR to make its way towards industry 5.0.
AI developers need support in whatever way they consume software with open-source programming models and various tools and kits to accelerate time-to-solution. The aim is to meet developers where they are and support them in their preferred software usage.
AI developers and creators require optimizations for popular deep learning, machine learning, and big data analytics frameworks and libraries like TensorFlow, PyTorch, scikit-learn, XGBoost, Modin*, and Apache Spark*.
The right open-software ecosystem gives developers access to a rich suite of optimized libraries, frameworks, and tools for their AI development needs, including data preparation, training, inference, deployment, and scaling. Businesses can deploy workloads across diverse AI hardware with development tools built on an open, standards-based, unified oneAPI programming model and constituent libraries.
Co-optimized hardware architectures and software tools enable unmatched performance for diverse AI workloads, allowing developers to witness performance benefits with just one line of code change. Implementing these technologies is critical for achieving next-gen business goals, as they enable faster execution of AI workloads, scalability, and drive business agility to respond to changing market conditions. These benefits are particularly important for advanced business use cases that require the utilization of complex AI workloads and large datasets.
The oneAPI open standard promotes maximum code reuse across different stacks and architectures. This is a significant advantage over proprietary environments where code must often be rewritten to support new hardware targets.
With oneAPI, businesses can easily add GPUs and other specialized accelerators with less complexity while maintaining performance and high fidelity without being locked into specific hardware. By providing an open standard that supports code reuse, organizations can reduce the complexity and costs associated with developing AI solutions that are optimized for specific hardware targets.
Developing AI solutions is a complex process that involves data engineering, model training, deployment, and scale. Organizations need to derive actionable insights quickly and efficiently, choose and optimize the right model architecture, deal with computational-intensive AI models that require specialized hardware, ensure the explainability of AI systems, and address ethical concerns.
An open software ecosystem can help organizations overcome these challenges by providing AI developers with the tools, data, and resources needed to succeed at every stage of the AI workflow. AI is quickly evolving and an open ecosystem offers organizations the flexibility to evolve with it, empowering them to customize it to their needs, which is particularly beneficial as there’s no one-size-fits-all approach to AI.
With an open ecosystem, developers also have choices and full control to meet their unique needs, which creates an environment of constant innovation. Also, an open ecosystem allows developers to easily and efficiently pivot alongside the changing needs of their organization.
Moreover, an open ecosystem can help to reduce barriers to entry for new developers and startups looking to enter the AI space. By providing access to open-source code, tools, and knowledge, an open ecosystem can help to level the playing field and enable new players to compete with established players. This can drive innovation, increase competition, and ultimately benefit end-users
An open software ecosystem, like Intel’s AI-optimized software, helps organizations address the common roadblocks that are stalling productivity and AI implementation and enable AI creators and developers to overcome industry technology and AI challenges.
Check out Intel to learn more about the resources needed to optimize AI solution preparation, development, deployment, and scale.
By Ronald van Loon
