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31 INTEL 2021 itself a subset of ML. Intel AI development tools focus on these two algorithmic approaches to achieving machine intelligence. But before diving into the specifics of each tool, let's examine how Intel breaks the AI development pipeline into three distinct phases: 1 Data Processing: Data is the fuel that makes AI work. The first crucial step is getting data from the real- world into a digital format ready for use downstream, whether it is images for object identification applications or audio samples for language processing applications. The process of collecting, cleansing, and classifying massive amounts of digital data is necessary to ensure that the model training stage of the Intel AI pipeline has a robust data set from which to draw upon. In short, more quality data results in more complete training, which in turn results in better accuracy when the model is put into use during the final inferencing stage. 2 Model Training: After one has built a library of data representing the problem space they wish to address, it is time to select the appropriate type of AI model and train that model using the data. This is no trivial matter as matching the problem to the correct solution affects the deployed model's accuracy and efficiency. Furthermore, training can occur across a wide variety of computational environments. From a single laptop computer for R&D purposes to large distributed server farms for enterprise deployment at scale, training can occur on devices with wide- ranging memory, storage, and CPU performance specifications. 3. Inference: The trained model does no good if it cannot deploy at scale and within its intended operating environment's computational constraints. For example, models running on cloud-based server hardware have considerably more resources than embedded devices designed to support edge computing use cases. Lastly, inferencing requires the models to be fed inputs from the real world in order to extract any utility from ML/DL algorithms. Figure 1: The Intel 4004 microprocessor, the microchip that made Intel a global semiconductor leader. (Source: Intel) Otherwise, it is like having an engine but no fuel; the engine might be engineered well but provides no functional purpose. Again, the use case significantly affects this step. In some cases, it may be low-power sensors feeding a field-programmable gate array (FPGA), or it may be an enterprise resource planning (ERP) software suite that runs on Intel Xeon server- grade hardware. A note on the difference between ML and DL algorithms. DL and neural networks (NN) are a subset of ML. Both are capable of improving with exposure to more data; however, ML requires human intervention to make adjustments while DL models can improve independently. In short, ML can imitate intelligence, whereas DL can learn and improve. oneAPI To Rule Them All Intel is very mindful of the learning gap (or "chasm" as they call it their educational materials) between experts in the field of artificial intelligence and the broader community of data scientists and analysts who simply want to harness AI for practical purposes. In recognition of the complexity and the significant interest of AI technologies, Intel provides many tools and resources for developers to leverage in their development process. Furthermore, most of the tools are provided as open- source and cost-free so that developers can leverage Intel AI tools with their current workflows or datasets. Thus, if your organization has already made significant investments in Apache Hadoop or Spark for big data analysis, you need not throw it away to take advantage of ML/DL algorithms. Intel's AI strategy is nestled into their larger oneAPI initiative. oneAPI is an open, unified programming model built on standards to simplify the development and deployment of data-centric workload on any type Figure 2 Intel's Artificial Intelligence website. (Source: Intel)