What Is AI Modeling?
Following data collection and data preparation, the third phase in the data pipeline involves the creation of intelligent machine learning models to support advanced analytics. These models use various types of algorithms, such as linear or logistic regression, to recognize patterns in the data and draw conclusions in a manner that emulates human expertise. Simply put, AI modeling is the creation of a decision-making process that follows three basic steps:
- Modeling: The first step is to create an AI model, which uses a complex algorithm or layers of algorithms that interpret data and make decisions based on that data. A successful AI model can act as a surrogate for human expertise in any given use case.
- AI model training: The second step is to train the AI model. Most often, training involves processing large amounts of data through the AI model in iterative test loops and checking the results to ensure accuracy, and that the model is behaving as expected and desired. Engineers are on hand during this process to modify and improve the AI model as it learns.
- Inference: The third step is known as inference. This step refers to the deployment of the AI model into its real-world use case, where the AI model routinely infers logical conclusions based on available data.
AI/ML is a complex process with high computational, storage, data security, and networking requirements. Intel® Xeon® Scalable processors, Intel® storage and networking solutions, and Intel® AI toolkits and software optimizations offer a variety of resources to help businesses quickly and securely design and deploy AI/ML solutions with ease and cost efficiency.
Machine and Deep Learning
AI is an umbrella term that refers to any methodology in which machines or computers emulate decision-making capacity based on available data like human operators. Machine learning is specifically the application of AI in the form of algorithms to enable automated tasks. A key attribute of machine learning is that as it churns more data, it learns and makes better decisions over time.
Deep learning is a specialized category of machine learning where the structure of AI algorithms is layered and more powerful, creating what is referred to as a neural network. Deep learning models will still undergo an iterative test-loop process where engineers will continually tweak the model to improve accuracy and get the model to recognize more layers of nuance beyond what machine learning is capable of.
Common Types of AI Algorithms
The purpose of AI models is to use one or more algorithms to predict outcomes or make decisions by trying to understand the relationship between multiple inputs of varying type. AI models differ in how they approach this task, and AI developers can deploy multiple algorithms in tandem to achieve a target goal or function.
- Linear regression maps the linear relationship between one or more X input(s) and Y output, often represented in a simple line graph.
- Logistic regression maps the relationship between a binary X variable (such as true or false, present or absent), and a Y output.
- Linear discriminant analysis performs like logistic regression except starting data is characterized by separate categories or classifications.
- Decision trees apply branching patterns of logic to a set of input data until the decision tree reaches a conclusion.
- Naive Bayes is a classification technique that assumes there are no relationships between starting inputs.
- K-nearest neighbor is a classification technique that assumes inputs with similar characteristics will be near each when their correlation is graphed (in terms of Euclidean distance).
- Learning vector quantization is similar to k-nearest neighbor, but instead of measuring the distance between individual data points, the model will converge like data points into prototypes.
- Support vector machine algorithms establish a divider, called a hyperplane, that distinctly separates data points for more accurate classification.
- Bagging combines multiple algorithms together to create a more accurate model, whereas random forest combines multiple decision trees together to get a more accurate prediction.
- Deep neural networks refer to a structure of many layers of algorithms that inputs must pass through, culminating in a final prediction or decision point.
The Technology Requirements of AI Modeling
AI models are becoming so large that more data is required to effectively train them, and the faster you can move the data, the faster you can train and deploy the model. Intel-based platforms help provide configurations tuned for AI workloads with high-performance CPUs, high-capacity storage, and high-bandwidth network fabrics that can handle dense traffic flow.
- 3rd Gen Intel® Xeon® Scalable processors offer high core count, high memory capacity, PCIe 4.0 connectivity, and AI and security features that are exclusive to Intel® platforms. Intel® Deep Learning Boost (Intel® DL Boost) accelerates deep learning inference while reducing its memory requirements. Intel® Software Guard Extensions (Intel® SGX) helps isolate workloads in memory for better system security and to enable federated learning of AI models in multiparty computing (AI models from different entities can train on the same encrypted datasets).
- Intel® Optane™ technology augments both storage and memory solutions. Intel® Optane™ DC SSDs deliver extreme capacity with PCIe interfaces that position data closer to the CPU and offer incredible I/O speeds. Intel® Optane™ persistent memory offers large capacity with near-DRAM performance and allows data in memory to persist through system reboots or shutdowns.
- Intel® Ethernet 800 Series Network Adapters and Intel® Silicon Photonics deliver speeds up to 100GbE and are the cornerstone for low-latency datacenter fabrics that empower your analytics engine.
Intel® Software Solutions for AI/ML
It’s easy to be overwhelmed by the sheer number of options for machine learning and deep learning software available on the market today. However, with Intel offerings, you can access a one-stop source for common frameworks and libraries, all of which are optimized for performance on Intel® platforms.
- The Intel® Distribution of OpenVINO™ toolkit allows you to optimize and accelerate AI inference on Intel-enabled platforms, supporting fast time to results. This toolkit is useful for both datacenter implementations and AI-enabled data generation or analysis in edge deployments.
- The Intel® AI Analytics Toolkit, part of Intel® oneAPI, offers pretrained AI models and includes the Intel distribution of common frameworks such as TensorFlow, PyTorch, and scikit-learn, all of which are optimized for performance on Intel-enabled platforms. These resources can help developers speed up their AI modeling efforts and accelerate time to deployment.
- Analytics Zoo is a unified platform of AI and analytics tools, designed for deep learning implementations and built on frameworks Apache Spark, TensorFlow, Keras, and BigDL. The platform also includes high-level abstractions and APIs, as well as built-in deep learning models to facilitate database integration and easy startup for deep learning projects.
Intel Partners for AI Analytics
Intel works closely with industry-leading partners in business intelligence and AI to integrate key technologies such as Intel® Xeon® Scalable processors into their analytics platforms. For business intelligence providers this includes SAP, Oracle, and SAS.
AI partners include Cloudera, Data Robot, Omni-Sci, H20, SAS, and Splunk. End customers will benefit from Intel-enabled security and AI acceleration features when using Intel® technologies with these platforms.