AIML Clusters and Models
AI-ML Compute Clusters
Due to constant technological advancements across all industries, humans produce, collect, store, and analyze more data than ever before. Data analysis is one of the most compute-intensive activities organizations face today. Training AI models requires substantially more compute resources to process vast datasets and optimize complex algorithms over numerous iterations.
To combat the need for such resources, AI-enabled systems commonly deploy a compute cluster, a group of interconnected computers, or nodes, that act as a cohesive system. For a cluster to be effective in reducing the time it takes to complete computing tasks, the nodes of the cluster must process data in parallel. By breaking up a workload across many nodes that handle a small chunk of the overall task, computing time can be reduced significantly.
Training an advanced AI model with clusters containing thousands of nodes still takes weeks or months. A model could have billions or trillions of parameters and data points. The process of training an AI model at this scale without the improved performance from clusters could take hundreds of years.
Compute clusters are highly scalable so you can easily add more compute resources to the cluster to meet demand. Also, with multiple nodes, clusters promote high availability by ensuring that the failure of a node does not disrupt the operation of the entire system. Orchestration tools can help with managing compute clusters with functionality like automatically scaling up or down and replacing malfunctioning nodes.
Cluster Components
The nodes are the core of a compute cluster, handling the computational heavy lifting. The nodes in an AI-optimized cluster use GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) for computing. Unlike CPUs, which have a few powerful cores optimized for sequential processing, GPUs and TPUs are designed with many cores, often thousands. These devices are specialized to execute many simple, repetitive operations simultaneously, like calculating the value that represents the color a pixel should display and performing matrix and vector computations involved in training deep learning models.
Tasks like large-scale distributed training involve a great deal of node-to-node communication. Therefore, cluster nodes must be connected through a very high-speed network fabric like InfiniBand or RDMA over Converged Ethernet (RoCEv2), often using a spine and leaf network topology. Other tasks like running inference do not require high bandwidth but do require high redundancy, availability, and uptime. Because of the unique requirements of the various engineering or computing tasks related to AI, these tasks will often be handled by completely different clusters with similarly unique specifications. The same is true of distributed data storage. In general, inference with a trained model requires fewer resources than any step of the training process.
The specific configurations of each cluster can be set and managed through cluster management software. Due to the lightweight and portable nature of containerized applications, they are a common choice for applications running in clusters. Docker and Kubernetes (K8s) are powerful container orchestration platforms that can be used to deploy, manage, and scale these applications with ease. K8s offers several other helpful features as well, like automatically restarting failed containers, replacing containers, load balancing, storage orchestration, secret and configuration management, and more.
AI-ML Cluster Use Cases
Imagine you are a data scientist at a large tech company tasked with developing a new AI model to improve network performance and reliability for enterprise clients. The use of AI-ML clusters in the development of your AI model at each step of the AI development process enhances the workflow and enables you to achieve your objectives effectively.
The AI-ML cluster provides the ability to process vast amounts of network data, train complex models, tune an ML model with different parameters, optimize network performance through inference, and deploy a scalable system.
It is very important to preprocess and clean your training dataset before you start training and deploying your AI model. This task requires removing all irrelevant data, dealing with missing values, and formatting everything for use with the model. AI-ML clusters are excellent at data cleaning because they can handle massive datasets by distributing workloads over several nodes. As a result, even the largest datasets are quickly prepared, making your data pipeline robust and scalable.
Once your dataset is ready, you can train your model. Training on one machine with a large dataset could be tediously slow and inefficient. Using AI-ML clusters, training can be spread across multiple nodes, each working on some part of the data at the same time. Iterating through models more quickly speeds up development, allowing you to readily try different algorithms or architectures.
A significant amount of experimentation with various model architectures and hyperparameters may be required to find just the right AI model. These experiments can be performed concurrently in an AI-ML cluster. If you want to know how performance changes as a function of the base model, learning rate, or number of layers in a neural network, then you need to run many instances of these tests in parallel. In an AI-ML cluster, you can deploy multiple models or versions of the same model with different parameters simultaneously to quickly determine the best specification for your system.
Once your model is trained and optimized, you can then deploy it to monitor and optimize network performance in real-time. By using the collective processing power of the nodes, the AI-ML cluster can handle high volumes of inference requests simultaneously. As network traffic flows through your system, the model can quickly analyze patterns, detect anomalies, and optimize routes without delays.
Lastly, the ability to scale by adding more nodes to your AI-ML cluster ensures that you can handle increasing workloads without compromising performance. Whether you need to process larger datasets, train more complex models, or handle more inference requests, the scalability of the cluster allows it to grow with your needs. This flexibility ensures that your infrastructure can adapt to the dynamic demands of AI development, providing a future-proof solution for your projects.
Custom AI Models–Process
Sometimes, an off-the-shelf AI model is not going to be able to solve your problem or handle your specific workflow tasks. In these cases, you can build a custom model that is either built from scratch or is significantly modified from existing models to meet the precise needs of your application. Custom models offer higher accuracy, better performance, and greater flexibility than pre-built models, making them a much better fit for specialized tasks.
There are several steps involved in the process of building a custom AI model. First, you need to determine what the model is intended to achieve and how its success will be measured. This step guides the design, training, and evaluation of the model.
After defining what you are working toward, the data supporting that objective must be collected and processed for use in training your model. Depending on the intention, data used for training can come from various places, including existing databases, web scraping, sensors and other data collection devices. Data can also be purchased from an AI data marketplace like Innodata, Databricks Marketplace, or Defined.ai. The data you aggregate must be cleaned to remove noise and handle missing values for quality assurance. In this process, data transformation techniques like scaling data to a common range (normalization) can be applied to ensure that the data is suitable for analysis and modeling.
After ensuring you have a high-quality dataset, you develop your model by, first, choosing the model architecture you will use to train your model. The model architecture that you choose will depend on the type of problem that you are trying to solve. For example, neural networks are well suited for image recognition tasks, while decision trees might be appropriate for classification tasks. Once you decide on your architecture, you will most likely need to experiment with a few frameworks to see which performs the best for your task.
Part of the experimentation process is hyperparameter tuning, optimizing the parameters of a machine learning model that are not learned from the data but set before the training process. These parameters, which are known as hyperparameters, control various aspects of the training process and the structure of the model itself. Properly tuning these hyperparameters can significantly improve the model’s performance. Tuning involves adjusting parameters such as the learning rate, batch size, and the number of layers to improve performance. When working out these details, you should work with a smaller version of your dataset if time and cost are factors. Keep in mind that the best-performing model and settings will scale with the size of the training dataset.
Finally, the model is deployed to a production environment where it can make predictions on new data. Ongoing monitoring is necessary to ensure that the model maintains its accuracy and efficiency over time, as well as to improve the model going forward.
Custom AI Models–Tools
Various tools are available for each step in the process of building a custom AI model. There are various vendors that offer tools used for data pre-processing, model selection, training and tuning, performance evaluation, and deployment. Other than the tools listed to deploy a model, all the tools shown in the figure can be leveraged through Python. Python is considered the industry standard for most of the tasks involved in building custom ML models. Python offers an extension collection of libraries and frameworks, ease of use, strong community support, and integration capabilities.
For data collection and preparation, tools like Pandas and NumPy can be used for data manipulation and numerical computations. TensorFlow and PyTorch are popular frameworks for deep learning, offering flexibility and powerful features for building and training neural networks.
Other tools like Optuna provide automatic hyperparameter optimization, facilitating exhaustive and random searches over parameter values. This process allows you to try numerous settings as prescribed by the method you choose to determine the optimal configuration for your model.
Once you have decided on hyperparameter values and have trained your model, evaluation of its performance can be performed using visualization libraries like Matplotlib and Seaborn, which help create static, animated, and interactive visualizations.
It is common for an ML model application to be containerized. Platforms like Docker and Kubernetes can be used to deploy and manage any containerized application including those applications of an ML model deployment. Tensorflow is an open-source platform for machine learning using data flow graphs that allows machine learning algorithms to be described as a graph of connected operations.
Lastly, there are more robust tools that offer features that span across multiple steps of building a custom AI model. Tools like Scikit-learn, MLflow, and Kubeflow can be used to build, manage, and deploy machine learning models efficiently, ensuring streamlined workflows and scalable infrastructure. Scikit-learn offers comprehensive functionalities for data preparation, model selection and training, hyperparameter tuning, and model evaluation. Tools like MLflow and Kubeflow help with managing and deploying machine learning models, providing a platform to manage the end-to-end machine learning lifecycle, including experiment tracking, model packaging, and deployment.
Prebuilt AI Model Optimization
Prebuilt AI models are pre-trained machine learning models that have been developed and trained on large datasets by experts in the field of AI. These models are organized into categories like “Natural Language Processing,” “Computer Vision,” “Audio,” and so on.
To determine which AI model meets your specific needs, it is common to run initial training on a very reduced set of data from your training dataset with several models. The result of this process establishes a baseline that indicates which model will perform best after fine-tuning.
Optimizing machine learning models is a necessary step in model development to improve their performance, efficiency, and reliability. If you skip this step, it would be like buying the right type of shoe but not the correct size. The configuration settings of a model change depend on the quality, size, and other aspects of the training dataset or the AI task. The optimization process ensures that models quickly deliver predictions that are as accurate as possible and use computational resources efficiently by aligning the model with the specific task.
Hyperparameter Tuning
Hyperparameter tuning is one of the most important steps of the model optimization process. The most impactful and well-known hyperparameters include the learning rate, the number of layers in a neural network, and the number of neurons per layer.
The learning rate controls how much the model’s parameters are adjusted with each iteration or epoch during training. In other words, the learning rate controls how many times the learning algorithm will work through the entire training dataset. The number of layers and neurons per layer determines the model's capacity and complexity, influencing its ability to learn from data and generalize to new data. Other common hyperparameters, such as the number of epochs and batch size (the number of training examples used in one iteration), also play an important role in the model's training process and performance.
The right combination of hyperparameters can significantly affect a model’s ability to learn from data and generalize to new, unseen data. Proper tuning can substantially improve model performance, making it more accurate and reliable in real-world applications.
Feature Engineering
Features are the characteristics associated with the data that help an AI model make predictions. For example, in a real estate AI application, features include the size of a house, the number of bedrooms, and the location. The feature engineering process includes picking the most appropriate features and then changing them to be more useful. This process includes scaling the features to a common range, converting categories to numbers, and ensuring that the features are in the right format for the model to understand.
Techniques like pruning and quantization can be used to boost efficiency and reduce inference time in models. Pruning removes redundant parameters, like unnecessary neurons in a neural network or redundant weights in a layer, simplifying the model without significantly affecting performance. Quantization reduces the precision of model parameters, speeding up inference and decreasing model size.
Regularization Techniques
Regularization techniques add small penalties to prevent the model from getting too complex, reducing the amount of irrelevant data. Regularization techniques that can be used to ensure that your model performs well include the following.
Regularization Techniques:
Modem Ensembling: is the process of combining multiple models to make prediction or classifications. Ensembling methods, like bagging (averaging diverse models), boosting (sequentially improving weak models), and stacking (combining model outputs), combine multiple models to make predictions more accurate and reliable.
Dropout: randomly ignores some parts of the model during training to attempt to improve performance
Data Augmentation: creates more varied training data by making random changes, like rotating or flipping images, helping the model learn better.
Cross-Vvalidation: tests the model on different parts of the data to ensure it works well on new, unseen data. A common approach to training and testing your model is to split the training dataset and use 75% of it for training and the other 25% for testing. In cross-validation, you repeatedly split the training dataset into different training and testing datasets to ensure that the model can perform across different data segments.
Pre-Trained AI Models
Training your own machine learning model takes time and your own data and powerful computational resources. A pre-trained model is a kind of machine learning model that has been trained on a large dataset and can be used as a standalone model or as a starting point for custom fine-tuned models.
BERT, which is used in natural language processing, and ResNet, used for image recognition, are examples of popular open-source pre-trained models. These models can be customized or fine-tuned on your specific dataset, enabling you to build on the knowledge already internalized by the model. Using the extensive training that the pre-trained model has already undergone not only saves you time and money but also produces results that are almost impossible for you to reach with limited data.
Like many open-source software products, free-to-use pre-trained models are hosted in repositories. Popular repositories include TensorFlow Hub, PyTorch Hub, and Hugging Face Model Hub which offer a wide range of models for different tasks like image classification, text processing, and more. As seen in the following figure, the Hugging Face website has models organized by the categories and subcategories of ML tasks, making it easy to get started looking for a model that fits your needs.
These platforms not only provide access to high-quality pre-trained models but also offer tools and documentation to help you integrate them into your project. Fine-tuning tailors the model to excel at your specific task while applying the knowledge it gained during pre-training before you get your hands on it.
To incorporate a pre-trained model with your specific dataset, you would perform the following:
Install the Hugging Face Python library, which includes the pre-trained model.
Load the pre-trained model and its tokenizer, a tool that converts data into numerical data to be used as input data for the model.
Incorporate your own data by preparing your dataset in a format compatible with the model, typically by tokenizing text or preprocessing images.
Train the model on your dataset, allowing it to adjust its parameters to fit your specific data better. You might use a lower learning rate to make fine adjustments rather than drastic changes, as the model already has a good understanding and performance from its initial training.
Monitor the model's performance on your data, adjusting hyperparameters if necessary, until it achieves the desired level of accuracy and generalization.
AI Model Parameters
AI model parameters are the internal variables that a machine learning model adjusts during training to make accurate predictions. These parameters are learned from the data itself, meaning they change as the model is trained to better fit the data. Model parameters are unlike hyperparameters, which are set before training and guide the learning process without being directly influenced by the data. Model parameters shape how a model interprets input data and generates predictions. As a model learns, it continuously adjusts these parameters to minimize errors, effectively "learning" the best values that lead to the most accurate outcomes.
Common model parameters in machine learning include weights and biases:
Weights—the factors applied to each input feature to determine their influence on the model's prediction.
Bias—an additional constant value that allows the model to make accurate predictions even when all input features are zero.
In a linear regression model, for example, an AI model multiplies each input feature by its corresponding weight and then sums these products together. The bias is then added to this sum to shift the output, providing a final prediction.
For example, in a model that is attempting to determine the price of a house, there are three features that represent factors such as square footage, number of bedrooms, and location. Through training, the model will determine the appropriate weights and therefore the bias of these features based on how they seem to affect the final home value across the dataset.
Unlike a deep neural network, linear regression models like the one shown only have one layer, the input layer, in which the input values are used in the model’s algorithm, and a value is produced. Although this example is a fairly simple linear regression, there could be thousands or even millions of features in an application like genome analysis or text processing that would require an extremely complicated and fine-tuned algorithm.
Since training is how parameter values are determined, to improve their accuracy, you need to make changes to the elements that you can manually configure, like the hyperparameters, features, and data quality. Understanding and optimizing these elements is key to ensuring that the model performs well not just on training data but on new, unseen data as well.
Service Placements – On-Premises vs. Cloud vs. Distributed
Like any application, there are numerous considerations when determining how and where you will host your AI/ML service. Service placement may depend upon functional and non-functional requirements of the system:
Functional requirements — define WHAT a system does, like scalability, integration with existing infrastructure or services, user experience, and security.
Non-functional requirements — define HOW the system performs its functions. This category includes things like latency, cost, reliability, availability, regulatory compliance, and geographic distribution.
On-premises, cloud, and distributed AI/ML service placements have their benefits and drawbacks. The placement you choose depends upon which consideration is most important to your organization.
On-Premise
On-premises service placement involves hosting AI/ML clusters within the organization’s own data centers. This strategy has great security and control benefits, which are necessary for managing sensitive data, especially in industries with strict compliance requirements, such as finance and healthcare. On-premises deployment also reduces latency, which is vital for real-time decision-making in situations like monitoring network traffic to detect security threats or optimizing network performance. On-premises deployments allow for close integration with existing network infrastructure so that AI/ML models can work together smoothly with other network management tools.
Unfortunately, the costs of deploying a model or application on-premises are very high as they involve substantial upfront investment in hardware and continuous maintenance efforts. Scaling this infrastructure up to deal with increasing demands can prove difficult and costly, requiring large resource commitments.
Cloud
Cloud placement services provide a lot of advantages for AI/ML applications, primarily scalability and flexibility. Cloud platforms enable organizations to scale their AI/ML resources up or down depending on network analytics and fluctuating demand. This capability makes it easier to deal with large amounts of network traffic or save money when activity is low. Cloud facilitates deploying advanced networking tools without building and maintaining infrastructure internally. Cloud deployment also provides centralized management capabilities, which are beneficial for organizations managing complex, geographically dispersed networks.
However, the drawbacks associated with using cloud placements include a greater security risk that may exist if networking data is processed in the cloud. Also, there could be delays in real-time processing, which is vital for tasks such as detecting and responding to network activity and generating insights. Organizations also depend on the cloud provider’s reliability and pricing, which can affect long-term operational stability.
Distributed
Distributed service placements offer a hybrid approach that combines the benefits of both on-premises and cloud environments, making it particularly well suited for AI/ML applications in networking that require both low latency and scalable processing power. In a distributed deployment, AI/ML models can be deployed at the edge of the network—closer to the data source—to provide real-time analytics and decision-making. Conversely, larger-scale data processing and storage might occur in the cloud. This approach enhances resilience through geographic redundancy, ensuring that if one part of the network or infrastructure fails, other parts can continue to operate and provide critical services.
However, managing a distributed environment is complex, requiring sophisticated tools and expertise to synchronize data and workloads across different locations. This complexity can increase costs, as organizations need to maintain both on-premises and cloud resources. Ensuring data consistency and compliance across multiple environments also presents significant networking challenges, particularly when dealing with large and sensitive datasets.
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