Data Collection and Wrangling

Gathering and migrating relevant data for your AI project typically involves collecting data from multiple databases and unstructured data sources, then scrubbing and validating it to determine relevance, and sufficient quantity/quality. For this, speed, scalability, and price make BOXX PAC Data storage solutions the ideal choice.

Data Preprocessing

In most instances, cleaning and preprocessing collected data in order to remove noise, handle missing values, and prepare for analysis involves data cleaning, feature engineering, normalization, and other techniques which improve data quality and relevance.  To accelerate these processes, rely on BOXX AI base model data science workstations. 

Model Selection & Training

Selecting, testing, and training the model on preprocessed data to learn patterns and relationships requires feeding the data into the model, adjusting its parameters using optimization algorithms, and iteratively refining the model's performance. For this, minimal CUDA and Tensor cores may be sufficient. However, greater or maximum cores may also be required to process model data, epochs/iterations, and compute for the preferred framework.

Model Evaluation and Tuning

Evaluating trained model performance requires metrics and validation to assess how well it generalizes to new unseen data and identifies overfitting, underfitting, and other issues. Based on those results, fine-tuning hyperparameters further optimizes performance. Minimal cores may be sufficient for a modest project, but the workload may require greater or maximum performance.

Deployment

AI inferencing through integration into existing software systems, building APIs for access, or deploying it on edge workstations, on-prem infrastructure, or cloud environments justifies the use of AI workstations (see Why Workstations for AI). Multiple base model workstations are preferred for most inferencing environments, however fewer, more powerful workstations are an option.