Building, Deploying & Fine-Tunning AI Models

Year

2024

Industry

Artificial Intelligence

Building, Deploying & Fine-Tunning AI Models
With our expertise, you can efficiently harness the power of AI technology to drive innovation, improve decision-making, and enhance operational efficiency within your organization.

Maximize Performance with AI-Tailored NVIDIA A100 and H100 GPUs
Unlock the full potential of your AI workloads with our range of high-performance GPUs. Our offerings include AI-optimized NVIDIA A100 and H100 GPUs integrated into DELTA HGX Baseboards, featuring 8 GPUs interconnected by NVLink. Additionally, we provide L40s and other GPUs available in the PCIe form factor.

Efficient Onboarding and Support Services
Benefit from our onboarding process and expert assistance tailored to your needs. Whether you require support with complex cases or optimization of platform usage, our team is dedicated to reducing your problem-solving time and ensuring a seamless experience.

Explore Our Marketplace for AI-Specific Tools
Discover a wide range of AI-specific tools from leading vendors, including OS images and Kubernetes® apps. Our marketplace provides the perfect workspace for data scientists and ML engineers, offering everything you need to enhance your AI projects.

Understanding the Difference : Model Training vs. Fine-Tuning

The process of developing machine learning models can be categorized into two main approaches: model training and fine-tuning. While model training involves building a model from scratch, fine-tuning adjusts an existing, pre-trained model to meet specific requirements. Here’s a practical comparison of model fine-tuning versus model training:

Aspect	Model Training	Model Fine-tuning
Starting Point	Begins with a blank slate, no prior knowledge	Starts with a pre-trained model
Data Requirements	Requires large, diverse datasets	Can work with smaller, specific datasets
Time and Resources	Often time-consuming and resource-intensive	More efficient, leverages existing resources
Objective	To create a general model capable of learning from data	To adapt a model to perform better on specific tasks
Techniques	Involves basic learning algorithms, building layers, setting initial hyperparameters	Involves hyperparameter tuning, regularization, adjusting layers
Challenges	Needs extensive data to avoid overfitting, underfitting	Risk of overfitting to new data, maintaining balance in adjustments
Metrics	Focuses on overall accuracy, loss metrics	Emphasizes improvement in task-specific performance
Best Practices	Requires careful data preprocessing, model selection	Necessitates cautious adjustments, validation of new data