Software Development and AI Integration for NVIDIA Certified Associates

Overview

As an NVIDIA Certified AI Associate, you will need to understand software development practices and how to integrate AI models into applications. This includes the ability to create, maintain, and test software, deploy and evaluate model scalability and performance, build language model use cases, utilize Python natural language packages, identify system requirements, monitor data processes, implement machine learning analyses, and write software components.

Software Development and Testing

You should be able to assist in the full software development lifecycle, including creating, maintaining, and testing software components or scripts under the supervision of senior team members. This involves following best practices for coding, documentation, version control, and testing to ensure software quality and reliability.

AI Model Integration

A key aspect of this domain is understanding how to effectively integrate AI models, particularly large language models (LLMs), into applications. This includes:

• Deploying and evaluating model scalability, performance, and reliability under supervision
• Building LLM use cases such as retrieval-augmented generation (RAG) models, chatbots, and summarizers
• Familiarity with Python natural language packages like spaCy, NumPy, and vector databases for working with text data

System Requirements and Data Pipelines

You should be able to identify the system data, hardware, or software components required to meet user needs for AI applications. This includes understanding data pipelines, monitoring data collection processes, experiments, and other software processes involved in building and deploying AI solutions.

Worked Example: Building an LLM Chatbot

Scenario: You need to build a chatbot application using an LLM model to assist customers on an e-commerce website.

Steps:

1. Identify system requirements: hardware (GPU, RAM, storage), software (Python, PyTorch, spaCy, vector database), and data (customer conversations, product information)
2. Set up data pipelines for collecting and preprocessing customer conversations and product data
3. Use Python packages like PyTorch and spaCy to fine-tune an LLM model on the collected data
4. Develop and test the chatbot application, integrating the fine-tuned LLM model
5. Deploy the chatbot application and monitor its performance, scalability, and reliability

Traditional Machine Learning

In addition to working with LLMs, you should have familiarity with Python packages like NumPy and Keras to implement traditional machine learning analyses, such as supervised and unsupervised learning tasks, under the guidance of senior team members.