Core Machine Learning and AI Knowledge for NVIDIA Certified AI Associate - GenAI LLM

Core Machine Learning and AI Knowledge

Understanding the fundamental concepts in machine learning and artificial intelligence (AI) is essential for aspiring professionals in the field. This knowledge forms the foundation for deploying scalable and reliable AI models.

1.1 Model Deployment and Evaluation

Assist in the deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members. This involves understanding how to monitor models in production and ensuring they meet performance benchmarks.

1.2 Data Insights Extraction

Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques is crucial. This includes utilizing tools and methods to uncover patterns and trends within data.

1.3 Building LLM Use Cases

Build Large Language Model (LLM) use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers. Understanding how to implement these use cases can significantly enhance user interaction and data processing capabilities.

1.4 Curating Content Datasets

Curate and embed content datasets for RAGs. This involves selecting relevant data that can improve the performance and accuracy of language models.

1.5 Fundamentals of Machine Learning

Familiarity with the fundamentals of machine learning, including feature engineering, model comparison, and cross-validation, is essential for evaluating model effectiveness and ensuring robust performance.

1.6 Python Natural Language Packages

Familiarity with the capabilities of Python natural language packages such as spaCy, NumPy, and vector databases is important for implementing various machine learning techniques.

1.7 Research Paper Analysis

Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies. Staying updated with the latest research helps in understanding the evolving landscape of AI.

1.8 Creating Text Embeddings

Select and use models to create text embeddings. This is a critical step in processing natural language and improving model understanding of context.

1.9 Prompt Engineering Principles

Use prompt engineering principles to create prompts that achieve desired results. Crafting effective prompts is key to leveraging the capabilities of LLMs effectively.

1.10 Implementing Machine Learning Analyses

Utilize Python packages such as spaCy, NumPy, and Keras to implement specific traditional machine learning analyses. These tools provide the necessary functionality to conduct various analyses and build predictive models.