Core Machine Learning and AI Knowledge for NVIDIA Certified AI Associate

Core Machine Learning and AI Knowledge

As an NVIDIA Certified AI Associate (NCA), you need a solid understanding of fundamental machine learning and AI concepts. This includes:

1.1 Model Deployment, Scalability, and Reliability

You should be able to assist in deploying and evaluating the scalability, performance, and reliability of AI models under the guidance of senior team members. This involves understanding factors that impact model performance, scalability requirements, and techniques for monitoring and maintaining reliable AI systems.

1.2 Data Mining and Visualization

You need awareness of processes for extracting insights from large datasets using data mining, data visualization, and similar techniques. This includes familiarity with tools and methods for data exploration, feature engineering, and communicating findings through visualizations.

1.3 Building LLM Use Cases

You should be able to build use cases for large language models (LLMs), such as retrieval-augmented generation (RAG), chatbots, and summarizers. This involves understanding the architecture, training, and deployment of LLMs for various natural language processing tasks.

1.4 Curating Content Datasets

For RAG models, you need to curate and embed content datasets. This involves selecting, cleaning, and preprocessing relevant data sources to be used as the knowledge base for the LLM.

1.5 Machine Learning Fundamentals

You should have familiarity with the fundamentals of machine learning, including feature engineering, model comparison, and cross-validation techniques. This foundational knowledge is essential for building, evaluating, and tuning AI models.

Worked Example: Feature Engineering

Problem: Given a dataset with numerical and categorical features, how can we preprocess the data for use in a machine learning model?

Solution:

1. Numerical features: Normalize or standardize to a common scale
2. Categorical features: One-hot encode or use label encoding
3. Handle missing values (e.g., imputation or dropping rows/columns)
4. Split data into training and validation sets

1.6 Natural Language Processing Packages

You should have familiarity with the capabilities of Python natural language processing packages, such as spaCy, NumPy, and vector databases. These tools are essential for preprocessing text data, building language models, and working with embeddings.

1.7 Staying Up-to-Date with LLM Research

To identify emerging LLM trends and technologies, you need to read research papers, articles, and conference proceedings. This helps you stay informed about the latest advancements in the field and potential applications.

1.8 Creating Text Embeddings

You should be able to select and use models to create text embeddings, which are dense vector representations of text data. Embeddings are essential for many NLP tasks, such as text classification, clustering, and information retrieval.

1.9 Prompt Engineering

You need to use prompt engineering principles to create prompts that achieve desired results from LLMs. This involves crafting effective prompts, understanding prompt design patterns, and iteratively refining prompts based on model outputs.

1.10 Traditional Machine Learning Analyses

Finally, you should be able to use Python packages (e.g., spaCy, NumPy, Keras) to implement specific traditional machine learning analyses, such as regression, classification, and clustering. This knowledge complements your understanding of more advanced AI techniques.