Core Machine Learning and AI Knowledge for NVIDIA AI Associates

Core Machine Learning and AI Concepts

As an NVIDIA Certified AI Associate, it is essential to have a solid understanding of fundamental machine learning and AI concepts. This knowledge forms the foundation for deploying models, extracting insights from data, and building LLM use cases effectively.

Model Deployment and Evaluation

You should be able to assist in the deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members. This involves:

• Understanding deployment strategies and model optimization techniques
• Monitoring model performance metrics and identifying issues
• Ensuring model reliability and robustness across different use cases

Data Mining and Visualization

Gain awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques. This includes:

• Applying data mining algorithms to uncover patterns and trends
• Using data visualization tools to present insights effectively
• Familiarity with techniques like clustering, classification, and regression

Large Language Model (LLM) Use Cases

Develop skills to build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers. This involves:

• Understanding the architecture and training of LLMs
• Implementing RAG models for question-answering and knowledge retrieval
• Building conversational chatbots and text summarization systems

Worked Example: Curating Content Datasets for RAGs

To build an effective RAG model, you need to curate and embed relevant content datasets. This involves:

1. Identifying authoritative sources for the domain of interest
2. Preprocessing and cleaning the content data
3. Embedding the content using techniques like sentence transformers
4. Storing the embeddings in a vector database for efficient retrieval

Machine Learning Fundamentals

Gain familiarity with the fundamentals of machine learning, including:

• Feature engineering and selection
• Model comparison and evaluation techniques (cross-validation, metrics, etc.)
• Implementing traditional machine learning analyses using Python packages like NumPy, Keras, and scikit-learn

Natural Language Processing (NLP)

Develop proficiency in using Python natural language packages like spaCy and NLTK for tasks like:

• Text preprocessing (tokenization, stemming, lemmatization)
• Named entity recognition and part-of-speech tagging
• Creating text embeddings using techniques like word2vec and BERT

Research and Prompt Engineering

To stay updated with emerging LLM trends and technologies, you should:

• Read research papers, articles, and conference proceedings
• Understand prompt engineering principles to create effective prompts
• Experiment with different prompting techniques to achieve desired results