Core Machine Learning and AI Concepts 1.1 Model Scalability, Performance, and Reliability Work under senior guidance to assist in deploying, evaluating, and opt...
Work under senior guidance to assist in deploying, evaluating, and optimizing machine learning models for scalability, performance, and reliability. This involves understanding techniques for model parallelization, distributed training, hardware acceleration (e.g., NVIDIA GPUs), and monitoring model inference performance.
Develop an awareness of the processes involved in extracting insights from large datasets using data mining, data visualization, and similar techniques. This includes familiarity with tools and libraries for data exploration, feature extraction, and communicating findings through visualizations.
Build and deploy LLM use cases such as retrieval-augmented generation (RAG) models for question answering, chatbots, and text summarization. Understand the architectures, training processes, and challenges involved in developing LLM applications.
Learn how to curate and embed content datasets for use in RAG models. This involves techniques for corpus preprocessing, document embeddings, and indexing to enable efficient retrieval of relevant information during model inference.
Develop a strong foundation in the fundamentals of machine learning, including feature engineering, model selection and comparison, cross-validation techniques, and understanding bias-variance tradeoffs. This knowledge is essential for building effective machine learning models.
Gain familiarity with the capabilities of Python natural language processing packages such as spaCy, NumPy, and vector databases. These tools are crucial for tasks like text preprocessing, feature extraction, and efficient storage and retrieval of text embeddings.
Stay up-to-date with the latest advancements in LLM research by reading and comprehending scientific papers, articles, and conference proceedings. This will help you identify emerging trends, technologies, and best practices in the rapidly evolving field of large language models.
Learn how to select and use appropriate models for creating text embeddings, which are dense vector representations of text that capture semantic and contextual information. These embeddings are essential for tasks like text similarity, clustering, and information retrieval.
Understand and apply prompt engineering principles to create effective prompts that guide language models to achieve desired results. This involves techniques for prompt design, few-shot learning, and iterative refinement of prompts based on model outputs.
Gain hands-on experience in using Python packages such as NumPy, Keras, and scikit-learn to implement traditional machine learning analyses, including supervised and unsupervised learning algorithms, model evaluation metrics, and data preprocessing techniques.