Machine Learning Operations

Computational Resources:

Relies on standard CPUs and GPUs for general machine learning tasks.

Utilizes high-performance GPUs and TPUs, optimizing the performance and efficiency of large language models.

Performance Metrics:

Measures success using traditional metrics like accuracy, AUC, and F1 score, which may not fully reflect the performance of LLMs.

Employs specialized, language-specific metrics such as BLEU and ROUGE, providing deeper insights into LLM performance and business relevance.

Training Approach:

Typically requires significant amounts of data and time to train models from scratch, potentially delaying time-to-market.

Emphasizes the fine-tuning of pre-trained models, speeding up deployment and allowing faster response to business needs.

Human Feedback:

Incorporates human feedback less frequently, often depending on traditional evaluation methods.

Integrates reinforcement learning from human feedback (RLHF) regularly, ensuring that models continuously improve and adapt to evolving business objectives.

Hyperparameter Tuning:

Focuses on optimizing accuracy using conventional metrics, which might not always align with cost-effectiveness.

Prioritizes resource efficiency, reducing overall operational costs and enhancing the return on investment through strategic hardware and software optimizations.

Prompt Engineering:

Typically deals with input features and model predictions without much focus on prompt design.

Concentrates on creating effective prompts that guide LLM behavior, ensuring the outputs are aligned with business goals and provide actionable insights.

Building Pipelines:

Focuses on building and optimizing end-to-end ML workflows, generally linearly.

Crafts sophisticated pipelines that integrate multiple LLM calls and external interactions, enabling complex business processes and more dynamic decision-making.