Large Language Models (LLMs) on machine learning (ML). It introduces the concept of Generative AI pipelines
Traditional machine learning (ML) follows a structured process involving data pre-processing, feature engineering, training, tuning, and deployment, with a focus on extracting meaningful features for model performance. In contrast, generative AI emphasizes prompt engineering, fine-tuning language learning models, and deployment. This approach shifts from traditional feature engineering to designing effective prompts that guide AI in producing desired outputs. The use of foundational and fine-tuned language learning models in generative AI enables more sophisticated content generation, surpassing the capabilities of traditional ML.
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Traditional ML Pipeline
A machine learning (ML) pipeline is a series of automated processes that are chained together in order to design, train, deploy, and maintain machine learning models. It encompasses the entire workflow from data preparation and feature engineering to model training, evaluation, and deployment. A typical ML pipeline consists of several key stages:
Data Collection and Preparation:
In this foundational phase, the pipeline is initiated by gathering pertinent data from diverse sources. The raw data undergoes a meticulous cleansing process, addressing missing values, outliers, and other anomalies. Subsequently, the data is judiciously divided into training and testing sets, laying the groundwork for subsequent stages.
Feature Engineering:
The pipeline then delves into the artistry of feature engineering, where the selection and transformation of pertinent features occur. This involves handling categorical variables, scaling numerical features, and, if necessary, crafting novel features. The goal is to sculpt a dataset that optimally fuels the subsequent model training.
Model Training:
Choosing a machine learning algorithm tailored to the specific problem at hand is pivotal. The training data is partitioned into subsets for training and validation purposes. The model is then honed on the training data, setting the stage for the subsequent evaluation.
Model Evaluation:
The model’s prowess is meticulously evaluated using the validation set. This phase involves scrutinizing performance metrics, fine-tuning hyperparameters, and iterating on the model or algorithm based on evaluation results. It’s a delicate dance of refinement to ensure the model meets or exceeds predefined benchmarks.
Model Deployment:
With a polished model in hand, the focus shifts to deployment. This involves seamlessly integrating the model into a production environment, complete with an interface for the model to receive fresh data and generate predictions. Scalability and efficiency take center stage in this crucial deployment process.
Monitoring and Maintenance:
Beyond deployment, the pipeline extends into the vigilant realms of monitoring and maintenance. Monitoring tools are established to track the model’s performance over time. Regular updates are orchestrated, incorporating new data or improved algorithms. This phase is also attuned to nuances like concept drift or shifts in data distribution, ensuring the model remains robust and relevant.
The entire pipeline, serves as an iterative process. It continuously propels information from the initial collection phase to the ultimate deployment phase. This iterative nature allows the machine learning systems to continuously learn and process new information, integrating insights from both data collection phases and user interactions. The seamless integration of these stages ensures that the machine learning pipeline remains adaptable and effective in evolving environments, providing a robust foundation for intelligent decision-making.
Generative Ai Pipeline
In recent years, the field of artificial intelligence (AI) has made significant strides, overcoming past setbacks with advancements in algorithms, computing power, and data collection.
Generative AI (GenAI), a subset of AI, has particularly flourished, producing digital content such as text, images, and audio based on existing data. Language models (LM) within GenAI, sophisticated neural networks, have gained considerable attention for their ability to interpret, summarize, and generate text, offering intelligent responses to human prompts by understanding word relationships. Major players like OpenAI, Google, Microsoft, Hugging Face, Anthropic , And Mistral provide a variety of commercial and open-source LLM.
Data Collection:
Data is a critical initial step in leveraging information for various business objectives. For projects such as developing consumer-facing chatbots, careful consideration must be given to the selection of relevant data sources. These sources may include company portals (e.g., Sharepoint, Confluent, Document storage) or internal APIs. Ideally, a push mechanism should be established to ensure a timely update of the Language Model (LLM) application for end consumers.
Vectorization Or Embedding :
Vectorization with metadata involves enriching data by incorporating additional information such as authorship, date, and contextual details. This integration of external knowledge into vectors enhances data retrieval by enabling smarter and more targeted searches.
Metadata associated with documents may be located in the portal or within the document’s metadata itself. However, when a document is linked to a business object (e.g., Case, Customer, Employee information), relevant information must be retrieved from a relational database. Addressing security concerns, it’s possible to include security metadata at this stage, which contributes to secure data access and aids retrieval in subsequent pipeline stages.
A crucial aspect of this process is the conversion of text and images into vector representations using Language Model (LLM) embedding models. For documents, a preliminary step involves chunking the text, followed by encoding, preferably utilizing on-premises zero-shot embedding models. This comprehensive approach facilitates effective vectorization and metadata integration, enhancing the overall data processing pipeline.
Vector Storing or Vector Indexing :
Vector indexing is a crucial aspect of managing vector representations, commonly used in applications like Language Model (LLM) systems. Vector databases or indexes play a vital role in efficiently storing and indexing these representations as embeddings. Serving as the “LLM source of truth,” these databases need to remain synchronized with the underlying data sources and documents. Real-time indexing becomes particularly essential for LLM applications that cater to customers or generate business-related information, preventing any inconsistencies between the LLM app and its data sources.
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Information Retrieval:
Extracting information from data through a RAG based approach involves retrieving pertinent context from a document. This method responds to user queries in real-time, retrieving relevant data from an index and subsequently passing it to the model for processing.
Conclusion
The comparison between Traditional ML Pipelines and Generative AI Pipelines reveals a paradigm shift from structured feature engineering in traditional ML to prompt engineering and language model fine-tuning in Generative AI. While traditional ML focuses on refining models through data preprocessing, training, and deployment stages, Generative AI emphasizes prompt-based content generation using sophisticated language learning models. This shift not only transforms the approach to data processing but also showcases the versatility of Generative AI in producing more sophisticated and context-aware outputs.
