Tag Archives: ML

AI, LLM, Technology

What’s Cooking in the AI Test Kitchen – RAG

Cooking is one of my favorite hobbies, so I took the opportunity to combine my interest in technology and cooking to discuss this innovative chapter on Generative AI, RAG. First, let’s get the basics out of the way.

What is Generative AI? How is it different from the AI?

Artificial Intelligence (AI)

AI refers to computer systems performing tasks that typically require intelligence. It includes machine learning (ML) and deep learning (DL) methods. AI systems learn from data, identify patterns, and make decisions autonomously. Examples of AI applications include speech recognition and self-driving cars. While AI can simulate thinking processes, it does not have human consciousness.

Generative AI

Generative AI is a subset of AI that focuses on generating content. In contrast to AI, which leads by rules, generative AI employs self-learning models to produce innovative outputs. Examples of AI encompass text generation models like GPT 4 and image creation models like DALL E. This branch of AI merges creativity with innovation, empowering machines to create art, music, and literature. Nonetheless, it encounters challenges related to considerations, bias mitigation, and control over the generated content.

Generally speaking, AI techniques encompass applications, while generative AI stands out for its emphasis on creativity and original content creation. 

Now, back to our test kitchen. Let’s put on our virtual aprons to travel on a flavor-filled journey to understand how RAG (Retrieval-Augmented Generation) works in the realm of Generative AI. Imagine we’re in a busy kitchen, aprons on, ready to cook some insights. Let’s use a basic roasted chicken recipe for this analogy.

The Recipe

Ingredients:

  • Chicken is our base ingredient, representing the raw text or prompt. I recommend cleaned chicken (unbiased).
  • Seasonings: These are the retrieval documents, like a well-stocked spice rack. Each seasoning (document) adds depth and context to our chicken (prompt).

Preparation:

  • Marinating the Chicken: We start by marinating our chicken with the prompt. This is where RAG comes into play. It retrieves relevant documents (seasonings) from its vast knowledge base (like a library pantry).
  • Selecting the Right Spices: RAG carefully selects the most relevant documents (spices) based on the prompt. These could be scientific papers, blog posts, or historical texts. This is my favorite part.

Cooking Process:

  • Simmering and flavor injecting: Just as we simmer our chicken with spices, RAG injects the prompt with context from the retrieved documents. It absorbs the flavors of knowledge, understanding nuances, and connections.
  • Balancing Flavors: RAG balances the richness of retrieved information. Too much spice (document) overwhelms the dish (response), while too little leaves it bland.

Generative Magic:

  • The Cooking Alchemy: Now, the magic happens. RAG combines the marinated prompt with the seasoned context. It’s like a chef creating a new recipe, drawing inspiration from old cookbooks of classic dishes.
  • Creating the Dish: RAG generates an informed and creative response. It’s not just recycling facts; it’s crafting a unique flavor profile.

Serving the Dish:

  • Plating and Garnishing: Our dish is ready! RAG delivers a rich, layered, and tailored response to the prompt—like presenting a beautifully plated meal.
  • Bon AppĂ©tit!: The user enjoys the response, savoring the blend of retrieval and generation. Just as a well-seasoned chicken satisfies the palate, RAG satisfies the hunger for knowledge and creativity.

RAG reminds me of a beautiful meal that can satisfy the desires of the most discerning taste. A traveled chef who searches for the best ingredients from around the globe and retrieves them to generate tasteful dishes. So, next time you encounter RAG, think of yourself as a chef creating delightful technology-based feasts.

A curated list of retrieval-augmented generation (RAG) in large language models.

Roasted Garlic Chicken

AI, LLM, Technology

Drifting through AI

AI drift refers to a phenomenon in artificial intelligence where sophisticated AI entities, such as chatbots, robots, or digital constructs, deviate from their original programming and directives to exhibit responses and behaviors that their human creators did not intend or anticipate.

The accuracy of data is becoming more and more critical as we move forward in this space. Let’s consider “drift” in AI, why it’s happening, and how to monitor it using Machine Learning.

Factors Leading to AI Drift

  • Loosely Coupled Machine Learning Algorithms: Modern AI systems heavily rely on machine learning algorithms that are more interpretive and adaptable. Unlike traditional technologies focused on rigid computing tasks and quantifiable data, AI now embraces self-correcting and self-evolving tools through machine learning and deep learning strategies. This shift allows AI systems to simulate human thought and intelligence more effectively.
  • Multi-Part Collaborative Technologies: AI drift also stems from collaborative technologies, often called “deep stubborn networks.” These technologies combine generative and discriminative components, allowing them to work together and evolve the AI’s capabilities beyond its original programming. This collaborative approach enables AI systems to produce more accessible results and become less constrained by their initial design.

Understanding AI Drift

AI drift, also known as model drift or model decay, refers to the change in distribution over time for model inputs, outputs, and actuals. In simpler terms, the model’s predictions today may differ from what it predicted in the past. There are different types of drift to monitor in production models:

  • Prediction Drift: This type of drift signifies a change in the model’s predictions over time. It can result in discrepancies between the model’s pre-production predictions and its predictions on new data. Detecting prediction drift is crucial in maintaining model quality and performance.
  • Concept Drift: Concept drift, on the other hand, relates to changes in the statistical properties of the target variable or ground truths over time. It indicates a shift in the relationship between current and previous actuals, making it vital to ensure model accuracy and relevance in real-world scenarios.
  • Data Drift: Data drift refers to a distribution change in the model’s input data. Shifts in customer preferences, seasonality, or the introduction of new offerings can cause data drift. Monitoring data drift is essential to ensure the model remains resilient to changing input distributions and maintains its performance.
  • Upstream Drift: Upstream drift, or operational data drift, results from changes in a model’s data pipeline. This type of drift can be challenging to detect, but addressing it is crucial to manage performance issues as the model moves from research to production.

Detecting AI drift: Key factors to consider.

  • Model Performance: Monitoring for drift helps identify when a model’s performance is degrading, allowing timely intervention before it negatively impacts the customer experience or business outcomes.
  • Model Longevity: As AI models transition from research to the real world, predicting how they will perform is difficult. Monitoring for drift ensures that models remain accurate and relevant even as the data and operating environment change.
  • Data Relevance:  Models trained on historical data need to adapt to the changing nature of input data to maintain their relevance in dynamic business environments.

Here’s a front-runner I discovered in my research on this topic:

Evidentlyai, is a game-changing open-source ML observability platform that empowers data scientists and Machine Learning(ML) engineers to assess, test, and monitor machine learning models with unparalleled precision and ease. 

Evidentlyai rises above the conventional notion of a mere monitoring tool or service; it is a comprehensive ecosystem designed to enhance machine learning models’ quality, reliability, and performance throughout their entire lifecycle.

Three Sturdy Pillars

This product stands on three sturdy pillars: Reporting, Testing, and Monitoring. These distinct components offer a diverse range of applications that cater to varying usage scenarios, ensuring that every aspect of model evaluation and testing is covered comprehensively.

  • Reporting: Visualization is paramount in reporting. Love this part. The reporting provides data scientists and ML engineers with a user-friendly interface to delve into the intricacies of their models. By translating complex data into insightful visualizations, Reports empower users to deeply understand their model’s behavior, uncover patterns, and make informed decisions. It’s more than just data analysis; it’s a journey of discovery.
  • Testing: Testing is the cornerstone of model reliability. Evidentlyai’s testing redefines this process by introducing automated pipeline testing. This revolutionary approach allows rigorous model quality assessment, ensuring every tweak and modification is evaluated against a comprehensive set of predefined benchmarks. Evidentlyai streamlines the testing process through automated testing, accelerating model iteration and evolution.
  • Monitoring:  Real-time monitoring is the key to preemptive issue detection and performance optimization. Evidentlyai’s monitoring component is poised to revolutionize model monitoring by providing continuous insights into model behavior. By offering real-time feedback on model performance, Monitoring will empower users to identify anomalies, trends, and deviations, allowing for swift corrective action and continuous improvement.

Evidentlyai

At the heart of Evidentlyai lies its commitment to open-source collaboration. This level of commitment always makes me smile. The platform’s Python library opens up a world of possibilities for data scientists and ML engineers, enabling them to integrate Evidentlyai seamlessly into their workflows. This spirit of openness fosters innovation, accelerates knowledge sharing, and empowers the AI community to collectively elevate model monitoring and evaluation standards.

Evidentlyai is a beacon of innovation, redefining how we approach model monitoring and evaluation. Its comprehensive suite of components, ranging from insightful Reports to pioneering automated Tests and real-time Monitors, showcases a commitment to excellence that is second to none. As industries continue to harness the power of AI, Evidentlyai emerges as a vital companion on the journey to model reliability, performance, and success. Experience the future of model observability today, and embrace a new era of AI confidence with Evidentlyai.

AI drift is an essential aspect of machine learning observability that cannot be overlooked. By understanding and monitoring different types of drift, data scientists and AI practitioners can take proactive measures to maintain the performance and relevance of their AI models over time. As AI advances, staying vigilant about drift will be critical in ensuring the success and longevity of AI applications in various industries. Evidentlyai will play a large part in addressing this issue in the future.

GitHub Test ML Models with Evidentlyai.

image credit – suspensions