Machine Learning’s Post

In case you haven’t read it yet, I recommend the post that LinkedIn wrote about building and deploying a scalable genAI application. One of the key learnings for them was how they managed to achieve 80% of the final product in one month but then spent the next 4 months basically doing a lot of “tweaking” (i.e. prompt engineering). How do you make sure you move away from trial and error and infinite prompt tweaking when building LLM applications? The key is in investing in a proper evaluation framework from the start, from gathering the right ground-truth data, set of test queries and potentially adding LLM evaluators to assess every step of your pipeline. We just released a new version of Haystack which introduces a simple framework to get performance metrics for your LLM pipeline, as well as do error analysis to understand where your pipeline fails. Sources: LinkedIn post: https://lnkd.in/dfb_J6ZU Docs: https://lnkd.in/dhHaQ_nS Evaluating a RAG pipeline: https://lnkd.in/dpK5GPNd

Hey LinkedIn fam! 👋 Are you curious about how your favorite online platforms seem to know you so well, suggesting just the right products, movies, or articles? 🤔 Well, it's all thanks to the incredible power of Recommendation Systems! 🔗 In my latest post, I'm diving into the world of recommendation systems – the technology that's shaping our online experiences and transforming the way we discover new content. From personalized product recommendations that keep us coming back for more, to movie suggestions that match our unique tastes, recommendation systems are the invisible wizards behind the curtain. 📈 As data-driven technologies continue to evolve, so do recommendation systems. Join me in this post as we explore the different types of recommendation algorithms – collaborative filtering, content-based filtering, and the exciting world of hybrid approaches. 🌐 🌟 Whether you're a tech enthusiast, a business professional, or simply someone who loves exploring the mechanics of the digital world, this post is for you. Let's unravel the magic behind the recommendations and gain insights into the algorithms that power them. 🤝 Let's spark a conversation! Share your thoughts on how recommendation systems have impacted your online experiences. Are they super helpful or sometimes a bit off the mark? Have you ever discovered something amazing through a recommendation? Let's chat and learn from each other's stories! Stay tuned for the upcoming posts! Author : Supriya Arun https://lnkd.in/eg5EVcEH #Personalization #DataScienceMagic #DigitalExperiences #machinelearning

Accountability Journal - Week 1 As I shared last week, I'm keeping myself accountable by sharing my progress on LinkedIn. Here are the highlights: - Refreshed my knowledge of the .NET framework and React concepts. - Registered for a 15-day Generative AI email course by Armand Ruiz. I'm interested in Generative AI but don't have much time to dive deep into the topics, so 5 minutes of reading every day sounds perfect. If you're new to GenAI, I highly recommend this course: https://lnkd.in/dew45Q9B. - Started volunteer tutoring in Math with Okul Destek Derneği, aiming to impact children's lives positively. I gave my first lesson and I am so excited. - Solving NeetCode 150 with JS: 3/150. I couldn't dedicate much time this week, but each week brings its own set of challenges. Stay tuned for more updates on my accountability journey! #Accountability #ProgressUpdate

Google Research's "Parrot" is a new framework for text-to-image generation, combining reinforcement learning and multi-reward optimization. It uniquely balances multiple rewards to enhance image quality and incorporates a prompt expansion network to refine text prompts. Parrot also features a mechanism to stay true to the original user prompt, preventing loss of initial intent. Its effectiveness surpasses traditional methods in various quality metrics, as proven by extensive testing and user studies. https://lnkd.in/e3AYH7qg

It's very intriguing to see how quickly upcoming companies have grabbed the opportunities in the changing landscape of product management. With Large Language Models becoming a core part of new products, there is an imminent need for prompt optimization and new evaluation metrics, an area in which PhaseLLM (https://phasellm.com/) works. If you're looking at ProductOps specifically for LLMs, there's Nebuly (https://www.nebuly.com/). Excited about the future of products in the LLM age. #LLM #productmanagement

In "Duets in Latent Spaces," I navigate the domain where machine learning finds its meaning, inspired by James Bridle's contemplation on the spaces beyond our capacity to visualize or understand. This endeavor is conceptualized as a lecture-performance, designed to bridge the realms of the tangible and virtual, facilitating presentations both in person and online. It unfolds through a series of vignettes, each spanning 3 to 5 minutes, that illuminate the generative potential of human-AI interaction, inviting the audience into a collaborative narrative that melds human intuition with algorithmic precision. https://lnkd.in/eSnC9ks2

AI’s Helping Hand: Coders’ New Best Friend or Foe? - Google’s #Gemini Code Assist: Announced at Google Cloud Next 2024, aims to help developers write code more efficiently, amidst competition with services like Microsoft’s GitHub Copilot. - Emerging AI Coding Tools: Startups like Pythagora, Tusk, and Ellipsis are introducing specialized AI tools for app creation, bug fixing, and converting GitHub comments into code, enhancing developer productivity. - Long-Term Implications for Developer Jobs: While AI coding aids are not currently poised to replace human developers, their evolving capabilities suggest a future where fewer developers may be needed for certain tasks. Subscribe to our daily newsletter here for more AI news https://lnkd.in/dkSncMHy #Technology #ArtificialIntelligence #SoftwareEngineering #Innovation #Programming #DataScience #MachineLearning #DeveloperTools #Coding #TechNews #AI

I am excited to share my latest blog post on representation learning in low-rank slate-based recommender systems. In this post, I explore the potential of reinforcement learning (RL) in optimizing recommendations for long-term user engagement. I highlight the challenges posed by large state and action spaces and propose a sample-efficient representation learning algorithm. By treating the problem as an online RL problem with low-rank Markov decision processes (MDPs), we can enhance the efficiency of learning and exploration. To dive deeper into this topic, check out my blog post here: https://bit.ly/3LqLAX6.

🔗 Introducing Vector Hub: Your Ultimate Resource for Leveraging the Power of Vector Retrieval in your Product! 🔗 At Superlinked, we understand the importance of staying ahead in the ever-evolving tech landscape. That's why we're thrilled to unveil Vector Hub, a groundbreaking educational and practical resource tailored for developers, ML engineers and Data Scientists keen on mastering vector retrieval for product launches. 👩🎓 🤖 What is Vector Hub? Vector Hub is more than just a platform; it's a dynamic space where developers can dive deep into the intricacies of vector retrieval and gain hands-on experience. We've curated a comprehensive set of resources, tutorials, and real-world examples to empower you in harnessing the power of vectors for your projects. 💡 Why Vector Retrieval? Vectors have become the backbone of modern machine learning applications, offering unparalleled efficiency in handling complex structured and unstructured data. Whether you're working on recommendation systems, search algorithms, or image recognition, understanding vector retrieval is crucial for building robust and scalable solutions. 🛠️ What Vector Hub Offers: 📚 In-depth Tutorials: Step-by-step guides to help you grasp the fundamentals and advanced concepts of vector retrieval. 💻 Practical Examples: Real-world use cases and code snippets to bridge the gap between theory and application. 🚀 Project Showcase: Explore inspiring projects developed by our community and learn from their experiences. [coming soon] 🤝 Community Support: Connect with like-minded developers, share your insights, and seek advice from experts in the field. 🌐 Join the Vector Hub Community: Whether you're a seasoned developer or just starting your journey in the world of ML, Vector Hub is designed to meet you where you are. Visit the website 👇 and subscribe for updates to access a wealth of knowledge and resources that will empower you to take your product performance to new heights. #VectorHub #DeveloperCommunity #VectorDB #VectorRetrieval #CodingJourney #ML #LLMs #GenAI

If you're a software developer looking to experiment with basic AI-enabled features in your product, it can feel like you need to become an expert in the complexities of underlying AI systems. It doesn't have to be that way. Our goal in building products should always be to focus on learning and the fastest path to feedback. Getting a PhD in Machine Learning is not the fast path. Folks who are iterating quickly are generally using this approach: 1) Explore your product ideas leveraging an existing foundation model from the likes of BedRock, Anthropic, or OpenAI. The technical investment is super light so you can focus on the experience. 2) When you find something people love and it’s not accurate enough, you can invest in RAG (retrieval-augmented generation). You'll be able to quickly improve accuracy, but it also drives up your cost. 3) Once you have a clear product with high accuracy, then you can look at scalability and sustainable cost. The options here get broader, from fine-tuning to open source to custom models. But you'll be investing for a known return. The tech is new but the process remains the same: fail fast, fail early, figure out what matters to your customers.