DBeaver’s Post

On LLM Zoomcamp, we begin module 3 about vector databases. We'll cover: 🔸 Vector search with Elasticsearch 🔸 Creating and indexing embeddings 🔸 Retrieval evaluation metrics Start learning: https://lnkd.in/eS4MUKPK

🌟 Alhamdulillah! I completed the Stanford Online database course. It was incredibly helpful and increased my practical skills. Although it took me some time due to inconsistency, finishing it was important to me. Persistence pays off! #AchievementUnlocked #DatabaseSkills #StanfordOnline #NeverGiveUp #LifelongLearning #learning

I have successfully completed the "Ordered Data Structures" course on Coursera, where I gained in-depth knowledge of key data structures that organize and store data in a way that allows for efficient searching, insertion, and deletion. This course covered concepts such as: Balanced Binary Search Trees (BST), including AVL and Red-Black Trees Priority Queues and their implementation using heaps B-Trees and their application in database indexing Algorithms for maintaining ordered collections of data for optimal performance Through hands-on assignments, I developed a strong understanding of how to implement and apply these data structures to real-world problems, improving my problem-solving and software development skills.

Just finished Learning Google Dataflow! Check it out: https://lnkd.in/dSSn4Rvu #dataprocessing #googleclouddataflow

Just finished Learning Google Dataflow! Check it out: https://lnkd.in/eSmm54iE #dataprocessing #googleclouddataflow

This week I am reading a research paper by Amazon about how they scale Redshift. Here's a quick gist from my first skim ⚡ The paper is all about a system they built called RAIS – which utilizes machine learning to optimally provision the resources and automate the scaling up and down of Redshit's underlying infrastructure. By the way, the paper also has some maths in it. I am skipping that, for obvious reasons. But other than that the first skim was pretty awesome. One thing I loved about this paper is the time it spends in making us understand the problem and the constraints to play with. Give it a read if the topic seems interesting or if the database and data system domain amuse you. The paper will definitely make you think deeply about the systems you work with. find this paper on - arpitbhayani.me/papershelf ⚡ I keep writing and sharing my practical experience and learnings daily, so if you resonate, follow along. I keep it no fluff. youtube.com/c/ArpitBhayani #AsliEngineering #DatabaseInternals #SystemDesign

Get ready to unleash the power of MongoDB Atlas Vector Search and RAG with Richmond Alake, Developer Advocate at MongoDB! 🚀 Our new course on DeepLearning.AI dives into hands-on exercises to master the art of vector search with MongoDB. Learn to create multi-stage aggregation pipelines, refine search results, and implement prompt compression for unmatched efficiency and relevancy. Sign up now and experience the future of data optimization! 👇 https://lnkd.in/guYvYEih

While updating my skills, I discovered several useful resources. https://lnkd.in/gM-8mFg3

➡ Creating a prototype with GenAI is quite easy but bringing it into production, making it efficient and performant - that is where all the 'magic' starts. So I'm thrilled to see new course "𝐏𝐫𝐨𝐦𝐩𝐭 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐚𝐧𝐝 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 " from Andrew Ng #genai #ai

Something that I have been pondering for more optimized use of RAG model for project or use case specific. This course from DeepLearning.AI will definitely be beneficial to all corporates who are trying to bring LLM into workflow sustainably. #UseAISustainably