I have started taking a course on big data engineering and attended a few classes on Apache Spark (Databricks platform) and Apache Kafka (streaming real-time data). It was a lot of fun. Here is a related article. Are you also learning Big Data? Big Data Engineering knowledge can help your work as a Generative AI engineer in several impactful ways. Here are some interesting use cases (let me know if you have some more additional use cases): 1. RAG (Retrieval-Augmented Generation) at Scale - Problem: LLMs struggle with real-time or domain-specific knowledge. - Solution: Use Big Data tools (e.g., Apache Spark, Kafka) to process, store, and retrieve vast amounts of domain-specific data in real time. - Example: A legal AI chatbot fetching case laws dynamically from a document store like Elasticsearch or Pinecone. 2. AI-Powered Fraud Detection - Problem: Detecting anomalies in massive transaction datasets. - Solution: Combine Spark for batch processing and Kafka for real-time streaming with an LLM to analyze patterns in structured and unstructured data. - Example: A Generative AI assistant generating real-time fraud reports based on continuous transaction monitoring. 3. Personalized AI Assistants for E-commerce - Problem: Providing hyper-personalized recommendations based on user behavior. - Solution: Leverage data lakes (e.g., AWS S3, Delta Lake) and real-time processing (Flink, Spark) to feed AI models with the latest customer interactions. - Example: A chatbot generating tailored product descriptions and recommendations based on browsing history. 4. AI for Call Centers & Customer Support - Problem: Handling and analyzing millions of customer interactions efficiently. - Solution: Store call transcripts in a data warehouse, use NLP to categorize issues and apply LLMs for automated response generation. - Example: AI agent that generates real-time responses based on past call data and customer history.

It's like yesterday we were freaking out over the leap forward we saw from DeepSeek R1 - a model developed with relatively few resources and an excellent cost/quality ratio. Its cost-effective performance reportedly caused a panic within companies like Meta. Google's release of Gemini 2.0 Flash to the public could be an even greater disruption. Its design as a "workhorse model" for high-volume tasks, its multimodal reasoning capabilities, its substantial 1M token context window, and, most importantly, its pricing - make it an attractive choice for any LLM application that requires quality and scalability. And the cost-optimized Gemini 2.0 Flash-Lite is somehow even cheaper. I hear industry leaders like Sam Altman give lip service to democratizing AI, but I think this is what that actually looks like. It's great to see real competition heating up in the foundational LLM models. With any luck, LLM models is not going to be a winner-take-all business.

I wanted to share my findings here. Hope it is helpful for someone. I spent about 5 to 7 days just to explore where the best place is for me to start learning machine learning / AI. Eventually I settled down on taking subscription for Deeplearning.ai course in Coursera and started doing the course Machine Learning Specialization. It has been 3 days since I started and hands down, I am very excited to say that I glad i am made the choice. I used to dread learning mathematic especially those notations as it was hard for me to comprehend but this course completely changed my perception. It is very easy to follow. The instructor Andrew Ng explains it so beautifully. Highly recommend for starters

Hey everyone! As we’re heading towards the final month of 2024, I’ve been reflecting on what a whirlwind the past few months have been. Q3 and Q4 have been incredibly busy — between running Datalumina, managing client projects, seeing amazing growth in Data Freelancer, shipping the GenAI launchpad, moving into a new office, and onboarding the first customers for our new SaaS product, it’s been a ride. 🚀 On top of that, keeping up with the YouTube channel has been both a challenge and a joy. Honestly, it’s one of my favorite parts of what I do — helping you all learn, grow, and tackle data challenges with confidence. Seeing your feedback and progress keeps me motivated to keep creating. Now, I’m starting to map out my content plans for 2025, and I’d love to hear from YOU. What are you currently learning? What topics would you like me to cover next year? 🎥 Would you like to see more LLM-based tutorials? Deeper dives into machine learning workflows? Maybe even some freelancing tips for data professionals? Or something else entirely? Let me know in the comments — what are you most excited to learn in 2025? This is your chance to shape the direction of the content we create together. Thanks for being part of this community! 🙏🏻 — Dave

Goose, an open-source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM. It works with Local as well as commercial LLMs.