“Don’t bring a knife to a gunfight—choose the right AI database! Vector databases are ideal for AI/ML applications, but not all AI databases are vector-based. Examples include Neo4j (graph database), MongoDB (document database), Apache Cassandra (distributed database), and Amazon SageMaker (cloud-based database). Key features that make AI databases suitable for AI applications include integration with AI/ML frameworks, support for unstructured data, and features like explainable AI and transparency. Vector databases are designed for storing and querying vector data, perfect for tasks like image and video search, recommendation systems, and NLP. Some AI databases, such as Google Cloud Database and SingleStoreDB, also support vector data, bridging the gap between foundation models and enterprise GenAI apps for seamless integration.”

Step by step guide to becoming an Data Analyst in 2024

VS Code setup video from two years ago remains one of my most popular guides. It was time for a 2024 update with new tricks and optimizations to enhance your workflow. This setup is perfect for all data and AI related projects using Python. Learn how to create effective workflows, manage virtual environments, and double your productivity with the interactive mode. I also share my favorite new linter and code formatter! Post by Dave Ebbelaar

◼️ 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Link: https://lnkd.in/gygaeAcY ◼️ 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞: 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 Link: https://lnkd.in/gUNVYgGB ◼️ 𝐇𝐢𝐠𝐡-𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 Link: https://lnkd.in/gv9RV9Zc ◼️ 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐑 Link: https://lnkd.in/gUY3jd8v ◼️ 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬 Link: https://lnkd.in/g8gQ6N-H ◼️ 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Link: https://lnkd.in/gAdyf6xR From Introductory to Intermediate, great for beginners.