Singapore - Databricks AI Work Tours 2025

What I did at Databricks AI Work Tours 2025 ?

Overall, the event was a fantastic opportunity to learn about the latest trends and technologies in AI and data analytics. The sessions were informative, and the networking opportunities were invaluable. I look forward to applying the knowledge gained to my work and exploring new possibilities in the field of AI with using Databricks.

Key Highlights from the Event

Keynote Sessions: The event kicked off with keynote sessions from industry leaders who shared insights on the future of AI and data analytics. They discussed emerging trends, challenges, and opportunities in the field.
Technical Workshops: There were several hands-on workshops that provided practical knowledge on using Databricks for AI and data analytics. Topics covered included machine learning, data engineering, and data visualization.
Product Demos: Databricks showcased their latest products and features, including advancements in their AI and machine learning capabilities. The demos highlighted how these tools can be leveraged to build scalable and efficient AI solutions.
Networking Opportunities: The event provided ample opportunities to connect with other professionals in the field. I had the chance to meet data scientists, engineers, and business leaders from various industries, which was invaluable for knowledge sharing and collaboration.
Customer Success Stories: Several organizations shared their success stories of using Databricks to drive innovation and achieve business goals. These case studies provided real-world examples of how AI and data analytics can transform businesses.

Is Databricks is good for AI ?

Databricks is indeed a powerful platform for AI and data analytics. It provides a unified environment that combines data engineering, data science, and machine learning, making it easier for teams to collaborate and build AI solutions. Here are some reasons why Databricks is considered good for AI:

Unified Platform: Databricks offers a single platform that integrates data processing, machine learning, and analytics, allowing teams to work together seamlessly.
Scalability: It can handle large-scale data processing and machine learning workloads
Collaboration: Databricks provides collaborative notebooks and tools that enable data scientists, engineers, and analysts to work together effectively.
Integration with Popular Tools: It supports integration with popular AI and machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn, making it easier to build and deploy AI models.
Automated Machine Learning: Databricks offers automated machine learning capabilities that simplify the process of building and deploying models.
Real-time Analytics: It supports real-time data processing and analytics, which is crucial for many AI applications.
Robust Security: Databricks provides enterprise-grade security features to protect data and ensure compliance with regulations.

I can say the important thing is that Databricks is good for business care about scalability, collaboration, complexity, and security. It provides a comprehensive set of tools and features that can help organizations leverage AI to drive innovation and gain a competitive edge.

What I really using tools for my personal life ???

In my personal life, I want freedom in Opensource for exploration than depend on platform, I use almost open source tools for simple purposes, I use:

DuckDB: A fast, embedded database that is great for handling large datasets locally. I use it for data analysis and querying without the need for a full-fledged database server. Actually, you can using Motherduck for cloud version.
Pandas: A powerful data manipulation library in Python. I use it for data cleaning, transformation, and analysis. It's great for working with structured data.
Jupyter Notebooks: An interactive computing environment that allows me to create and share documents that contain live code, equations, visualizations, and narrative text. I use it for data exploration and prototyping.
VS Code: A versatile code editor that supports a wide range of programming languages and extensions. I use it for writing and debugging code, as well as managing projects.
GitHub: A platform for version control and collaboration. I use it to host my code repositories, track changes, and collaborate with others on projects.
Streamlit: An open-source app framework for creating and sharing data applications. I use it to quickly build and deploy interactive web apps for data visualization and analysis.
LangChain: A framework for building applications with large language models (LLMs). I use it to create AI-powered applications that leverage natural language processing capabilities.
LlamaIndex: A data framework for building applications with LLMs. I use it to manage and query large datasets using language models.
OpenAI: A platform that provides access to powerful AI models. I use it for natural language processing tasks, such as text generation and summarization.
Hugging Face: A platform for sharing and using machine learning models. I use it to access pre-trained models and fine-tune them for specific tasks.

Do I used Databricks ?

Yes, we using for business purposes, but I'm not using for personal life because I want freedom in Opensource for exploration than depend on platform.

I'm not a marketing of Databricks. It's sound like depend on what you want to do with it, if I have some issue need resolve with big data, I will using Databricks, but if I want to explore something new, I will using Opensource tools. Why ? Because I can explore new process, new technology, new method, new way to do it. I can learn more from it than learning from platform. I'm not say you need to explore everything, but when you try, you see the issue, and you just find the way to resolve the problem or make it faster, better, easier, cheaper.

Out of the box still have many solution than just using one platform. When we able to explore all the solution, we can choose the best one for our problem.