Articles for category: AI Tools

🚀 Boost Your Workflow with CI/CD: A Step-by-Step Guide to Supercharge Development!

In today’s fast-paced development world, waiting for manual deployments is a thing of the past. If you’re not using Continuous Integration and Continuous Deployment (CI/CD) yet, you’re missing out on a game-changing workflow that can save time, reduce errors, and make your releases smoother than ever! So, how do you set up CI/CD from scratch? Let’s break it down step by step! 🔥 Why CI/CD Matters? CI/CD isn’t just a fancy term—it’s a must-have for developers who want to: âś… Automate testing and deployment âś… Catch bugs early before they hit production âś… Deliver updates faster with confidence âś… Minimize

Hugging Face x LangChain : A new partner package

We are thrilled to announce the launch of langchain_huggingface, a partner package in LangChain jointly maintained by Hugging Face and LangChain. This new Python package is designed to bring the power of the latest development of Hugging Face into LangChain and keep it up to date. All Hugging Face-related classes in LangChain were coded by the community, and while we thrived on this, over time, some of them became deprecated because of the lack of an insider’s perspective. By becoming a partner package, we aim to reduce the time it takes to bring new features available in the Hugging Face

Big Data & Analytics Summit

I was recently invited to share at the Big Data & Analytics Innovation Summit on Data Science at Lazada. There were plenty of sessions sharing on potential use cases and case studies based on other companies, but none on the challenges of building and scaling a data science function. Thus, I decided to share about some of the challenges faced during Lazada-Data’s three-year journey, as we grew from 4 – 5 pioneers to a 40-ish man team. In a nutshell, the three key challenges faced were: How much business input/overriding to allow? How fast is “too fast”? How to set

Day-07: Unpacking Java: How Classes, Objects, and Methods Work Together

Java: From Source Code to ExecutionCompilation (Source Code to Byte Code) Source Code → Written in Java, with human-readable syntax. Compiler → Translates the source code into Byte Code. Compile Time → Occurs when the Java compiler processes the source code. Enter fullscreen mode Exit fullscreen mode Execution (Byte Code to Binary Code)Byte Code → The intermediate code generated by the compiler.JRE (Java Runtime Environment) → Interprets the byte code and converts it into machine-readable binary code.Run Time → Happens when the program is executed by the JRE. JDK (Java Development Kit)The JDK contains the Compiler and JRE for both

Introducing the Open Arabic LLM Leaderboard

The Open Arabic LLM Leaderboard (OALL) is designed to address the growing need for specialized benchmarks in the Arabic language processing domain. As the field of Natural Language Processing (NLP) progresses, the focus often remains heavily skewed towards English, leaving a significant gap in resources for other languages. The OALL aims to balance this by providing a platform specifically for evaluating and comparing the performance of Arabic Large Language Models (LLMs), thus promoting research and development in Arabic NLP. This initiative is particularly significant given that it directly serves over 380 million Arabic speakers worldwide. By enhancing the ability to

Deephaven at a glance | Deephaven

It’s natural to frame new information in the context of what you already know and understand. The same is true when determining where new vendors and technology/solution providers fit into established marketplaces. Deephaven enters an arena defined by Kafka, Spark, Influx, Redshift, BigQuery, Snowflake, Postgres, and dozens of other players. Below, we break down Deephaven’s differentiators and value propositions, and place them in the context of the use cases they serve, so that you can leverage Deephaven Core for your next big data project. What is Deephaven?​ In short, a data query engine. So what does that mean? Well, beyond

OMSCS CS7642 (Reinforcement Learning) Review and Tips

You might also be interested in this OMSCS FAQ I wrote after graduation. Or view all OMSCS related writing here: omscs. I know, I know, I’m guilty of not writing over the last four months. Things have been super hectic with project Voyager at Lazada, switching over to the new platform in end March and then preparing for our birthday campaign in end Apr. Any free time I had outside of that was poured into the Georgia Tech Reinforcement Learning (CS7642), which is the subject of this post. The course was very enriching and fun. Throughout the course, we learnt