Data Analytics

Image by Author   #  Introduction   Stress testing is crucial for understanding how your application behaves under heavy load. For machine learning-powered APIs, it is especially important because model inference can be CPU-intensive. By simulating a large number of users, we can identify performance bottlenecks, determine the capacity of our system, and ensure reliability.…

Image by Editor | ChatGPT   #  Introduction   Neo4j is a powerful database that works with connected data. Unlike traditional databases that use tables, Neo4j uses nodes and relationships. This setup makes it easy to explore complex links in data. Neo4j is popular for projects like social networks, recommendation systems, and network analysis. This…

Image by Author | Canva   #  Introduction   There is no doubt that large language models can do amazing things. But apart from their internal knowledge base, they heavily depend on the information (the context) you feed them. Context engineering is all about carefully designing that information so the model can succeed. This idea…

Image by Author | Canva   #  Introduction   Traditional debugging with print() or logging works, but it’s slow and clunky with LLMs. Phoenix provides a timeline view of every step, prompt, and response inspection, error detection with retries, visibility into latency and costs, and a complete visual understanding of your app. Phoenix by Arize…

Image by Author | Canva   #  Introduction   I understand that with the pace at which data science is growing, it’s getting harder for data scientists to keep up with all the new technologies, demands, and trends. If you think that knowing Python and machine learning will get the job done for you in…

Image by Author | ideogram.ai   #  Introduction   With the surge of large language models (LLMs) in recent years, many LLM-powered applications are emerging. LLM implementation has introduced features that were previously non-existent. As time goes on, many LLM models and products have become available, each with its pros and cons. Unfortunately, there is…

Image by Author | Canva   Python is widely known for its popularity among engineers and data scientists, but it’s also a favorite choice for web developers. In fact, many developers prefer Python over JavaScript for building web applications because of its simple syntax, readability, and the vast ecosystem of powerful frameworks and tools…

Image by Author | ChatGPT   Introduction   Python's built-in datetime module can easily be considered the go-to library for handling date and time formatting and manipulation in the ecosystem. Most Python coders are familiar with creating datetime objects, formatting them into strings, and performing basic arithmetic. However, this powerful module, sometimes alongside related libraries…

Sponsored Content       The landscape of big data analytics is constantly evolving, with organizations seeking more flexible, scalable, and cost-effective ways to manage and analyze vast amounts of data. This pursuit has led to the rise of the data lakehouse paradigm, which combines the low-cost storage and flexibility of data lakes with…

Image by Author | ChatGPT   Introduction   For years, Google Colab has stood as a cornerstone for data scientists, machine learning engineers, students, and researchers. It has democratized access to what amount to essential computing resources in today's world such as graphics processing units (GPUs) and tensor processing units (TPUs), and has offered a…