Image by Author | Ideogram # Introduction A few hours into your work day as a data engineer, and you’re already drowning in routine tasks. CSV files need validation, database schemas require updates, data quality checks are in progress, and your stakeholders are asking for the same reports they asked for yesterday (and the day …
Shortcuts for the Long Run: Automated Workflows for Aspiring Data Engineers Read More »
According to a technical paper from Google, accompanied by a blog post on their website, the estimated energy consumption of “the median Gemini Apps text prompt” is 0.24 watt-hours (Wh). The water consumption is 0.26 milliliters which is about five drops of water according to the blog post, and the carbon footprint is 0.03 gCO2e. …
Is Google’s Reveal of Gemini’s Impact Progress or Greenwashing? Read More »
Image by Author When I first started learning about how data science and machine learning could be used outside of finance and marketing, healthcare immediately stood out to me. Not just because it’s a massive industry, but because it literally deals with life and death. That’s when I stumbled into something that kept popping …
Predictive Analytics in Healthcare: Improving Patient Outcomes Read More »
been retained by an insurance company to help refine home insurance premiums across the southeastern United States. Their question is simple but high stakes: which counties are hit most often by hurricanes? And they don’t just mean landfall, they want to account for storms that keep driving inland, delivering damaging rain and spawning tornadoes. To …
Where Hurricanes Hit Hardest: A County-Level Analysis with Python Read More »
Image by Author | Ideogram # Introduction Docker has simplified how we develop, ship, and run applications by providing consistent environments across different systems. However, this consistency comes with a trade-off: debugging becomes deceptively complex for beginners when your applications — including Python applications — are running inside Docker containers. For those new to Docker, …
Debugging Python in Docker: A Tutorial for Beginners Read More »
, you have probably wondered several times how to improve your workflows, how to speed up tasks, and how to output better results. The dawn of LLMs has helped numerous data scientists and ML engineers to not only improve their models but also to help them iterate faster, learn and focus on the tasks that …
Advanced Prompt Engineering for Data Science Projects Read More »
Image by Author | Ideogram GPUs are great for tasks where you need to do the same operation across different pieces of data. This is known as the Single Instruction, Multiple Data (SIMD) approach. Unlike CPUs, which only have a few powerful cores, GPUs have thousands of smaller ones that can run these repetitive …
Writing Your First GPU Kernel in Python with Numba and CUDA Read More »
New experimental AI tool helps people explore the context and origin of images seen online.
that actually work in practice is not an easy task. You need to consider how to orchestrate the multi-step workflow, keep track of the agents’ states, implement necessary guardrails, and monitor decision processes as they happen. Fortunately, LangGraph addresses exactly those pain points for you. Recently, Google just demonstrated this perfectly by open-sourcing a full-stack …
LangGraph 101: Let’s Build A Deep Research Agent Read More »
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. In this …
