# Introduction In a recent article on Machine Learning Mastery, we built a tool-calling agent that reached outward, that is pulling weather, news, currency rates, and time from public APIs. That article covered the synthesis half of the pattern nicely, but it left the more interesting half on the table: an agent that reasons about …
The Asia-Pacific region is a global engine for economic growth, but it’s also highly vulnerable to climate change. While green technologies are gaining momentum, a recent report shows they aren’t scaling fast enough to keep up with the region’s rising environmental risks. To help innovators tackle these environmental challenges, we’re launching an inaugural Google DeepMind …
themes from a call corpus to the customer table. Customers without transcripts get NULL. NULL gets filled with zero, or with “no issue mentioned,” or quietly omitted as a reference category. In one line of preprocessing, the pipeline converts did not call support into did not experience billing frustration. The regression that follows looks clean. …
# Introduction Production data is typically subject to notable privacy and compliance constraints. For this reason, anonymizing such data becomes critical in virtually every real-world data science project involving the launch of a data-driven product, service, or solution. Mimesis is an open-source Python library that stands out for its ability to generate realistic “fake” data …
Anonymizing Production Data for Data Science with Mimesis Read More »
Street View: ground your worlds in real places When creating imaginative worlds in Project Genie, you can now also base them on real places. Just tap the Maps pin to choose a place in the U.S. and optionally select a style for your world, like “Desert Sands” or “Stone Age.” Then, describe your character — …
Simulate real-world places with Project Genie and Street View Read More »
teach you how to make a model accurate. They rarely teach you the decisions that come right after. How do you know when to fully automate something versus keeping a human in the loop? When does prompting stop being enough and fine-tuning become worth the cost? What does it actually mean to pick real-time inference …
Six Choices Every AI Engineer Has to Make (and Nobody Teaches) Read More »
# Introduction Why do you use Python? For a lot of people it comes down to “just because,” but it really shouldn’t. Python is a powerful, general-purpose programming language with a simple syntax highlighted by the Pythonic approaches to managing logic and data, that just happens to have found itself the go-to languages of data …
. A part of me started this journey because data engineering is one of the hottest and highest-paying careers right now. I’m not going to pretend that wasn’t a factor. But there’s more to it than that. I’ve been learning data analytics for a while now. SQL, Power BI, Python (Pandas, NumPy, a little Polars), …
From Data Analyst to Data Engineer: My 12-Month Self-Study Roadmap Read More »
# Introduction TurboQuant is a novel algorithmic suite and library recently launched by Google. Its goal is to apply advanced quantization and compression to large language models (LLMs) and vector search engines — indispensable elements of retrieval-augmented generation (RAG) systems — to improve their efficiency drastically. TurboQuant has been shown to successfully reduce cache memory …
TurboQuant: Is the Compression and Performance Worth the Hype? Read More »
I’ve seen a lot when I’m working with enterprise AI teams: they nearly always blame the model when something goes wrong. This is understandable, but it’s also frequently incorrect, and it ends up being quite costly. The usual scenario is as follows. The outputs are inconsistent; when someone raises it, the first reaction is to …
The Next AI Bottleneck Isn’t the Model: It’s the Inference System Read More »
