A small experiment in remote SQL execution
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A preprocessing pipeline let my car price model peek at the test set before the exam, and the twelve points of R squared it cheated its way to
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Can a language model do live adversarial level design? Yes, emphasis on the adversarial part
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A reproducible Lagos case study with population data, transport access, and airspace constraints
The post How to Place Vertiport Locations in Any City Using Geospatial Machine Learning appeared first on Towards Data Science.
Video is a core data path across NVIDIA Jetson applications, from robotics and intelligent video analytics to industrial automation, healthcare, media...Video is a core data path across NVIDIA Jetson applications, from robotics and intelligent video analytics to industrial automation, healthcare, media processing, and remote operations. A system may capture several cameras, decode network streams, run AI inference or conventional vision processing, draw results, and encode video for storage or delivery. The individual calls areβ¦
Not all Python data libraries are created equal!
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Building a production-ready web interface for a stateful LangGraph agent
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From Static Plots to Interactive Data Exploration
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Faster dataframe engines are nice, but they don't reduce the amount of syntax an analyst has to hold in their head.
The post The Problem with pandas Isnβt Performance. Itβs Cognitive Overhead. appeared first on Towards Data Science.
A practical guide to Bronze, Silver and Gold, with a working Python and DuckDB example
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Create a local CLI Agent from scratch completely for free
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NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users...NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users access to CUDA-X performance for common math operations without disrupting existing workflows. Depending on the API, operations can run on a CPU, CUDA-enabled GPU, or distributed multi-GPU, multi-node systems.
Building an ALNS heuristic in Python for vehicle routing, time windows, capacity constraints, and mandatory driver breaks.
The post Los Movimientos, Part II: Solving Large Pickup-and-Delivery Problems with Adaptive Large Neighborhood Search appeared first on Towards Data Science.
You "vibe coded" the import. Understand Adam's optimization dynamics, why it fails spectacularly, and how to fix it.
The post Donβt Just βThrow Adam at Itβ: Misunderstanding Adam Will Cost You appeared first on Towards Data Science.
Using mathematical optimization to solve a pickup-and-delivery problem with time windows.
The post βLos Movimientosβ: The Routing Problem That Nearly Broke My Spirit appeared first on Towards Data Science.
In a world where human time is expensive, learn how to use it only when really necessary
The post Reducing Human Annotation with ML Active Learning appeared first on Towards Data Science.
Building a great AI agent isnβt just about choosing the right models. The harness is the architecture surrounding the model. How it renders context, executes...Building a great AI agent isnβt just about choosing the right models. The harness is the architecture surrounding the model. How it renders context, executes actions, manages state, and decides when a task is done shapes outcomes just as much as the model itself. Harness design alone can account for double-digit swings in benchmark results and significant differences in token costβ¦
Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a...Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a few challenges. It requires infrastructure, technical expertise, and software specific to the workflow, as well as resources such as GPUs and the ability to use them effectively. It also depends on specialized domain knowledge: Whatβ¦
Apache Spark 4.2 launched last week, and it signals an expansion of Sparkβs decade-plus role at the center of enterprise data processing
With new features for AI workloads, including governed metrics, vector retrieval primitives, real-time processing, improved Python support and native geospatial analytics, Spark 4.2 builds on a recent history of new AI and streaming features, reflecting how many engineering teams use the platform today. The release builds on Sparkβs traditional role as a data processing engine by adding more of the capabilities needed to support production AI applications.
The launch introduced several features that enable developers to do more without leaving the platform, and for teams already using Spark, that could mean fewer systems to manage.
One teamβs definition of a business metric isnβt always the same as anotherβs. Over time, those differences can lead to conflicting reports and uncertainty about which number to trust.
That becomes even more problematic when AI applications start consuming the same enterprise data that analysts and business intelligence tools use. If different teams define the same metric differently, AI systems can produce inconsistent results for the same question.
Spark 4.2 introduces governed metric views to address that issue. Organizations can define a business metric once and reuse that definition across applications. A metric view makes dimensions and measures first-class objects that Spark understands, so the engine can preserve the intended aggregation semantics regardless of who or what is querying it.
Organizations can define a business metric once and reuse that definition across applications.
One of the more significant additions is native vector search, which reduces the need to move data between Spark and a separate vector database.
Spark 4.2 introduces vector distance and similarity functions, vector normalization, vector aggregation, and NEAREST BY, a new SQL operator for top-K similarity searches. By bringing vector search into Spark, developers can keep more of their retrieval pipeline on the same platform.
By bringing vector search into Spark, developers can keep more of their retrieval pipeline in the same platform.
Spark 4.2 makes it easier to move data between Spark and Arrow-native tools. With support for the Arrow C Data Interface and the PyCapsule protocol, Spark DataFrames can be passed directly to tools like Polars and DuckDB without copying or serializing the underlying data, as long as both sides support the standards.
Python also gets a few other updates. PySpark has been expanded; Arrow-optimized UDF execution is now the default, and Python Data Sources now include built-in time and memory profiling to help developers troubleshoot custom connectors.
Spark Connect, which separates the client from the Spark server via a gRPC- and Arrow-based protocol, receives several updates in 4.2. The key idea is that a client builds a logical plan, the server handles analysis, optimization, and execution, and the results come back as Arrow batches. The client doesnβt need a full Spark runtime or a colocated JVM.
This update includes several changes to Spark Connect, the projectβs client-server interface. AI applications can send processing requests to a remote Spark cluster while the work continues to run inside Spark. The release improves RDD API compatibility, error handling, and status reporting along that path.
Streaming gets several updates in Spark 4.2, including Auto CDC and Real-Time Mode. Many AI applications depend on continuously updated data rather than scheduled batch jobs. Auto CDC brings first-class change data capture to Spark Declarative Pipelines, handling the merge logic for keeping target tables current as source data changes β something that previously required hand-written, error-prone code. The new CHANGES SQL clause allows teams to retrieve data changes through a single SQL interface.
Spark 4.2 also adds built-in GEOMETRY and GEOGRAPHY types along with ST_* functions for location-aware analytics, without requiring external spatial extensions. For teams doing anything with location data β logistics, real estate, IoT β this removes another reason to move data out of Spark.
Spark 4.2 brings more of the AI and data stack into the platform itself. Features that once depended on separate tools can now be handled directly in Spark.
For teams that currently use Spark for ETL and then hand data off to other systems for retrieval, governance, or real-time processing, this release starts to blur that line. As more AI applications run directly on operational data, Spark is becoming part of the serving layer rather than simply preparing data for it.
Spark is becoming part of the serving layer rather than simply preparing data for it.
The post Spark 4.2 has a feature that could retire your vector database appeared first on The New Stack.
Mixture-of-experts (MoE) models have quickly become a foundational component of modern, large-scale AI systems. They are widely adopted because they enable...Mixture-of-experts (MoE) models have quickly become a foundational component of modern, large-scale AI systems. They are widely adopted because they enable substantially larger model capacity while activating only a subset of parameters for each token, offering an unparalleled approach for scaling performance within a practical compute budget. As model scales continue to growβ¦
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