Scikit-learn
Pandas
NumPy
OpenCV
Dataiku
Exploratory
WEKA
htm.java
DataStax
Ataccama
Dell EMC DataIQ
1010Data
Redis
Druva
ArangoDB
Hitachi Vantara
Scikit-learn DataStaxBased on our record, Scikit-learn seems to be a lot more popular than DataStax. While we know about 40 links to Scikit-learn, we've tracked only 2 mentions of DataStax. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Certutil.exe or notepad.exe opening an external connection lands in rare because, fleet-wide, those processes almost never egress. Tune the <= 3 threshold to your environment size. For a more principled version, score each (process, destination) pair by frequency and treat the long tail as the hunt queue, which is the same idea behind scikit-learn's rarity-based anomaly methods without the model overhead. - Source: dev.to / about 2 months ago
Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / 2 months ago
Pre-configured environment. A good course ships a VM or container with Jupyter, pandas, scikit-learn, PyTorch or transformers, and realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. No setup tax. - Source: dev.to / 2 months ago
Isolation-based models: Build random decision trees that split features. Points that are isolated quickly (short average path length across trees) are anomalies. IsolationForest in scikit-learn implements this. Handles high-dimensional feature spaces without assuming a distribution. - Source: dev.to / 3 months ago
In practice, youโll want to use libraries (like scikit-learn or TensorFlow.js for more advanced modeling), but the principle remains: find what similar users enjoy, and use that as a basis for recommendations. - Source: dev.to / 5 months ago
This is the first part of a multipart post about creating a multi-agent research assistant using Datastax AstraDB and Langflow. - Source: dev.to / over 1 year ago
When it comes to building one's own RAG applications, DataStax's Astra seems to be the preferred database solution for deploying RAG applications, thanks to its robust API and integrations that facilitate the development of high-performance RAG applications. Astra DB's architecture supports the high demands of RAG by providing low latency and high relevancy in data retrieval, which are pretty important for the... - Source: dev.to / about 2 years ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Ataccama - We deliver Self-Driving Data Management & Governance with Ataccama ONE. Itโs a fully integrated yet modular platform for any data, user, domain, or deployment.
NumPy - NumPy is the fundamental package for scientific computing with Python
Dell EMC DataIQ - Dell EMC DataIQ is one of the unique storage monitoring and dataset management software for unstructured data that allows a unified file system of PowerScale, ECS, and delivers unique insights into data usage and storage system health.
OpenCV - OpenCV is the world's biggest computer vision library
1010Data - 1010data provides cloud-based big data analytics for retail, manufacturing, telecom and financial services enterprises.