Detector is a complete machine learning workflow commands to perform an ensemble anomaly detection methoology. It's has a command line tool that interacts with the associated Snowflake database and multiple scripts to estimate anomaly probabilities, ranging from data collection to updating outputs to snowflake, passing by the creation of client / ticket features and modeling.
The key features are:
- Interact with Snowflake databases and collect data according to the used defined date window and table name.
- Build features by aggregating raw data by client or ticket.
- Train and optimize different anomaaly detection models and using bayesian optimization routine.
- Estimate anomaly probabilities using developed models and push result back to Snowflake.
Detector aims to automatically assign risk probabilites to client or ticket according certain period and push the results to Snwoflake.
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Detector We encourage you to read our Get Started guide to better understand what DVC is and how it can fit your scenarios.
The easiest (but not perfect!) analogy to describe it: DVC is Git (or Git-LFS to be precise) & Makefiles made right and tailored specifically for ML and Data Science scenarios.
Git/Git-LFSpart - DVC helps store and share data artifacts and models, connecting them with a Git repository.Makefiles part - DVC describes how one data or model artifact was built from other data and code.
DVC usually runs along with Git. Git is used as usual to store and version code (including DVC meta-files). DVC helps to store data and model files seamlessly out of Git, while preserving almost the same user experience as if they were stored in Git itself. To store and share the data cache, DVC supports multiple remotes - any cloud (S3, Azure, Google Cloud, etc) or any on-premise network storage (via SSH, for example).
The DVC pipelines (computational graph) feature connects code and data together. It is possible to explicitly specify all steps required to produce a model: input dependencies including data, commands to run, and output information to be saved. See the quick start section below or the Get Started tutorial to learn more.
``` Usage: stronghold.py [OPTIONS]
Securely configure your Mac. Developed by Aaron Lichtman -> (Github: alichtman)
- Options:
-lockdown Set secure configuration without user interaction. -v Display version and author information and exit. -help, -h Show this message and exit.
Common workflow commands include:
There are four options to install DVC: pip, Homebrew, Conda (Anaconda) or an OS-specific package.
Full instructions are available here.
pip install dvcDepending on the remote storage type you plan to use to keep and share your data, you might need to specify
one of the optional dependencies: s3, gs, azure, oss, ssh. Or all to include them all.
The command should look like this: pip install dvc[s3] (in this case AWS S3 dependencies such as boto3
will be installed automatically).
To install the development version, run:
pip install git+git://github.com/iterative/dvcSelf-contained packages for Linux, Windows, and Mac are available. The latest version of the packages can be found on the GitHub releases page.
sudo wget https://dvc.org/deb/dvc.list -O /etc/apt/sources.list.d/dvc.list
sudo apt-get update
sudo apt-get install dvcsudo wget https://dvc.org/rpm/dvc.repo -O /etc/yum.repos.d/dvc.repo
sudo yum update
sudo yum install dvcDetector's suite of algorithms aims at identifying possible events that deviate significantly from global observed events. In this sense, detector provides a level of risk of possible anomalies, but does not detect anomalies or fraud events with certainty.
As specified in its general rules of intervention, KPMG cannot be held responsible for decisions made on the basis of proposals or predictions made by detector.