Section2 - Chapter4:- Machine Learning for Cyber Security смотреть онлайн

Section2 - Chapter4:- Machine Learning for Cyber Security

* Introduction :- Intro to Machine Learning and Cyber Security
* Section1-Chapter1 :- ML Development environment using Docker, Conda, Jupyter Notebook , Kubernetes, Intellij etc.
* Section1-Chapter2 :- Feature Extraction, Dimensionality Reduction, Data Labelling using Unsupervised Learning .
* Section1-Chapter3 :- Supervised Learning (Malware Classification), Classification model comparison.
* Section1-Chapter4 :- AutoML Frameworks (H2O autoML)
* Section2-Chapter1 :- Machine Learning with Apache Spark on Kubernetes .
* Section2-Chapter2 :- End to End ML Pipeline using Apache Spark using Jupyter Notebook(s)
* Section2-Chapter3 :- End to End ML Pipeline using Apache Spark using CI/CD approach
* Section2-Chapter4 :- Integration of XGBoost and H2O autoML with Spark.
* Section2-Chapter5 :- GPU accelerated training using RAPIDS (a cuda framework based software)
* Section2-Chapter6 :- Deep Learning using Tensorflow/Keras on GPU
* Section2-Chapter7 :- TBD.

XGBoost integration with Apache Spark
H2O Sprinkling water module with Apache Spark.

Contact Me
LinkedIn :- https://www.linkedin.com/in/shaileshpilare/
Gmail :- [email protected]

Links discussed in the tutorial
GitHub :- https://github.com/ez-ml/ML-For-Cyber-Security
Docker Desktop :- https://docs.docker.com/desktop/
Kubernetes Kubectl :- https://kubernetes.io/docs/tasks/tools/install-kubectl/
IntelliJ IDEA :- https://www.jetbrains.com/idea/download
Docker Hub :- https://hub.docker.com/
Docker best practises :- https://docs.docker.com/develop/develop-images/dockerfile_best-practices #dockerfile-instructions

1) #Build the docker image from "Dockerfile" and tag it as "spark-xgboost:latest".
Docker build -t hishailesh77/spark-xgboost:latest -f Spark-XGBoost.Dockerfile . && docker push hishailesh77/spark-xgboost:latest
Note :- replace "hishailesh77" with your docker hub account name .

2) Delete the existing kubernetes services and object
./jupyter-deployment-delete.sh

3) Create new kubernetes deployment.
./jupyter-deployment-create.sh

4) Build the docker file to include H2O auto ML.
Docker build -t hishailesh77/spark-h2o:latest -f Spark-H2O.Dockerfile . && docker push hishailesh77/spark-h2o:latest