Centralized Log Management with the Elastic Stack

Centralized Log Management with the Elastic Stack

This Elastic Stack training is a complete overview of Elasticsearch, Logstash, Kibana, and Machine Learning features. Attendees will learn how to leverage logs to spot infrastructure problems, business issues, or cyber attacks using dashboards, parsing rules, and machine learning algorithms. The training program covers installation, configuration, and troubleshooting of high availability Elasticsearch clusters, working with logging pipelines, and building actionable dashboards for complex, distributed systems like Cloud Foundry.

Who should attend

  • Understand how logs from complex distributed systems are managed.
  • Install, configure, monitor, and troubleshoot Elasticsearch clusters.
  • Parse log data with Logstash.
  • Model and detect anomalies in time series data.
  • Set up a centralized logging system from scratch

Course deliverables

  • Gain a basic understanding of machine learning concepts
  • Learn how to use main troubleshooting techniques of machine learning
  • Learn how to build neural networks with TensorFlow

Training program

1
day
See details
Introduction to Deep Learning
  • Agenda for the training.
  • General introduction for neural networks and deep learning.
  • i>
Neural Networks
  • Components of an artificial neural network.
  • Connections and weights.
  • Propagation function.
  • Choosing a cost function.
  • Learning paradigms.
  • Types of neural networks.
Activation Functions
  • Identity function, unit step (binary step) function, sigmoid function, hyperbolic function, inverse trigonometric function, softmax function, rectified linear unit (ReLU), exponential linear unit (ELU), maxout
Learning of Neural
  • Training, test, and validation sets.
Networks
  • Selection of a validation dataset: holdout method and cross-validation.
  • Instance space decomposition.
Supervised Learning
  • Labeled training data.
  • Determination of the type of training examples.
  • Gathering a training set.
  • Determination of the input feature representation of the learned function.
  • Running the learning algorithm on the gathered training set.
  • Evaluation of the accuracy of the learned function.
Unsupervised Learning
  • Approaches to unsupervised learning.
  • Clustering, anomaly detection, Autoencoders, Generative Adversarial Networks, self-organizing map.
Reinforcement Learning
  • Markov decision process.
  • Algorithms for control learning.
  • Optimality criteria.
  • Brute force approach.
  • Value function approaches.
  • Monte Carlo methods.
  • Temporal difference methods.
  • End-to-end reinforcement learning.
2
day
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Perceptron
  • Initialization of the weights and the threshold.
  • Training of the algorithm.
  • Calculation of the actual output.
  • Multiclass perceptron.
  • Multilayer Perceptron
    • Activation function.
    • Layers of nonlinearly-activating nodes.
    • Learning of the perceptron.
    Recurrent Neural Networks
    • Directed graph along a sequence.
    • Infinite and finite impulses.
    • Long short-term memory networks.
    • Fully Recurrent Neural Network.
    • Independently Recurrent Neural Network.
    • Recursive Neural Network.
    • Hopfield network.
    • Boltzmann machine.
    • Restricted Boltzmann machine.
    • Recurrent Multilayer Perceptrons network.
    • Multiple Timescales Recurrent Neural Network.
    Autoencoders
    • Directed graph along a sequence.
    • Infinite and finite impulses.
    • Long short-term memory networks.
    • Fully Recurrent Neural Network.
    • Independently Recurrent Neural Network.
    • Recursive Neural Network.
    • Hopfield network.
    • Boltzmann machine.
    • Restricted Boltzmann machine.
    • Recurrent Multilayer Perceptrons network.
    • Multiple Timescales Recurrent Neural Network.
    Using Cloud Foundry security groups
    • Structure
    • Security groups scopes
    • Creating security groups
    • Verifying results using CATs
    • Binding security groups
    • Viewing security groups
    • Managing security groups
3
day
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Learn how the BOSH Director and the Agent work
  • SSH into running VMs created by BOSH
  • Investigating logs and BOSH Agent settings
  • Seeing how the BOSH Director talks to a BOSH
  • Agent in real time
Buildpacks
  • What is a buildpack?
  • The structure of a buildpack
  • Creating a custom buildpack
High Availability (HA)
  • An HA deployment
  • Rolling updates
  • Scaling Cloud
  • Foundry
Security
  • Web app SSO
  • Configuring TLS and HTTPS
  • Security key management (PKI)
  • Security inside a CF cluster
  • Secure network configuration
4
day
See details
Advanced Cloud Foundry CLI usage
  • What are CF CLI plug-ins?
  • How can I install a CF CLI plug-in?
  • Several useful plug-ins for listing available resources, simplifying blue-green deployments and other tasks.
  • How to delete unnecessary plug-ins?
  • Tracing API calls
  • CLI plug-ins
  • Raw API requests
Troubleshooting
  • Debugging techniques: cf apps, cf events, and cf logs recent
  • Remote debugging in CF
  • Troubleshooting containerized services
Concourse automation
  • Concourse basics
  • Running a simple
  • Concourse
  • Pipelines to deploy a service

Prerequisites

  • A basic knowledge of Linux (ssh, scp, vim, grep)
  • Basic Docker Experience
  • First-hand experience with an IaaS provider—AWS (EC2, VPC, S3, Route53, RDS)
  • A workstation with the following capabilities
  • Reporting forms: premiums collected/paid, amounts receivable/payable etc.
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Don't see a location that works for you?
Request training in your office
Contact us
Atlanta
Chicago
Boston
Austin
Dallas
Denver
Jacksonville
Los Angeles
New York City
Seattle
Toronto
San Francisco
Washington DC
Silicon Valley
Amsterdam
Frankfurt
Helsinki
London
Madrid
Munich
Oslo
Rome
Paris
Stockholm
Vienna
Zurich
Dubai
Tel Aviv

Our trainers

Sergey Matyukevich
Solutions Architect / Trainer
Sergey Balashevich
Hyperledger Developer / Trainer
Christian Hercules
Cloud Foundry Engineer
Andrei Krasnitski
Cloud Foundry Engineer
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Natalia Usenia

Training coordinator

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