Course Overview

We know the term blockchain mainly in relation to Bitcoin and blockchain technology. Blockchain Organizing is broader and mainly looks at the blockchain as a means to organize transactions almost frictionlessly between supply and demand. It is a way to reduce uncertainty and organize trust, without increasing complexity. We must realize that we will only get very little with technology if we do not change the model with which we organize supply and demand. The internet has not done this enough yet.

Blockchain Organizing is not technical, but it does use technology. You don’t need to know how an combustion engine works to drive a car. Blockchain Organizing is mainly about the impact on your position and organization, comparable to, for example, the question of what the impact of the internet was in the 1990s. Blockchain Organizing is a combination of a disruptive technology (blockchain) with a fundamentally new way of organizing. Blockchain Organizing is a form of organizational technology.

Course Objectives

  • awareness that with technology we also have to adapt the organizational model
  • gain knowledge about Blockchain Organizing
  • exchange experience which uses cases are useful
  • get in touch with other interested professionals to work together afterwards
  • get an idea of what Blockchain Organizing can mean for your position, organization and network
  • get more information about the Weconomics organizational model, knowledge infrastructure and transition program

Course Content

09:00- 09:30 hours | Introduction and context

  • Digital transformation
  • Using new organizational models with new technology
  • Are we aware that technology alone does not do enough?

09:30-10:30 hours | Blockchain basics technique

  • Blockchain basics
  • What can you use it for and what should not
  • Tokens, coins, cryptocurrencies en ICO’s
  • Different blockchains
  • Blockchain concepts

10:30-11:00 am | Why Blockchain?

  • Blockchain: next phase and extra layer on the internet
  • The organization of trust
  • New begins with letting go of old
  • Perspective on New Organizing
  • What organizational problems can we solve with it?

11:00-11:15 am | Coffee and tea break

11:15-12:00 hours | Blockchain in practice

  • Domain: fintech, organization of work, government and supply chain etc.
  • Function: identity management, documents, smart contracts
  • Other applications
  • Possibilities: exploiting strategy and opportunities of blockchain
  • What does it mean for your business model?
  • Group work and discuss use cases participants

12:00-12:30 pm | Consequences for you

  • Consequences for your role, function, organization and network
  • Blockchain projects (setting up)
  • Next step in transition programme
  • Knowledge Centre blockchain organisation (BLOCK)

12:30-13:00 hours | Use cases and questions

  • Which cases do you see for your organization, customers or network?
  • Questions and completion

Course Overview

This course reviews Blockchain and the architectural and technical issues that need to be considered before launching a development program. There are many decisions and issues that face the technical project team and this class will enable you to make those decisions.

Course Objectives

What you will learn:

  • What is Blockchain?
  • How does Blockchain work?
  • Blockchain types
  • How is Blockchain different from what we use today?
  • Blockchain use cases
  • What does a Blockchain app look like?
  • How do I design, develop, and test a Blockchain app?

Course Content

What is Blockchain?

  • A record of keeping systems
  • Trust
  • Decentralization
  • Trustless environment

How does Blockchain work?

  • Announcements
  • Blocks
  • Nodes
  • Chaining
  • Verification
  • Consensus
  • Scalability
  • Privacy
  • Crypto hashing
  • Digital fingerprinting
  • PoW versus PoS

Blockchain Types

  • Public versus private
  • Open versus closed
  • Smart contracts
  • Blockchain as history
  • Tokens/coins
  • Gas

How is Blockchain different from what we have today?

  • Decentralization
  • Peer-to-peer architecture
  • Software versus firmware
  • Database versus Blockchain
  • Distributed database or other technology?
  • Data sovereignty
  • Group consensus

Blockchain Use Cases

  • Use case examples
    • Currency
    • Banking
    • Voting
    • Medical records
    • Supply chain/value chain
    • Content distribution
    • Verification of software updates
    • Law enforcement
    • Title and ownership records
    • Social media and online credibility
    • Fractional asset ownership
    • Cable television billing
  • High fault tolerance
  • DDoS-proof
  • Public or private Blockchain?
  • Who are the participants?

What does a Blockchain app look like?

  • DApp
  • Resembles typical full stack web application
  • Any internal state changes and all transactions are written to the Blockchain
  • Node.js
  • IDE
  • Public Blockchain visibility
  • Private Blockchain solutions
  • Oracles

How do I design a Blockchain app?

  • What does the solution need to let users do?
  • Will the proposed solution reduce or remove the problems and pain points felt by users?
  • What should this solution prevent users from doing?
  • Do you need a solution ready for heavy use on day 1?
  • Is your solution idea enhanced by the use of Blockchain?
  • Does the use of Blockchain create a better end-user experience and how?
  • Has your business developed custom software solutions before?
  • What level of support are you going to need?
  • How big is the developer community?
  • Does your vision of the future align with the project or platform’s vision of the future?
  • Does the platform aim to make new and significant contributions to the development space, or is it an efficiency/cost play?
  • Should the solution be a public or private Blockchain?
  • Should the solution be an open or closed Blockchain?
  • Create a plan for contract updates and changes
  • Hybrid solutions
  • Monetary exchanges?

How do I develop a Blockchain app?

  • Agile approach pre-release
  • Define guiding principles up front
  • Software versus firmware
  • Announcements, not transactions!
  • Classes, not contracts
  • Link contracts to share functions
  • Use calling contracts to keep contract addresses the same
  • Hyperledger versus Ethereum
  • Consider the number of users and number of transactions per user
  • Should a blockless solution be applied?
  • Performance
  • Security
  • Anonymity
  • Monolithic versus modular
  • Sandwich complexity model

How do I test a Blockchain app?

  • Recommendations
  • Security
  • Networks (Ethereum)

Course Overview

SQL for PostgreSQL Course Overview

The SQL for PostgreSQL course is designed to give delegates practical experience in writing SQL statements and scripts using PostgreSQL. The basic SELECT statement,the use of SQL functions,and the basic table and view handling statements are introduced.

Exercises and examples are used throughout the course to give practical hands-on experience with the techniques covered.

Course Objectives

Course Objectives

The course aims to provide the delegate with the knowledge to be able to use SQL to manipulate data held in a PostgreSQL database and to progress their SQL skills beyond the basics.

Course Content

SQL for PostgreSQL Training Course

Course Contents – DAY 1

Course Introduction

  • Administration and Course Materials
  • Course Structure and Agenda
  • Delegate and Trainer Introductions

Session 1: RELATIONAL DATABASE CONCEPTS

  • What is an PostgreSQL Database
  • Relational Database Structures
  • Tables,Rows and Columns
  • Indexes,Primary Keys and Foreign Keys
  • Supported Datatypes
  • The Data Dictionary

Session 2: USING psql

  • What is psql
  • Getting Started
  • Entering and Executing SQL Statements
  • Editing SQL Statements
  • Creating,Editing and Executing SQL Files

Session 3: RETRIEVING DATA WITH THE SELECT STATEMENT

  • The SELECT Statement
  • The SELECT and FROM Clauses
  • Conditions and the WHERE Clause
  • Other Conditional Operators
  • Logical Operators
  • The ORDER BY Clause
  • Column Aliases
  • Arithmetic Expressions
  • Precedence of Operators

Session 4: AGGREGATE FUNCTIONS

  • Overview of Built In Aggregate Functions
  • The GROUP BY Clause
  • The HAVING Clause

Session 5: JOINING TABLES

  • Overview of Table Joins
  • Inner Joins
  • Table Aliases
  • Outer Joins
  • Self Joins
  • ANSI Standard Joins

Session 6: BASIC SUBQUERIES AND SET OPERATORS

  • Overview of Subqueries
  • Basic Subqueries
  • Set Operators
  • The Union,Intersect and Except Operators

SQL for PostgreSQL Training Course

Course Contents – DAY 2

Session 7: NUMERIC,CHARACTER AND DATE FUNCTIONS

  • Function Types
  • Numeric Functions
  • Character Functions
  • String Concatenation
  • Date Arithmetic and Date Functions

Session 8: CONVERSION AND MISCELLANEOUS FUNCTIONS

  • Conversion Functions
  • CASE Expressions
  • The COALESCE and NULLIF Functions

Session 9: COMPLEX SUBQUERIES

  • Subqueries Usage
  • In-Line Views
  • Subqueries with Joins
  • Multi Column Subqueries
  • Correlated Subqueries
  • Subquery Rules

SQL for PostgreSQL Training Course

Course Contents – DAY 3

Session 10: MANAGING DATA

  • Inserting Rows
  • Updating Rows
  • Updating Join and Upsert Rows
  • Deleting Rows
  • Transaction Control
  • Commit and Rollback
  • Savepoints
  • Commits and Constraints
  • Amending Data in pgAdmin 4
  • Insert data to Table from csv file
  • Export data from Table to csv file

Session 11: MANAGING TABLES

  • Creating Tables
  • Specifying Constraints
  • Altering Tables,Columns and Constraints
  • Dropping Tables,Columns and Constraints
  • Recovering Dropped Tables
  • Copying Tables

Session 12: MANAGING INDEXES AND VIEWS

  • Creating Indexes
  • Dropping Indexes
  • Listing Indexes
  • Creating and Using Views
  • Dropping Views
  • Listing Views

Session 13: MANAGING SEQUENCES

  • Create a Sequence
  • View Sequence Details

Course Overview

Artificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course, you will spend a day in the life of a data scientist so that you can collaborate efficiently with data scientists and build applications that integrate with ML. You will learn the basic process data scientists use to develop ML solutions on Amazon Web Services (AWS) with Amazon SageMaker. You will experience the steps to build, train, and deploy an ML model through instructor-led demonstrations and labs.

Course level: Intermediate

Duration: 1 day


Activities

This course includes presentations, hands-on labs, and demonstrations.

Course Objectives

In this course, you will learn to:

  • Discuss the benefits of different types of machine learning for solving business problems
  • Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
  • Explain how data scientists use AWS tools and ML to solve a common business problem
  • Summarize the steps a data scientist takes to prepare data
  • Summarize the steps a data scientist takes to train ML models
  • Summarize the steps a data scientist takes to evaluate and tune ML models
  • Summarize the steps to deploy a model to an endpoint and generate predictions
  • Describe the challenges for operationalizing ML models
  • Match AWS tools with their ML function

Course Content

Module 1: Introduction to Machine Learning

  • Benefits of machine learning (ML)
  • Types of ML approaches
  • Framing the business problem
  • Prediction quality
  • Processes, roles, and responsibilities for ML projects

Module 2: Preparing a Dataset

  • Data analysis and preparation
  • Data preparation tools
  • Demonstration: Review Amazon SageMaker Studio and Notebooks
  • Hands-On Lab: Data Preparation with SageMaker Data Wrangler

Module 3: Training a Model

  • Steps to train a model
  • Choose an algorithm
  • Train the model in Amazon SageMaker
  • Hands-On Lab: Training a Model with Amazon SageMaker
  • Amazon CodeWhisperer
  • Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks

Module 4: Evaluating and Tuning a Model

  • Model evaluation
  • Model tuning and hyperparameter optimization
  • Hands-On Lab: Model Tuning and Hyperparameter Optimization with Amazon SageMaker

Module 5: Deploying a Model

  • Model deployment
  • Hands-On Lab: Deploy a Model to a Real-Time Endpoint and Generate a Prediction

Module 6: Operational Challenges

  • Responsible ML
  • ML team and MLOps
  • Automation
  • Monitoring
  • Updating models (model testing and deployment)

Module 7: Other Model-Building Tools

  • Different tools for different skills and business needs
  • No-code ML with Amazon SageMaker Canvas
  • Demonstration: Overview of Amazon SageMaker Canvas
  • Amazon SageMaker Studio Lab
  • Demonstration: Overview of SageMaker Studio Lab
  • (Optional) Hands-On Lab: Integrating a Web Application with an Amazon SageMaker Model Endpoint

Course Overview

You already completed the preparation course and activated your achievement badge for this course. Now you want to know if you are ready for the exam. Microsoft creates exams based on real world scenarios. These are not necessarily covered in the preparation course you did.

This 2-day exam preparation training will help you to complete your study for certification. During the first day you will run through a selection of hands-on labs guided by a trainer. The second day you will be prepared for the actual exam with practice tests.

Course Objectives

You will be best prepared for the actual exam.

Course Content

Trainer guided hands on labs

Trainer guided practice exam

Practice exam

Exam – you will not do the exam during the course

Course Overview

PostgreSQL Administration Course Overview

This PostgreSQL Administration course covers administration,maintenance,security and performance tuning of PostgreSQL version 10,11,12,13,14,15 and 16 databases.

Exercises and examples are used throughout the course to give practical hands-on experience with the techniques covered.

Course Objectives

Course Objectives

This course aims to provide the delegate with the knowledge to be able to install,administer,maintain,backup,recover and tune a PostgreSQL database.


Course Content

PostgreSQL Administration Training Course

Course Contents – DAY 1

Course Introduction

  • Administration and Course Materials
  • Course Structure and Agenda
  • Delegate and Trainer Introductions

Session 1: INSTALLATION AND CONFIGURATION OF POSTGRESQL

  • PostgreSQL Version Numbers
  • PostgreSQL Configuration Recommendations
  • Single Cluster and Database per Server
  • File System Layouts
  • Install PostgreSQL
  • Install PostgreSQL on Linux/UNIX
  • Post-Installation Setup
  • Create a Database Cluster
  • Basic Server Configuration
  • Account Management

Session 2: POSTGRESQL ARCHITECTURE

  • Overview of PostgreSQL Architecture
  • The PostgreSQL Instance
  • How Data is Processed by PostgreSQL
  • PostgreSQL Processes
  • Shared Memory
  • PostgreSQL Directory Structure
  • Manage a PostgreSQL Instance
  • The PostgreSQL Configuration Files
  • Multiversion Concurrency Control
  • The Vacuum utility
  • PostgreSQL WAL

Session 3: POSTGRESQL CLIENT APPLICATIONS

  • Overview of PostgreSQL Client Applications
  • The PostgreSQL Interactive Client Terminal – psql
  • The pgAdmin Tool
  • Client and host based access control
  • Client Connection Problems
  • Authentication Failures
  • Server Startup Failures

Session 4: CREATE AND CONFIGURE A DATABASE

  • Create a New Database using the CREATE DATABASE Command
  • Create a New Database using pgAdmin
  • Start and Stop the Database Server
  • Drop a Database
  • Copy a Database
  • List Databases
  • Obtain Database Object Sizes
  • Initialise a Cluster

PostgreSQL Administration Training Course

Course Contents – DAY 2

Session 5: POSTGRESQL SCHEMAS

  • Overview of the Key Concepts of PostgreSQL Schemas
  • Create and Drop a Schema
  • The Public Schema
  • The Schema Search Path
  • Schemas and Privileges
  • The System Catalog Schema
  • Overview of the Information Schema

Session 6: ROLE MANAGEMENT

  • Overview of PostgreSQL Roles and Privileges
  • Create a User Defined Role
  • Role Attributes
  • Role Membership
  • Assigning Users to Roles
  • Group and User Role Inheritance
  • Removing Roles
  • Troubleshooting and Understanding Role Access

Session 7: FINE GRAINED ACCESS CONTROL USING GRANT

  • Control Database Level Permissions
  • Control Schema Level Permissions
  • Grant Table Level Permissions
  • Define Access Privileges with the GRANT Command
  • Remove Access Privileges with the REVOKE Command
  • Manage Column Rights

Session 8: INDEX CREATION AND MANAGEMENT

  • Index Overview
  • The CREATE INDEX Command
  • Index Types
  • Efficient Usage of PostgreSQL Indexes
  • Index creation
  • B-tree,Hash and BRIN Indexes
  • Single Column Indexes
  • Unique indexes
  • Multicolumn Indexes
  • The INCLUDE clause
  • Partial Indexes
  • Index Based Expressions
  • Creating an index concurrently
  • The REINDEX command
  • List Indexes
  • Manage and Maintain Indexes
  • When Indexes Should be Avoided

Session 9: TRANSACTIONS AND CONCURRENCY

  • Overview of Transaction Processing in PostgreSQL
  • Transaction Properties
  • Transaction Control
  • Multi-version Concurrency Control
  • Concurrency Problems
  • Isolation Levels
  • Implicit Locking
  • Explicit Locking
  • Possible Causes of Lock Contention
  • Deadlocks
  • Advisory Locks
  • Lock Management Parameters

PostgreSQL Administration Training Course

Course Contents – DAY 3

Session 10: POSTGRESQL DATABASES STRUCTURE

  • The PostgreSQL Configuration Files
  • Relocate the Configuration Files
  • Physical Storage and File Layout
  • Overview of Tablespace Usage
  • Table and Row Storage
  • Column Limitations
  • Free Space Map
  • The Visibility Map
  • Index Storage

Session 11: MANAGE TABLESPACES

  • Overview of PostgreSQL Tablespaces
  • Default PostgreSQL Tablespaces
  • Create a Tablespace using SQL Commands and pgAdmin
  • Create a Tablespace in the UNIX Operating System
  • Alter a Tablespace
  • Drop a Tablespace

Session 12: POSTGRESQL LOGGING

  • PostgreSQL Event Log Destinations
  • Configuring syslog,eventlog,stderr and csv format output
  • Configuring What Should be Logged and When

Session 13: POSTGRESQL EXTENSIONS

  • Install the PostgreSQL Contrib Module
  • List the Available Extensions
  • Add an Extension to the postgresql.conf File
  • Create an Extension in a Database
  • Drop an Extension in a Database

PostgreSQL Administration Training Course

Course Contents – DAY 4

Session 14: BACKUP AND RECOVERY OF DATABASES

  • Overview of Backup Methods
  • Export and Import Operations with COPY
  • Backup a Database with Operating System Commands
  • Backup a Database with pg_dump
  • Backup All Databases with pg_dumpall
  • Backup User Credentials
  • Backup Database Object Definitions
  • Overview of Database Restore
  • Restore using psql
  • Restore using pg_restore
  • File system Backup and Recovery

Session 15: POINT-IN-TIME RECOVERY (PITR)

  • Write-ahead Logging and Crash Recovery
  • Checkpoints
  • List the Transaction Logs
  • Transaction Log Optimisation
  • Overview of PITR
  • Setup PITR
  • Continuous Archiving
  • Test Transaction Log Archiving
  • Create a Base Backup using the Low Level API
  • Create a Base Backup using pgBaseBackup
  • The PITR Recovery Process
  • How To Perform a PITR Recovery
  • Recovery Configuration Parameters
  • Timelines
  • Locating the Correct Timestamp
  • Restore Points
  • Clean up the Archived Transaction Logs

PostgreSQL Administration Training Course

Course Contents – DAY 5

Session 16: THE POSTGRESQL QUERY OPTIMIZER

  • Query Optimization
  • Optimization Operations
  • Optimization Decisions
  • Scan Methods
  • Join Methods
  • Join Order
  • Statement Transformation
  • Prepared Statements
  • Query Performance Analysis
  • Detect Slow Queries
  • Use EXPLAIN to optimize Queries and Indexes
  • Execution Plans
  • Query Planner Statistics
  • The ANALYZE command
  • The CREATE STATISTICS command
  • Parameters Affecting Optimization
  • Memory Settings That Affect Performance

Session 17: ROUTINE DATABASE MAINTENANCE

  • Optimize Storage and Manage Clean up with VACUUM
  • Configure VACUUM
  • Configure Autovacuum
  • Cost Based Vacuum Delay
  • Track a VACUUM Process
  • Routine Maintenance Tasks

Session 18: SERVER PERFORMANCE MONITORING AND TUNING

  • Monitor Database Activities
  • System Monitoring & PostgreSQL Monitoring
  • Performance Statistics in the Server Log
  • Statistics Collection Configuration
  • Monitor Database Activity
  • Monitor Table,Index and SQL Statement Activity
  • Monitor Background Writer,WAL and Archiving Activity
  • Progress Reporting
  • Locks

Session 19: POPULATE A DATABASE EFFICIENTLY

  • DISABLE autocommit
  • Configure Variables for Increased Performance
  • Use the COPY Command to Bulk Load Data
  • Drop Indexes and Foreign Keys Temporarily
  • Use the COPY command to Bulk Load Data
  • Temporarily drop indexes and Foreign Key Constraints before a Bulk Load
  • Configure Variables for Increased Performance
  • Temporarily Disable WAL Archival and Streaming Replication
  • Use pg_dump Efficiently

Course Overview

This course helps you explore the IBM Cloud Pak for Data monitoring and alerting framework. In this course, you customize alert forwarding, configure the monitoring stack so that it sends Cloud Pak for Data metrics to Prometheus.  You perform routine Cloud Pak for Data monitoring, which leads to investigating and resolving issues in your environment. You also deploy scripts that use platform APIs, and enhance the monitoring feature by introducing custom monitors.

Virtual Learning

This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

Course Objectives

  • Explain the Cloud Pak for Data monitoring and alerting framework
  • Set up the SMTP settings and alert forwarding
  • Configure the monitoring stack
  • Perform routine Cloud Pak for Data monitoring
  • Use platform APIs to review events and monitors
  • Deploy a custom monitor

Course Content

  • Introduction
  • Monitor Cloud Pak for Data
  • Work with the monitoring stack
  • Perform a routine Cloud Pak for Data monitoring
  • Use alerting APIs for platform monitoring
  • Set up a custom monitor
  • Review and evaluation

Course Overview

This course builds upon and extends the DevOps methodology prevalent in software development to build, train, and deploy machine learning (ML) models. The course is based on the four-level MLOPs maturity framework. The course focuses on the first three levels, including the initial, repeatable, and reliable levels. The course stresses the importance of data, model, and code to successful ML deployments. It demonstrates the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course also discusses the use of tools and processes to monitor and take action when the model prediction in production drifts from agreed-upon key performance indicators.

Course Objectives

In this course, you will learn to:

  • Explain the benefits of MLOps
  • Compare and contrast DevOps and MLOps
  • Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies
  • Set up experimentation environments for MLOps with Amazon SageMaker
  • Explain best practices for versioning and maintaining the integrity of ML model assets (data, model, and code)
  • Describe three options for creating a full CI/CD pipeline in an ML context
  • Recall best practices for implementing automated packaging, testing and deployment. (Data/model/code)
  • Demonstrate how to monitor ML based solutions
  • Demonstrate how to automate an ML solution that tests, packages, and deploys a model in an automated fashion; detects performance degradation; and re-trains the model on top of newly acquired data

Course Content

Day 1

Module 1: Introduction to MLOps

  • Processes
  • People
  • Technology
  • Security and governance
  • MLOps maturity model

Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio

  • Bringing MLOps to experimentation
  • Setting up the ML experimentation environment
  • Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio
  • Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog
  • Workbook: Initial MLOps

Module 3: Repeatable MLOps: Repositories

  • Managing data for MLOps
  • Version control of ML models
  • Code repositories in ML

Module 4: Repeatable MLOps: Orchestration

  • ML pipelines
  • Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines

Day 2

Module 4: Repeatable MLOps: Orchestration (continued)

  • End-to-end orchestration with AWS Step Functions
  • Hands-On Lab: Automating a Workflow with Step Functions
  • End-to-end orchestration with SageMaker Projects
  • Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects
  • Using third-party tools for repeatability
  • Demonstration: Exploring Human-in-the-Loop During Inference
  • Governance and security
  • Demonstration: Exploring Security Best Practices for SageMaker
  • Workbook: Repeatable MLOps

Module 5: Reliable MLOps: Scaling and Testing

  • Scaling and multi-account strategies
  • Testing and traffic-shifting
  • Demonstration: Using SageMaker Inference Recommender
  • Hands-On Lab: Testing Model Variants

Day 3

Module 5: Reliable MLOps: Scaling and Testing (continued)

  • Hands-On Lab: Shifting Traffic
  • Workbook: Multi-account strategies

Module 6: Reliable MLOps: Monitoring

  • The importance of monitoring in ML
  • Hands-On Lab: Monitoring a Model for Data Drift
  • Operations considerations for model monitoring
  • Remediating problems identified by monitoring ML solutions
  • Workbook: Reliable MLOps
  • Hands-On Lab: Building and Troubleshooting an ML Pipeline

 

Checkpoint Training Courses Malaysia

About Checkpoint

Expand your knowledge of Check Point products and services with world-class training and accredition courses from LernIX.

This certification courses provides an understanding of the advanced concepts and skills necessary to automate and orchestrate tasks relating to managing Check Point Security Policies.

 

Cisco Certification Training Courses Malaysia

About Cisco Certification

Whether you have years of IT experience or are just starting your journey in the field, getting certified is a great way to boost your career. Certifications are proof of knowledge, aptitude, and a lifelong learning mentality, and hiring managers trust certified employees to connect, secure and automate Cisco networks across the globe.

Launch a new career. Amplify your current skills. Learn just because you want to. No matter where you are in your journey, Cisco can help you thrive in the IT world. Cisco training and certifications are recognized worldwide, preparing you for a range of tech roles – and with hands-on experiences, online resources, and self-paced courses, you can learn the way that works best for you.