Microsoft Azure AI-900 Training-The only Course you need

⏰26 hours | ▶️ 23 Videos | 📣 9152 Participants | 🎓 5782 Reviews | 4.9 ⭐⭐⭐⭐⭐

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Course Description

Microsoft Azure AI 900 training delivers knowledge on artificial intelligence and machine learning with Azure services.

Azure AI 900 course is proposed for technical and non-technical background applicants planning to start a career with Azure with artificial intelligence.

Learn cloud with artificial intelligence by joining our online classes under the guidance of expert trainers with complete hands-on practices.

Avail 24/7 support from our tech team and clear your queries on the course.

You can earn Azure AI 900 certification by attending the azure 900 exams by attending classes with some basic programming knowledge.

Enroll now with us and avail yourself of the accreditation and pursue your dream job.

Describe Artificial Intelligence workloads and considerations

Identify features of common AI workloads

identify prediction/forecasting workloads
identify features of anomaly detection workloads
identify computer vision workloads
identify natural language processing or knowledge mining workloads
identify conversational AI workloads

Identify guiding principles for responsible AI

describe considerations for fairness in an AI solution
describe considerations for reliability and safety in an AI solution
describe considerations for privacy and security in an AI solution
describe considerations for inclusiveness in an AI solution
describe considerations for transparency in an AI solution
describe considerations for accountability in an AI solution

Describe fundamental principles of machine learning on Azure

Identify common machine learning types

identify regression machine learning scenarios
identify classification machine learning scenarios
identify clustering machine learning scenarios

Describe core machine learning concepts

identify features and labels in a dataset for machine learning
describe how training and validation datasets are used in machine learning
describe how machine learning algorithms are used for model training
select and interpret model evaluation metrics for classification and regression

Identify core tasks in creating a machine learning solution

describe common features of data ingestion and preparation
describe common features of feature selection and engineering
describe common features of model training and evaluation
describe common features of model deployment and management

Describe capabilities of no-code machine learning with Azure Machine Learning

automated Machine Learning UI
azure Machine Learning designer

Describe features of computer vision workloads on Azure

Identify common types of computer vision solution

identify features of image classification solutions
identify features of object detection solutions
identify features of semantic segmentation solutions
identify features of optical character recognition solutions
identify features of facial detection, facial recognition, and facial analysis solutions

Identify Azure tools and services for computer vision tasks

identify capabilities of the Computer Vision service
identify capabilities of the Custom Vision service
identify capabilities of the Face service
identify capabilities of the Form Recognizer service

Describe features of Natural Language Processing (NLP) workloads on Azure

Identify features of common NLP Workload Scenarios

identify features and uses for key phrase extraction
identify features and uses for entity recognition
identify features and uses for sentiment analysis
identify features and uses for language modeling
identify features and uses for speech recognition and synthesis
identify features and uses for translation

Identify Azure tools and services for NLP workloads

identify capabilities of the Text Analytics service
identify capabilities of the Language Understanding Intelligence Service (LUIS)
identify capabilities of the Speech service
identify capabilities of the Translator Text service

Describe features of conversational AI workloads on Azure

Identify common use cases for conversational AI

identify features and uses for webchat bots
identify features and uses for telephone voice menus
identify features and uses for personal digital assistants
identify common characteristics of conversational AI solutions

Identify Azure services for conversational AI

identify capabilities of the QnA Maker service
identify capabilities of the Bot Framework

Describe Artificial Intelligence workloads and considerations

Identify features of common AI workloads

identify prediction/forecasting workloads
identify features of anomaly detection workloads
identify computer vision workloads
identify natural language processing or knowledge mining workloads
identify conversational AI workloads

Identify guiding principles for responsible AI

describe considerations for fairness in an AI solution
describe considerations for reliability and safety in an AI solution
describe considerations for privacy and security in an AI solution
describe considerations for inclusiveness in an AI solution
describe considerations for transparency in an AI solution
describe considerations for accountability in an AI solution

Describe fundamental principles of machine learning on Azure

Identify common machine learning types

identify regression machine learning scenarios
identify classification machine learning scenarios
identify clustering machine learning scenarios

Describe core machine learning concepts

identify features and labels in a dataset for machine learning
describe how training and validation datasets are used in machine learning
describe how machine learning algorithms are used for model training
select and interpret model evaluation metrics for classification and regression

Identify core tasks in creating a machine learning solution

describe common features of data ingestion and preparation
describe feature engineering and selection
describe common features of model training and evaluation
describe common features of model deployment and management

Describe capabilities of no-code machine learning with Azure Machine Learning studio

automated ML Wizard UI
azure Machine Learning designer

Describe features of computer vision workloads on Azure

Identify common types of computer vision solution

identify features of image classification solutions
identify features of object detection solutions
identify features of semantic segmentation solutions
identify features of optical character recognition solutions
identify features of facial detection, facial recognition, and facial analysis solutions

Identify Azure tools and services for computer vision tasks

identify capabilities of the Computer Vision service
identify capabilities of the Custom Vision service
identify capabilities of the Face service
identify capabilities of the Form Recognizer service

Describe features of Natural Language Processing (NLP) workloads on Azure

Identify features of common NLP Workload Scenarios

identify features and uses for key phrase extraction
identify features and uses for entity recognition
identify features and uses for sentiment analysis
identify features and uses for language modeling
identify features and uses for speech recognition and synthesis
identify features and uses for translation

Identify Azure tools and services for NLP workloads

identify capabilities of the Text Analytics service
identify capabilities of the Language Understanding service (LUIS)
identify capabilities of the Speech service

Describe features of conversational AI workloads on Azure

Identify common use cases for conversational AI

identify features and uses for webchat bots
identify features and uses for telephone voice menus
identify features and uses for personal digital assistants
identify common characteristics of conversational AI solutions

Identify Azure services for conversational AI

identify capabilities of the QnA Maker service
identify capabilities of the Azure Bot service

FAQ’s

(more…)

Microsoft Azure AI-100 Training – The only Course you need

⏰26 hours | ▶️ 23 Videos | 📣 9152 Participants | 🎓 3819 Reviews | 4.9 ⭐⭐⭐⭐⭐

Choose a Plan that Works for You

Self Paced

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Instructor Led Live Training

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Upcoming Batches PST

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Oct 09(1 HR A DAY)

06:00 PM PST

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Course Description

Microsoft Azure AI 100 training designates conversational AI, deep learning, data mining, and Microsoft AI solutions.

Enroll andLearnto design and implement Azure AI solutions, performance requirements, and app building using AI in our courses.

Avail of our online classes with complete support and guidance from our tech team and clear your queries on the tech.

We cover all the required perceptions in our online courses with hands-on experiments with guidance under our expert trainers

Join and exercise for the exam and achieve your dream job by completing the AI 100 certification.

Recommend Azure Cognitive Services APIs to meet business requirements

select the processing architecture for a solution
select the appropriate data processing technologies
select the appropriate AI models and services
identify components and technologies required to connect service endpoints
identify automation requirements

Map security requirements to tools, technologies, and processes

identify processes and regulations needed to conform with data privacy, protection, and
regulatory requirements
identify which users and groups have access to information and interfaces
identify appropriate tools for a solution
identify auditing requirements

Select the software, services, and storage required to support a solution

identify appropriate services and tools for a solution
identify integration points with other Microsoft services
identify storage required to store logging, bot state data, and Azure Cognitive Services output

Design AI solutions

Design solutions that include one or more pipelines

define an AI application workflow process
design a strategy for ingest and egress data
design the integration point between multiple workflows and pipelines
design pipelines that use AI apps
design pipelines that call Azure Machine Learning models
select an AI solution that meet cost constraints

Design solutions that use Cognitive Services

design solutions that use vision, speech, language, knowledge, search, and anomaly
detection APIs

Design solutions that implement the Microsoft Bot Framework

integrate bots and AI solutions
design bot services that use Language Understanding (LUIS)
design bots that integrate with channels
integrate bots with Azure app services and Azure Application Insights

Design the compute infrastructure to support a solution

identify whether to create a GPU, FPGA, or CPU-based solution
identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
select a compute solution that meets cost constraints

Design for data governance, compliance, integrity, and security

define how users and applications will authenticate to AI services
design a content moderation strategy for data usage within an AI solution
ensure that data adheres to compliance requirements defined by your organization
ensure appropriate governance of data
design strategies to ensure that the solution meets data privacy regulations and industry standards

Implement and monitor AI solutions

Implement an AI workflow
- develop AI pipelines
- manage the flow of data through the solution components
- implement data logging processes
- define and construct interfaces for custom AI services
- create solution endpoints
- develop streaming solutions

Integrate AI services and solution components

configure prerequisite components and input datasets to allow the consumption of Azure Cognitive Services APIs
configure integration with Azure Cognitive Services
configure prerequisite components to allow connectivity to the Microsoft Bot Framework
implement Azure Cognitive Search in a solution

Monitor and evaluate the AI environment

identify the differences between KPIs, reported metrics, and root causes of thedifferences
identify the differences between expected and actual workflow throughput
maintain an AI solution for continuous improvement
monitor AI components for availability
recommend changes to an AI solution based on performance data

Analyze solution requirements

Recommend Azure Cognitive Services APIs to meet business requirements

select the processing architecture for a solution
select the appropriate data processing technologies
select the appropriate AI models and services
identify components and technologies required to connect service endpoints
identify automation requirements

Map security requirements to tools, technologies, and processes

identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
identify which users and groups have access to information and interfaces
identify appropriate tools for a solution
identify auditing requirements

Select the software, services, and storage required to support a solution

identify appropriate services and tools for a solution
identify integration points with other Microsoft services
identify storage required to store logging, bot state data, and Azure Cognitive Services output

Design AI solutions

Design solutions that include one or more pipelines
- define an AI application workflow process
- design a strategy for ingest and egress data
- design the integration point between multiple workflows and pipelines
- design pipelines that use AI apps
- design pipelines that call Azure Machine Learning models
- select an AI solution that meet cost constraints

Design solutions that use Cognitive Services

design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs

Design solutions that implement the Microsoft Bot Framework

integrate bots and AI solutions
design bot services that use Language Understanding (LUIS)
design bots that integrate with channels
integrate bots with Azure app services and Azure Application Insights

Design the compute infrastructure to support a solution

identify whether to create a GPU, FPGA, or CPU-based solution
identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
select a compute solution that meets cost constraints

Design for data governance, compliance, integrity, and security

define how users and applications will authenticate to AI services
design a content moderation strategy for data usage within an AI solution
ensure that data adheres to compliance requirements defined by your organization
ensure appropriate governance of data
design strategies to ensure that the solution meets data privacy regulations and industry standards

Implement and monitor AI solutions

Implement an AI workflow
- develop AI pipelines
- manage the flow of data through the solution components
- implement data logging processes
- define and construct interfaces for custom AI services
- create solution endpoints
- develop streaming solutions

Integrate AI services and solution components

configure prerequisite components and input datasets to allow the consumption of
Azure Cognitive Services APIs
configure integration with Azure Cognitive Services
configure prerequisite components to allow connectivity to the Microsoft Bot Framework
implement Azure Search in a solution

Monitor and evaluate the AI environment

identify the differences between KPIs, reported metrics, and root causes of the differences
identify the differences between expected and actual workflow throughput
maintain an AI solution for continuous improvement
monitor AI components for availability
recommend changes to an AI solution based on performance data

FAQ’s

(more…)

Workday PECI Training – PECI Certification

⏰8 hours | ▶️ 11 Videos | 📣 8992 Participants | 🎓 5826 Reviews | 4.9 ⭐⭐⭐⭐⭐

Choose a Plan that Works for You

Self Paced

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Advanced sessions
Interview Q&A
Free study Materials
Premium Technical support

Instructor Led Live Training

✉️

Live Instructor
Advanced sessions
Interview Q&A
Premium Technical Support

Corporate Training

✉️

Live Instructor
Advanced sessions
Interview Q&A
Premium Technical Support

Upcoming Batches PST

Weekday

Oct 07(1 HR A DAY)

07:30 PM PST

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07:30 AM PST

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Oct 08 (1 HR A DAY)

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Course Description

Workday Payroll Effective Change Interface (PECI) provides employers an effective solution for quickly managing and implementing payroll changes. Employers can utilise PECI to update the payroll details of multiple workers without manually editing each record individually.

Workday PECI online courses provide comprehensive training experiences for professionals looking to hone their abilities in this area so that all modifications are consistently applied on time, saving time and reducing potential errors.

PECI is a data management interface designed to allow businesses to generate and oversee payroll modification requests in an organised, safe, and compliant fashion. Modification requests could involve adjustments in employee pay, deductions from benefits plans, or tax withholdings, among other things.

Staff with access to Workday can easily make, evaluate, authorise and deploy change requests within its system. Professionals completing Workday PECI training will have the skills to modify payroll accurately across varying workflows.

PECI excels at overseeing large-scale payroll data changes. Organisations looking to make changes across many employees at once may find this ability especially advantageous.

PECI allows businesses to use its software to ensure employees in one job grade are handled equally and consistently while saving time and limiting mistakes. For instance, an organisation could utilise PECI to generate change requests that apply across job grades when raising salaries by five per cent, thus guaranteeing every employee is treated the same while saving time and potential mistakes.

Registering for Workday PECI online training, you can ensure consistency throughout payroll procedures while learning how to implement changes effectively.

PECI provides organisations with another significant benefit by overseeing the authorisation and execution of payroll modifications. By creating an open and systematic process for examining requests for alterations to payroll processes, PECI helps create a transparent approach to dealing with such requests.

PECI provides thorough audit trails that enable businesses to monitor all payroll modifications for accountability and transparency, providing businesses with peace of mind when considering changes.

Workday PECI online classes provide insight into designing workflows to maximise efficiency and provide information about optimising them to increase productivity.

Organisations using PECI must draft a payroll modification request, noting which employees the change will impact and providing details such as revised deduction amounts or compensation rates.

Once a change request has been submitted and reviewed by staff members for approval, implementation typically involves amending all relevant personnel records using batch procedures.

Workday PECI courses give students confidence when creating, reviewing and implementing payroll changes within Workday systems.

PECI stands out for adaptability; organisations can easily create individual change request templates to address their unique requirements.

PECI provides businesses with customised approval processes to assess every change carefully before acceptance or rejection. In addition, tools and services such as reports, alerts, and notifications help track payroll changes and manage them more efficiently.

Enrol in Workday PECI’s online training session to utilise its capabilities to ensure system integrity and speed up payroll processes.

Security is another vital aspect of PECI; organizations can utilize PECI to control who generates and approves payroll modification requests and who has access to payroll data.

As such, this makes it simpler to ensure the privacy of private payroll information is preserved while all modifications take place in an efficient and regulated fashion.

Enrolling in Workday PECI online training will teach you how to set up security procedures within Workday that protect payroll data while guaranteeing its adherence to rules and industry standards.

Workday PECI is an outstanding solution that assists businesses in quickly and efficiently handling payroll modifications.

Any organisation seeking to ensure its payroll data is correct, current, and compliant with applicable regulations should turn to PECI. It can facilitate mass changes while overseeing approval and implementation processes and offering comprehensive logs and audit trails.

PECI can assist your organisation with streamlining payroll procedures, minimising mistakes, and ensuring all employees are paid promptly and correctly, regardless of size or industry.

To succeed in this field, Workday PECI online classes provide essential knowledge and abilities to use this powerful instrument effectively.

Course Content

1. Workday Core Connector Worker

When to Use It (vs EIB/VICI) – Change Detection,
Top-of-Stack Logic & Practical Setup

2. Configure Integration Field Attributes and Overrides Launch Schedule Integration, CCW vs Global Worker

3. View Worker History And Create Integration System, Workday CCW

DT pipeline:
eligibility/ad-hoc runs, XSLT/XTT mapping, ATV validations, BP trigger, and
performance tuning.

4. Create Integration System, Workday PICOF (Payroll Interface) configuration

5. Create Integration System (WECI), VG vs PG/CCW vs Pick-off, Critical VG Configuration Attributes, Security Setup

6. View Integration System,Full Snapshot And Primary Run, Workday VG Connector: Configuration and Run Modes, Ad Hoc Run

7. Create a PECI And Thirt Party Payroll Errors, Discussion on configuring Payroll Gateway (PG) attributes, event-driven integration, third-party payroll connectors, and best practices for multi-country payroll implementations

8. Roll Up Entry Dated Changes, Return from Leave (Payroll effect only), Hire Data Checkpoint for 3rd-party

9. Cloud Connect overview, Integration Events,Period Status And Pay Group , Preview of Cloud Connect for Benefits (CCB)

10. Connector types & vendors (CCB vs. Benefit Cloud Connect)

11. Configuring Workday Benefit/Cloud Connect (TRIAD/COBRA) integrations-data delivery (SFTP/API), event triggers, setup steps, and vendor-defined output formats.

FAQ’s

(more…)

Workday Extend Training – The only Course you need

⏰15 hours | ▶️ 15 Videos | 📣9248 Participants | 🎓 6476 Reviews | 4.9 ⭐⭐⭐⭐⭐

Choose a Plan that Works for You

Self Paced

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Advanced sessions
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Free study Materials
Premium Technical support

Instructor Led Live Training

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Live Instructor
Advanced sessions
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Premium Technical Support

Corporate Training

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Live Instructor
Advanced sessions
Interview Q&A
Premium Technical Support

Upcoming Batches PST

Weekday

Oct 10(1 HR A DAY)

06:30 AM PST

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06:00 PM PST

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Weekend

Oct 26(1 HR A DAY)

07:30 AM PST

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Upcoming Batches IST

Weekday

Oct 10(1 HR A DAY)

07:00 PM IST

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Weekday

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06:30 AM IST

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08:00 PM IST

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Course Description

Workday Extend Training is an application process that makes us reach the unique business marketing strategies for customer solving criteria.

Have your great opportunity to grab an intelligent deal, an offer to join with us now in Cloudfoundation for your Classroom Course Learning.

It is software that consumes less time and less expensive than we spend on a data integration platform. So you can get notable upgrades in the technology to work on it.

Learn more on U.I.& the other object model in building the efficient means of solving business challenges.

Apps built on power means it is one source for Finance people’s data and one more source as Security gathers at one place and in the H.R. fields. It is a robust integrated development.

So now give your best to get through the Certification process anywhere.

Be the one in acquiring a great opportunity for your job by this program-based.

Course Content

Module 1: Introduction to Workday Extend

Overview of Workday Extend Platform
Use Cases & Benefits
Structure of Extend Applications
Introduction to PMD, AMD, and SMD

Module 2: Presentation Layer – PMD, AMD, SMD

PMD: Page configuration, layout controls, file structure
AMD: App-level settings and deployment
SMD: Service layer configuration
Demo: Walkthrough of a PMD file and rendering UI

Module 3: Endpoints & Authentication

Creating and using Endpoints
Types of Authentication: OAuth, Basic Auth
Secure endpoint configuration
Demo: Secure endpoint call from Extend App

Module 4: UI Widgets in Workday Extend

Common Widgets: TextInput, Dropdown, Button
Layout and conditional logic
Demo: Dynamic widget rendering

Module 5: Error Handling & Flow Variables

Exception handling in services and UI
Setting up and using Flow Variables
Best practices in state management
Demo: Pass state between pages using flow variables

Module 6: Query Params & Outbound Variables

Using query parameters in Extend
Outbound variables for data flow
Demo: Transfer user inputs between screens

Module 7: PMD Functions and Reusability

Creating and using PMD functions
Parameterization and logic
Demo: Conditional rendering using PMD functions

Module 8: Model Components – Security & Business Logic

Security Domain: Defining access
Business Objects: Designing and using
Task & Attachments management
Integration with Workday BPs
Demo: Creating and securing a custom BO

Module 9: Orchestration & Service Calls

Types of Orchestrations
Calling orchestrations from PMD
Data mapping and response handling
Demo: Orchestration call from UI

Module 10: Capstone Walkthrough & Q&A

Full App Review: PMD, Orchestration, BO
Live demo and discussion
Deployment tips & best practices
Open Q&A

Project: A simple app will be created and used throughout the course.

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PingFederate Admin Training The only IAM Course you need

⏰24 hours | ▶️ 24 Videos | 📣 49 Participants | 🔥11 Reviews

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Self Paced

✉️

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Free study Materials
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Live Instructor
Advanced sessions
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Premium Technical Support

Corporate Training

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Course Description

So you want to learn Ping Federate Training? Great job!

Do you know Ping Federate Training is the most trending Analytics course?

There are massive opportunities in Ping Federate Training as it leads the Analytics market.

Our Ping Federate Training course is a job oriented course ie at the end of the course you can
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Also the salaries in Ping Federate Training is very impressive (Indeed.com report)