Head AI Analytics: Virtual Reality and Augmented Reality engineering.



More Uses of the AI Analytics Toolkit:




• Evaluate AI Analytics: about it and learning solutions IT development center Product Engineering services digital services Cloud Services application Managed Services Data Analytics and AI services learning services.
  
  

• Orchestrate AI Analytics: present your AI team is focused on all aspects of 1) designing, prototyping and developing solutions (algorithms and architectures for object detection, classification etc.
  
  

• Formulate AI Analytics: Big Data, analytics, AI and Data Science, development and integration.
  
  

• Manage advanced AI based systems that interact with users, deliver information and that intake action on the users behalf.
  
  

• Systematize AI Analytics: partner with platform teams, Data Engineering, and Data Science teams to develop the tools and processes needed to build AI driven platforms.
  
  

• Guide AI Analytics: how csp should build out portfolios with Data Analytics / AI solutions that integrate with 5g, IoT and Edge Computing.
  
  

• Methodize AI Analytics: algorithmic complexity, Deep Learning Performance Analysis and profiling, Distributed Computing, ai accelerators, gpus.
  
  

• Bring to market AI powered Consulting Services that address use cases across predictive engagement, Self Service, orchestration and employee optimization.
  
  

• Ensure you establish; lead and support the delivery of a broad range of Data, Analytics, Advanced Analytics, Visualization, Data Science, RPA and AI related Strategy / Advisory engagements.
  
  

• Organize AI Analytics: Risk Assessments, cloud workgroup meetings, Contract Negotiation, develop standards and policy related to cloud, Machine Learning, AI and Big Data.
  
  

• Systematize AI Analytics: data, analytics and AI are central to how work and you have invested heavily in your data pipeline, your Machine Learning and your insight capabilities.
  
  

• Establish continual improvements and Productivity Improvements through effective use of AI Ops and Ops Automation solutions and techniques.
  
  

• Ensure you integrate; lead systems IT As A Service, Managed Services for servers, mainframe, storage as a service, leveraging analytics and AI in the Data Center.
  
  

• Guide AI Analytics: Risk Assessments, cloud workgroup meetings, Contract Negotiation, develop standards and policy related to cloud, Machine Learning, AI and Big Data.
  
  

• Collaborate with Data Engineers, AI Software Engineers, Data Analysts, and stakeholders to make effective use of Core Data assets and model deployment.
  
  

• Ensure you advanced AI based systems that interact with users, deliver information and that intake action on the users behalf.
  
  

• Ensure you keep on top of new developments in enablement tools and AI based Content Management Systems, like serving up content proactively in the context of a deal and auto tagging.
  
  

• Formulate AI Analytics: Robotic Process Automation virtual worker process builds, script generation utilizing AI Tool Sets.
  
  

• Interact directly with security professionals who use your software and AI systems on a daily basis to increase effectiveness and the quality of work.
  
  

• Collaborate with Data Analysts, Data Scientists, AI Software Engineers, and stakeholders to make effective use of Core Data assets.
  
  

• Develop AI Analytics: direct data and AI as an advise, influencer and consulting, leading Design Thinking, Strategic Roadmap, architectural vision and Thought Leadership.
  
  

• Lead AI Analytics: more recently, GPU Deep Learning ignited modern AI the next era of computing.
  
  

• Establish AI Analytics: by applying AI and Data Science, you help leading companies to prototype, refine, validate, and scale AI and analytics products and delivery models.
  
  

• Head AI Analytics: data, analytics and AI are central to how work and you have invested heavily in your data pipeline, your Machine Learning and your insight capabilities.
  
  

• Systematize AI Analytics: work using powerful servers, AI Software, web resources, data feeds, and Proprietary Trading systems.
  
  

• Oversee AI Analytics: partner with platform teams, Data Engineering, and Data Science teams to develop the tools and processes needed to build AI driven platforms.
  
  

• Ensure you cultivate; lead and support the delivery of a broad range of Data, Analytics, Advanced Analytics, Visualization, Data Science, RPA and AI related Strategy / Advisory engagements.
  
  

• Devise AI Analytics: Software Engineering Management, AI compiler.
  
  

• Initiate AI Analytics: Risk Assessments, cloud workgroup meetings, Contract Negotiation, develop standards and policy related to cloud, Machine Learning, AI and Big Data.
  
  

• Develop strategic vision of your clients goals for the cloud, reducing cost, improving insights or analytics platforms or infrastructure, or innovation through technologies like AI and Machine Learning.
  
  

• Ensure you accomplish; lead systems IT As A Service, Managed Services for servers, mainframe, storage, leveraging cognitive analytics and robotics.
  
  

• Communicate an honest interpretation of data to all stakeholders; support and facilitate Open Communication between all stakeholders.
  
  




 



Save time, empower your teams and effectively upgrade your processes with access to this practical AI Analytics Toolkit and guide. Address common challenges with best-practice templates, step-by-step Work Plans and maturity diagnostics for any AI Analytics related project.



Download the Toolkit and in Three Steps you will be guided from idea to implementation results.



The Toolkit contains the following practical and powerful enablers with new and updated AI Analytics specific requirements:



STEP 1: Get your bearings



Start with...




• The latest quick edition of the AI Analytics Self Assessment book in PDF containing 49 requirements to perform a quickscan, get an overview and share with stakeholders.




Organized in a Data Driven improvement cycle RDMAICS (Recognize, Define, Measure, Analyze, Improve, Control and Sustain), check the…




• Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation




Then find your goals...



STEP 2: Set concrete goals, tasks, dates and numbers you can track



Featuring 999 new and updated case-based questions, organized into seven core areas of Process Design, this Self-Assessment will help you identify areas in which AI Analytics improvements can be made.



Examples; 10 of the 999 standard requirements:




1. How do you promote understanding that opportunity for improvement is not criticism of the status quo, or the people who created the status quo?
   
   

2. Ask yourself: how would you do this work if you only had one staff member to do it?
   
   

3. Which AI Analytics data should be retained?
   
   

4. How do you manage AI Analytics Knowledge Management (KM)?
   
   

5. What knowledge or experience is required?
   
   

6. Is there a high likelihood that any recommendations will achieve their intended results?
   
   

7. What evidence is there and what is measured?
   
   

8. What process should you select for improvement?
   
   

9. How do you verify performance?
   
   

10. How are measurements made?
    
    




Complete the self assessment, on your own or with a team in a workshop setting. Use the workbook together with the self assessment requirements spreadsheet:




• The workbook is the latest in-depth complete edition of the AI Analytics book in PDF containing 994 requirements, which criteria correspond to the criteria in...




Your AI Analytics self-assessment dashboard which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next:




• The Self-Assessment Excel Dashboard; with the AI Analytics Self-Assessment and Scorecard you will develop a clear picture of which AI Analytics areas need attention, which requirements you should focus on and who will be responsible for them:
  
  


• Shows your organization instant insight in areas for improvement: Auto generates reports, radar chart for maturity assessment, insights per process and participant and bespoke, ready to use, RACI Matrix

• Gives you a professional Dashboard to guide and perform a thorough AI Analytics Self-Assessment

• Is secure: Ensures offline Data Protection of your Self-Assessment results

• Dynamically prioritized projects-ready RACI Matrix shows your organization exactly what to do next:





 



STEP 3: Implement, Track, follow up and revise strategy



The outcomes of STEP 2, the self assessment, are the inputs for STEP 3; Start and manage AI Analytics projects with the 62 implementation resources:




• 62 step-by-step AI Analytics Project Management Form Templates covering over 1500 AI Analytics project requirements and success criteria:




Examples; 10 of the check box criteria:




1. Cost Management Plan: Eac -estimate at completion, what is the total job expected to cost?
   
   

2. Activity Cost Estimates: In which phase of the Acquisition Process cycle does source qualifications reside?
   
   

3. Project Scope Statement: Will all AI Analytics project issues be unconditionally tracked through the Issue Resolution process?
   
   

4. Closing Process Group: Did the AI Analytics Project Team have enough people to execute the AI Analytics Project Plan?
   
   

5. Source Selection Criteria: What are the guidelines regarding award without considerations?
   
   

6. Scope Management Plan: Are Corrective Actions taken when actual results are substantially different from detailed AI Analytics Project Plan (variances)?
   
   

7. Initiating Process Group: During which stage of Risk planning are risks prioritized based on probability and impact?
   
   

8. Cost Management Plan: Is your organization certified as a supplier, wholesaler, regular dealer, or manufacturer of corresponding products/supplies?
   
   

9. Procurement Audit: Was a formal review of tenders received undertaken?
   
   

10. Activity Cost Estimates: What procedures are put in place regarding bidding and cost comparisons, if any?




 
Step-by-step and complete AI Analytics Project Management Forms and Templates including check box criteria and templates.



1.0 Initiating Process Group:




• 1.1 AI Analytics project Charter

• 1.2 Stakeholder Register

• 1.3 Stakeholder Analysis Matrix




2.0 Planning Process Group:




• 2.1 AI Analytics Project Management Plan

• 2.2 Scope Management Plan

• 2.3 Requirements Management Plan

• 2.4 Requirements Documentation

• 2.5 Requirements Traceability Matrix

• 2.6 AI Analytics project Scope Statement

• 2.7 Assumption and Constraint Log

• 2.8 Work Breakdown Structure

• 2.9 WBS Dictionary

• 2.10 Schedule Management Plan

• 2.11 Activity List

• 2.12 Activity Attributes

• 2.13 Milestone List

• 2.14 Network Diagram

• 2.15 Activity Resource Requirements

• 2.16 Resource Breakdown Structure

• 2.17 Activity Duration Estimates

• 2.18 Duration Estimating Worksheet

• 2.19 AI Analytics project Schedule

• 2.20 Cost Management Plan

• 2.21 Activity Cost Estimates

• 2.22 Cost Estimating Worksheet

• 2.23 Cost Baseline

• 2.24 Quality Management Plan

• 2.25 Quality Metrics

• 2.26 Process Improvement Plan

• 2.27 Responsibility Assignment Matrix

• 2.28 Roles and Responsibilities

• 2.29 Human Resource Management Plan

• 2.30 Communications Management Plan

• 2.31 Risk Management Plan

• 2.32 Risk Register

• 2.33 Probability and Impact Assessment

• 2.34 Probability and Impact Matrix

• 2.35 Risk Data Sheet

• 2.36 Procurement Management Plan

• 2.37 Source Selection Criteria

• 2.38 Stakeholder Management Plan

• 2.39 Change Management Plan




3.0 Executing Process Group:




• 3.1 Team Member Status Report

• 3.2 Change Request

• 3.3 Change Log

• 3.4 Decision Log

• 3.5 Quality Audit

• 3.6 Team Directory

• 3.7 Team Operating Agreement

• 3.8 Team Performance Assessment

• 3.9 Team Member Performance Assessment

• 3.10 Issue Log




4.0 Monitoring and Controlling Process Group:




• 4.1 AI Analytics project Performance Report

• 4.2 Variance Analysis

• 4.3 Earned Value Status

• 4.4 Risk Audit

• 4.5 Contractor Status Report

• 4.6 Formal Acceptance




5.0 Closing Process Group:




• 5.1 Procurement Audit

• 5.2 Contract Close-Out

• 5.3 AI Analytics project or Phase Close-Out

• 5.4 Lessons Learned




 



Results



With this Three Step process you will have all the tools you need for any AI Analytics project with this in-depth AI Analytics Toolkit.



In using the Toolkit you will be better able to:




• Diagnose AI Analytics projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices

• Implement evidence-based Best Practice strategies aligned with overall goals

• Integrate recent advances in AI Analytics and put Process Design strategies into practice according to Best Practice guidelines




Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role; In EVERY company, organization and department.



Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?'



This Toolkit empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make AI Analytics investments work better.



This AI Analytics All-Inclusive Toolkit enables You to be that person.



 



Includes lifetime updates



Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.