Systematize AI Production: proactively engage in the identification / remediation of software issues as Code Quality, pattern mismatch, and security issues related to the code or solution/configuration.
More Uses of the AI Production Toolkit:
- Evaluate AI Production: AI Algorithms Engineering Management.
- Systematize AI Production: direct data and AI as an advise, influencer and consulting, leading Design Thinking, Strategic Roadmap, architectural vision and Thought Leadership.
- Guide AI Production: algorithmic complexity, Deep Learning Performance Analysis and profiling, Distributed Computing, AI accelerators, gpus.
- Lead AI Production: Machine Learning and AI (especially deep neural networks).
- Organize AI Production: Risk Assessments, cloud workgroup meetings, Contract Negotiation, develop standards and policy related to cloud, Machine Learning, AI and Big Data.
- Initiate AI Production: not to mention fully automated from the start, providing the most advanced solution leveraging your AI Machine Learning technology.
- Devise AI Production: Software Engineering Management, AI compiler.
- Be an advocate for and help to identify new Machine Learning and AI product opportunities for the business.
- Orchestrate AI Production: present your AI team is focused on all aspects of 1) designing, prototyping and developing solutions (algorithms and architectures for object detection, classification etc.
- 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.
- Ensure you advanced AI based systems that interact with users, deliver information and that intake action on the users behalf.
- 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.
- Ensure you motivate; lead and support the delivery of a broad range of Data, Analytics, Advanced Analytics, Visualization, Data Science, RPA and AI related Strategy / Advisory engagements.
- Supervise AI Production: algorithmic complexity, Deep Learning Performance Analysis and profiling, Distributed Computing, AI accelerators, gpus.
- Establish AI Production: AI or Artificial intelligence, Big Data, analytics, cloud and Data Center, collaboration, video, internet of everything, networking, security, service provider, Software Development, testing, wireless, mobility.
- 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.
- Formulate AI Production: Big Data, analytics, AI and Data Science, development and integration.
- Interact directly with security professionals who use your software and AI systems on a daily basis to increase effectiveness and the quality of work.
- Establish AI Production: by applying AI and Data Science, you help leading companies to prototype, refine, validate, and scale AI and analytics products and delivery models.
- Develop, influence, and execute the AI Strategy at the Imaging portfolio level in partnership with the segments.
- Organize AI Production: algorithmic complexity, Deep Learning Performance Analysis and profiling, Distributed Computing, ai accelerators, gpus.
- Collaborate with internal Reinforcement Learning and Engineering teams to integrate the latest AI technology Best Practices.
- Systematize AI Production: science teams to translate customer needs and AI outputs into impactful products.
- Evaluate AI Production: 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.
- Oversee AI Production: partner with platform teams, Data Engineering, and Data Science Teams to develop the tools and processes needed to build AI driven platforms.
- Guide AI Production: how csp should build out portfolios with Data Analytics / AI solutions that integrate with 5g, IoT and Edge Computing.
- Ensure you direct; lead the Design And Delivery of Data/ Business Intelligence/ AI and automation solutions advisory engagements involving strategy, roadmap and longer term CoE models (Operating models).
- Methodize AI Production: algorithmic complexity, Deep Learning Performance Analysis and profiling, Distributed Computing, ai accelerators, gpus.
- Assure your organization addresses aspects as Data Privacy and security, data ingestion and processing, Data Storage and compute, analytical and operational consumption, Data Modeling, Data Virtualization, Self Service data preparation and analytics, AI enablement, and API integrations.
- Develop AI Production: direct data and AI as an advise, influencer and consulting, leading Design Thinking, Strategic Roadmap, architectural vision and Thought Leadership.
- Ensure Quality Standards are being met throughout the entire Production Process and support no.
- Make sure that your enterprise participates in the identification, development, and communication of new standards and Best Practices as appropriate.
Save time, empower your teams and effectively upgrade your processes with access to this practical AI Production Toolkit and guide. Address common challenges with best-practice templates, step-by-step Work Plans and maturity diagnostics for any AI Production 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 Production specific requirements:
STEP 1: Get your bearings
Start with...
- The latest quick edition of the AI Production 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 Production improvements can be made.
Examples; 10 of the 999 standard requirements:
- What is the scope of the AI Production effort?
- Is there an opportunity to verify requirements?
- What are your Best Practices for minimizing AI Production project risk, while demonstrating incremental value and quick wins throughout the AI Production project lifecycle?
- Who will be in control?
- What are the processes for audit reporting and management?
- What process should you select for improvement?
- Who will be responsible for deciding whether AI Production goes ahead or not after the initial investigations?
- What is the problem and/or vulnerability?
- Have all basic functions of AI Production been defined?
- What is your organizations process which leads to recognition of value generation?
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 Production book in PDF containing 994 requirements, which criteria correspond to the criteria in...
Your AI Production 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 Production Self-Assessment and Scorecard you will develop a clear picture of which AI Production 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 Production 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 Production projects with the 62 implementation resources:
- 62 step-by-step AI Production Project Management Form Templates covering over 1500 AI Production project requirements and success criteria:
Examples; 10 of the check box criteria:
- Cost Management Plan: Eac -estimate at completion, what is the total job expected to cost?
- Activity Cost Estimates: In which phase of the Acquisition Process cycle does source qualifications reside?
- Project Scope Statement: Will all AI Production project issues be unconditionally tracked through the Issue Resolution process?
- Closing Process Group: Did the AI Production Project Team have enough people to execute the AI Production Project Plan?
- Source Selection Criteria: What are the guidelines regarding award without considerations?
- Scope Management Plan: Are Corrective Actions taken when actual results are substantially different from detailed AI Production Project Plan (variances)?
- Initiating Process Group: During which stage of Risk planning are risks prioritized based on probability and impact?
- Cost Management Plan: Is your organization certified as a supplier, wholesaler, regular dealer, or manufacturer of corresponding products/supplies?
- Procurement Audit: Was a formal review of tenders received undertaken?
- Activity Cost Estimates: What procedures are put in place regarding bidding and cost comparisons, if any?
Step-by-step and complete AI Production Project Management Forms and Templates including check box criteria and templates.
1.0 Initiating Process Group:
- 1.1 AI Production project Charter
- 1.2 Stakeholder Register
- 1.3 Stakeholder Analysis Matrix
2.0 Planning Process Group:
- 2.1 AI Production 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 Production 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 Production 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 Production 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 Production 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 Production project with this in-depth AI Production Toolkit.
In using the Toolkit you will be better able to:
- Diagnose AI Production 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 Production 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 Production investments work better.
This AI Production 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.