Course Introduction
Large Language Model Testing and Evaluation for Project Applications Intensive Course provides professionals with the technical expertise and practical methodologies required to assess the quality, reliability, safety, and business suitability of large language models (LLMs) used in project environments. As organizations increasingly adopt generative AI for project reporting, planning, forecasting, risk analysis, contract review, document summarization, and decision support, systematic testing becomes essential for determining whether AI-generated outputs are accurate, consistent, explainable, and fit for operational use. This course equips participants with structured evaluation techniques that connect model performance with real project requirements, organizational controls, and measurable business outcomes.
Project applications present distinctive evaluation challenges because AI outputs may influence budgets, schedules, contractual interpretations, risk assessments, resource allocation, stakeholder communications, and management decisions. A model that produces fluent answers may still misunderstand project constraints, overlook critical dependencies, fabricate supporting evidence, or provide inconsistent recommendations when presented with similar scenarios. Participants learn to identify these failure patterns and develop evaluation strategies that assess factual correctness, reasoning quality, contextual relevance, instruction adherence, numerical accuracy, completeness, and consistency across realistic project tasks.
The programme explores the complete LLM evaluation lifecycle, beginning with use-case definition and evaluation criteria, followed by test dataset development, benchmark design, test execution, metric selection, error analysis, and performance reporting. Participants examine traditional automated metrics, reference-based comparisons, model-based evaluation, expert review, pairwise preference testing, and task-specific acceptance criteria. They learn how to combine quantitative measurements with human judgment to produce meaningful evidence about model capabilities, limitations, and suitability for different project applications. The course also addresses evaluation dataset quality, representative sampling, data contamination, test leakage, reproducibility, and the importance of testing across different project types, industries, document formats, and levels of complexity.
A central component of the course focuses on evaluating LLM-powered applications rather than testing language models in isolation. Participants investigate prompt engineering, retrieval-augmented generation, tool calling, structured outputs, multi-step workflows, and agentic systems. They learn to assess retrieval relevance, answer faithfulness, citation correctness, context utilization, tool selection, workflow completion, and recovery from errors. Practical exercises demonstrate how to build repeatable test suites, automate regression testing, evaluate model and prompt changes, compare alternative models, and monitor performance after deployment. These techniques help organizations distinguish genuine capability improvements from changes that merely make responses sound more persuasive or polished.
Responsible AI evaluation is equally important when language models operate in environments involving confidential project records, commercial agreements, financial information, employee data, regulatory obligations, or safety-related decisions. Participants learn to test for hallucinations, prompt injection, sensitive-data disclosure, unauthorized information retrieval, harmful or discriminatory outputs, excessive confidence, and inappropriate reliance on unsupported assumptions. The course introduces risk-based testing, human oversight, audit trails, evaluation documentation, model limitations reporting, and release criteria that support accountable deployment. Because acceptable controls vary by industry, jurisdiction, and use case, participants also learn to align evaluation procedures with applicable organizational policies and external requirements.
By completing the Large Language Model Testing and Evaluation for Project Applications Intensive Course, participants will be able to establish practical evaluation frameworks for selecting, testing, improving, and monitoring LLM-powered project solutions. They will gain hands-on experience designing representative test cases, selecting evaluation metrics, benchmarking models, building automated test pipelines, investigating failures, and communicating results to technical teams and project decision-makers. The course supports organizations seeking to improve AI reliability, reduce operational risks, strengthen governance, and ensure that language-model applications deliver measurable value throughout the project lifecycle.
Duration
10 days
Who Should Attend
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AI engineers and machine learning professionals responsible for testing, benchmarking, and improving large language models and generative AI applications.
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Software developers building LLM-powered project management tools, document assistants, reporting applications, and decision-support systems.
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Quality assurance engineers and software testers seeking to implement systematic testing methodologies for probabilistic AI applications.
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Data scientists and evaluation specialists responsible for designing benchmarks, analyzing model outputs, and measuring AI performance.
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Project managers and PMO professionals implementing AI applications for planning, scheduling, forecasting, risk analysis, reporting, and governance.
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AI product managers and business analysts translating project requirements into measurable AI acceptance criteria and evaluation scenarios.
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MLOps, LLMOps, and DevOps professionals responsible for evaluation automation, release validation, monitoring, and model lifecycle management.
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Enterprise architects assessing LLM providers, model alternatives, application architectures, and AI solution readiness for organizational deployment.
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Responsible AI, risk, compliance, and governance professionals developing model evaluation controls and operational assurance procedures.
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Cybersecurity professionals assessing prompt injection, data leakage, unsafe tool use, and other security risks in language-model applications.
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Retrieval-augmented generation developers evaluating search relevance, source grounding, citation accuracy, and document-based question answering.
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Technical consultants delivering AI testing, model selection, application validation, and performance improvement services.
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Business intelligence and analytics professionals using LLMs for interpretation, reporting, summarization, and analytical decision support.
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Digital transformation leaders responsible for validating the business value, reliability, and operational readiness of generative AI investments.
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Engineering, construction, infrastructure, financial services, public-sector, and consulting professionals applying LLMs to complex project documentation and operational workflows.
Course Objectives
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Develop a comprehensive understanding of LLM testing and evaluation principles, including the differences between conventional software testing, statistical model evaluation, generative output assessment, and production monitoring.
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Translate project application requirements into measurable evaluation criteria covering factual accuracy, completeness, relevance, consistency, reasoning quality, instruction adherence, and business usefulness.
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Design representative test datasets, benchmark suites, test scenarios, and expected outcomes that reflect real project documents, workflows, user questions, and operational constraints.
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Apply automated evaluation metrics, reference-based comparisons, semantic similarity measures, model-based judging, expert review, and pairwise comparisons to assess language-model performance.
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Evaluate hallucination risks, unsupported claims, citation accuracy, numerical reliability, contextual understanding, and the model's ability to recognize uncertainty or insufficient evidence.
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Test LLM-powered project applications involving document analysis, report generation, risk identification, schedule interpretation, contract review, and project knowledge retrieval.
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Develop repeatable testing pipelines for prompts, models, retrieval components, tool calling, structured outputs, multi-step workflows, and agentic application behavior.
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Compare alternative LLMs, model configurations, prompting strategies, context-window settings, and deployment options using consistent benchmarks and task-specific performance requirements.
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Implement adversarial testing for prompt injection, sensitive-data disclosure, unauthorized information access, unsafe tool execution, biased outputs, and other application-specific vulnerabilities.
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Assess operational performance through latency, throughput, resource utilization, token consumption, cost per task, failure rates, and scalability under representative workloads.
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Establish regression testing, version control, evaluation reporting, production monitoring, feedback analysis, and release gates to maintain quality as models and applications evolve.
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Produce a practical LLM evaluation strategy that connects technical performance, risk controls, business outcomes, human oversight, and continuous improvement across the project lifecycle.
Comprehensive Course Outline
Module 1: Foundations of Large Language Model Testing and Evaluation
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Examine the architecture, capabilities, limitations, and probabilistic behavior of large language models and understand why conventional software testing alone is insufficient for evaluating generative applications.
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Distinguish model-level evaluation, prompt testing, application-level testing, end-to-end workflow validation, safety testing, and production performance monitoring.
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Identify project use cases involving document summarization, contract interpretation, risk assessment, schedule analysis, stakeholder communication, knowledge retrieval, and automated reporting.
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Define evaluation objectives, stakeholders, operational assumptions, acceptance criteria, and failure consequences for LLM applications deployed in different project environments.
Module 2: Evaluation Strategy, Requirements, and Acceptance Criteria
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Translate business requirements and user expectations into measurable quality dimensions, including correctness, relevance, completeness, consistency, explainability, usability, and task completion.
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Develop evaluation matrices that connect project use cases with test scenarios, expected behaviors, critical failure conditions, measurable indicators, and release requirements.
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Establish risk-based acceptance criteria that differentiate low-consequence productivity assistance from applications supporting financial, contractual, compliance, or safety-related decisions.
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Define evaluation scope, testing responsibilities, approval processes, evidence requirements, and escalation thresholds for technical teams, business owners, and project governance structures.
Module 3: Test Dataset Engineering and Benchmark Development
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Build representative evaluation datasets from project reports, requirements documents, contracts, risk registers, schedules, meeting minutes, technical specifications, and other approved information sources.
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Develop test cases containing realistic user questions, contextual information, expected answers, reference evidence, scoring rubrics, and explicitly defined failure conditions.
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Apply sampling strategies that cover common tasks, rare scenarios, ambiguous requests, conflicting information, edge cases, multilingual content, and different levels of task complexity.
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Control test leakage, duplication, contamination, labeling inconsistency, data imbalance, and benchmark drift while maintaining appropriate privacy, provenance, and dataset versioning.
Module 4: Core LLM Evaluation Metrics and Measurement Techniques
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Compare exact-match accuracy, precision, recall, F1 score, semantic similarity, reference-based text metrics, and task-specific scoring approaches for appropriate evaluation scenarios.
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Assess answer relevance, factual correctness, completeness, coherence, instruction adherence, readability, and consistency using structured scoring rubrics and repeatable test procedures.
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Examine the strengths and limitations of automated metrics when evaluating open-ended responses, technical explanations, summaries, analytical recommendations, and other generated text.
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Develop measurement plans that define metric interpretation, scoring thresholds, aggregation methods, uncertainty reporting, and the limitations of conclusions drawn from benchmark results.
Module 5: Human Evaluation and Model-as-a-Judge Methods
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Design expert review protocols for assessing generated responses against project requirements, authoritative documentation, technical standards, and predefined quality criteria.
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Apply pairwise preference testing, ranked comparisons, rubric-based scoring, blinded reviews, and adjudication procedures to improve consistency in human evaluations.
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Evaluate model-as-a-judge approaches for scalable assessment while examining judge bias, position bias, verbosity preference, model self-preference, and sensitivity to prompt wording.
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Calibrate automated judges against human-reviewed examples and establish agreement measurements, disagreement analysis, escalation rules, and ongoing evaluator quality controls.
Module 6: Hallucination, Faithfulness, and Factual Reliability Testing
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Develop tests that distinguish supported facts, reasonable inferences, unsupported claims, contradictions, fabricated references, and misleading certainty in generated project responses.
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Evaluate whether answers accurately reflect source documents, preserve important qualifications, represent numerical information correctly, and avoid introducing claims absent from the evidence.
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Test abstention and uncertainty behavior using incomplete records, ambiguous questions, contradictory sources, missing data, and requests that exceed the model's available knowledge.
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Conduct error analysis to identify hallucination patterns associated with prompts, context length, retrieval failures, model selection, numerical reasoning, and complex multi-document synthesis.
Module 7: Prompt Engineering and Application Behavior Testing
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Create test suites for system prompts, user instructions, few-shot examples, output constraints, role definitions, formatting rules, and context assembly strategies.
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Test robustness to paraphrased questions, spelling errors, ambiguous terminology, irrelevant context, instruction conflicts, long inputs, and unexpected document structures.
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Validate structured outputs such as JSON, tables, decision summaries, risk classifications, project status reports, and other application-specific response formats.
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Measure prompt sensitivity, response stability, instruction adherence, task completion, and failure recovery while controlling changes in model versions and generation settings.
Module 8: Retrieval-Augmented Generation Evaluation
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Evaluate document retrieval quality using precision at k, recall at k, mean reciprocal rank, normalized discounted cumulative gain, and other suitable information-retrieval metrics.
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Assess answer faithfulness, source attribution, citation correctness, context utilization, evidence completeness, and the relationship between retrieved passages and generated conclusions.
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Test document ingestion, chunking, embedding models, metadata filters, hybrid search, reranking, query rewriting, and retrieval configuration through controlled comparisons.
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Diagnose RAG failures involving outdated documents, conflicting versions, missing evidence, irrelevant passages, incorrect page references, access-control filtering, and incomplete multi-document context.
Module 9: Tool Calling, Multi-Step Workflows, and Agentic Evaluation
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Test the accuracy of tool selection, argument construction, API usage, response interpretation, and handoffs between language models and external project systems.
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Evaluate multi-step workflows for task completion, intermediate reasoning quality, dependency handling, recovery from tool failures, and consistency of final outputs with available evidence.
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Assess agentic behaviors such as iterative retrieval, planning, delegation, memory use, and stopping decisions against explicit task boundaries and operational constraints.
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Define bounded execution policies, tool permissions, maximum iterations, timeouts, retry limits, human approval checkpoints, and safeguards against unauthorized or unnecessary actions.
Module 10: Security, Safety, and Adversarial Testing
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Design adversarial test scenarios covering direct and indirect prompt injection, malicious document instructions, jailbreak attempts, and manipulation of retrieval or tool-use behavior.
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Assess sensitive-data leakage, unauthorized retrieval, cross-user information exposure, insecure output handling, secret disclosure, and misuse of connected project systems.
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Test robustness against misleading inputs, conflicting instructions, unsafe requests, biased content, harmful outputs, and attempts to bypass application safeguards.
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Document security findings, assign severity according to potential impact, validate mitigations through retesting, and define escalation procedures for unresolved high-risk issues.
Module 11: Numerical Reasoning and Project-Specific Task Evaluation
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Evaluate LLM performance on project calculations involving budget variances, schedule deviations, percentages, unit conversions, financial forecasts, resource utilization, and quantitative comparisons.
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Test the model's ability to interpret tables, extract values from project reports, reconcile figures across documents, and preserve units, dates, currencies, and numerical assumptions.
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Validate generated risk assessments, milestone summaries, change-impact explanations, and management recommendations against project data and approved calculation methods.
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Design workflows that use deterministic calculators, validated software functions, or authoritative data sources for calculations requiring precision beyond unaided language-model generation.
Module 12: Performance, Cost, and Scalability Testing
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Measure end-to-end latency, time to first token, throughput, concurrency, failure rates, timeout behavior, and resource utilization under realistic project workloads.
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Estimate token consumption, inference charges, embedding costs, retrieval infrastructure expenses, storage requirements, and the cost of evaluation and human review.
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Compare model configurations, prompt lengths, context windows, batching strategies, caching approaches, and retrieval settings to identify practical cost-performance trade-offs.
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Conduct load, stress, endurance, and resilience testing to assess system behavior during demand spikes, provider interruptions, rate limiting, dependency failures, and recovery events.
Module 13: Model Benchmarking, Selection, and Comparative Analysis
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Develop controlled benchmarks for comparing candidate LLMs using identical or appropriately normalized prompts, test datasets, evaluation rubrics, and operating conditions.
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Assess models across reasoning, summarization, document extraction, multilingual performance, instruction following, factual reliability, tool calling, and project-specific tasks.
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Examine trade-offs involving model capability, deployment flexibility, data-handling requirements, response speed, context length, infrastructure, and total cost of ownership.
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Produce comparative evaluation reports that explain observed differences, uncertainty, limitations, workload suitability, and the evidence supporting model-selection decisions.
Module 14: Automated Evaluation, Regression Testing, and CI/CD Integration
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Build repeatable evaluation pipelines that execute test datasets, collect model outputs, calculate metrics, validate citations, record failures, and generate structured reports.
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Integrate automated evaluation into development workflows so that changes to prompts, models, retrieval pipelines, tools, or application code trigger appropriate regression checks.
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Establish release gates, metric thresholds, critical-failure rules, benchmark versioning, test-result retention, and exception procedures for controlled application updates.
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Investigate regression causes using error classification, comparison reports, representative failed examples, and targeted tests before approving changes for operational deployment.
Module 15: Production Monitoring, Governance, and Continuous Improvement
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Design production monitoring systems that track response quality signals, retrieval failures, user feedback, latency, cost, tool errors, system availability, and safety incidents.
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Establish feedback collection, incident triage, root-cause analysis, evaluation dataset updates, corrective actions, and retesting procedures for recurring application problems.
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Maintain documentation covering model versions, evaluation datasets, metric definitions, test results, known limitations, risk assessments, approval decisions, and change history.
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Develop lifecycle governance practices for periodic reassessment, model replacement, prompt updates, operational drift, changing project requirements, and evolving security expectations.
Module 16: Capstone Project and LLM Evaluation Framework Implementation
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Select a realistic project application, define its users and operational context, and translate business requirements into a complete evaluation plan with measurable acceptance criteria.
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Build a representative test dataset and implement automated and human evaluation procedures for factual reliability, task performance, robustness, security, and operational efficiency.
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Execute comparative tests across candidate models or application configurations, analyze failures, quantify performance trade-offs, and document unresolved risks and limitations.
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Present an actionable evaluation framework containing benchmark results, regression procedures, production monitoring indicators, governance controls, release recommendations, and a continuous improvement roadmap.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
