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Ground Zero Agile Project

Ground Zero Agile Project   So you have chosen to do a project the Agile way and read the Agile Manifesto, what to do?  What is the plan?  So this article is a proposed "Ground Zero" project.   All Agile projects must be: 1) deployable to the target computer system to demo, 2) a manifest to make it deployable but flexible, 3) the developer team is ready to go with working (and proficient) with their programming language (if not, then we need to train them) and code generator or AI, 4) there is a given source control app governing CI/CD, 5) and that the user stories have been done for the first sprint.   All Agile projects for software vendors, need to have a common company app (for the company's eventual suite of products) that: 1) does admin piece (where first installer sets up the administrator user and some other users, 2) shows some license screen, 3) shows the common expected user interface layout (so team gets to practice with this.)    Suggestio...
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AI Exam 5 - AI Aspects

AI Practitioner Exam Prep -  Aspects of AI Cost Considerations   Cost factors are responsiveness and availability, redundancy and regional coverage, performance, token-based pricing, provisioned throughput, and refining your custom models. Responsible AI Aspects: controllability, explainability, fairness, governance, interpretability\transparency, privacy\security (theft and exposure risk), safety, and veracity\robustness.   Business Benefits: trust, regulatory complain, mitigate risks, competitive advantage, improved decision making, and improved products.    Model Selection: a)  Narrow the use case to tune your model to your use case.  Ex: favor recall or precision , b) pick by performance with some test data sets,  c) responsible agency, d) environmental reasons, e) economical reasons.   Dataset Prep: want  balanced dataset  so  inclusive and diverse  in data collection, curating by 1) preprocessing, 2) augmentat...
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AI Exam 4 - Products

AI Practitioner Exam Prep -  Products Abbreviations KMS  =  Amazon   Key Mgmt Service  product Developer Products (in ML frameworks layer) SageMaker   AI  =  IDE  plugin that is  fully managed service that automates ML lifecycle ( from data prep to production) with " no-code" environment and handles infrastructure to streamline building, tuning, and deploying models.  Capabilities: Predictive analytics, computer vision, NLP, and fraud detection. Operations: Features auto-training, integrated logging, and CloudWatch monitoring. Security & Data: Secured via IAM ; integrates with S3 (storage), Lambda (triggers), and API Gateway (endpoints) .     Auto Model Tuning feature exists on Sagemaker.     Auto Pilot = uses Clarify to show how ML models could make  predictions. uses  SHAP values. Auto finds the  best hyperparameters.     Canvas =  No-code ML tool to create predictions...
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AI Exam 3B - Data and Learning

Abbreviations IDP = Intelligent Data Processing, Data Terms Classification  =  SL.  groups data into known  labeled  groups .    ex1: data =  car pictures  labeled by maker and model, ex2: customer  sentiment  grouping.  Types are  binary classification  and  multi-class classification. Clustering  =  UL. groups data with no labels into previously unknown groups . Curating  =  structures the data for processing prior to learning . See Labeling. De-identification  =  removing PII such as social security Encoding  =  converts from non-numeric to numeric . Governance  =  managing, securing, and monitoring data throughout its lifecycle IDP  =  extracts and classifies unstructured data in docs. gives  summaries and  actionable insights. Labeling  =  id and tags with content labels of each piece thus classifying . See Curating. Multi-...
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AI Exam 3A - Abbrev and Terms

AI Practitioner Exam Prep - Terms Abbreviations ART prompting = Automatic Reasoning and Tool-use, ATLAS = Adversarial Threat Landscape for AI Systems, BERT metric = Bidirectional Encoder Representations from Transformers, BLEU metric = Bilingual Evaluation Understudy,  CNN = Convolutional Neural Networks ,  CoT prompting = Chain-Of-Thought,  GANs = Generative Adversarial Networks, GLUE benchmark = General Language Understanding Evaluation, GPT = Generative Pre-trained Transformers, FM = Foundational Models , L = Labeled Data,  LLM = Large Language Models ,  METEOR metric = Metric for Evaluation of Translation with Explicit ORdering , NL or NLP = Natural Language Processing ,  NTM = Neural Topic Modeling ,  PDP = Partial dependence plots ,  PRA = Privacy Reference Architecture , RAG = Retrieval-Augmented Generation , RL = Reinforcement Learning , RLHF = Reinforcement Learning from Human Feedback , ROUGE metric = Recall-Orient...
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AI Exam 2 - Layers

AI Practitioner Exam Prep Layers Overall AI Abbreviations AI   =   Artificial Intelligence,  DL  =  Deep Learning,  GenAI  =  Generative AI,  L = Labeled data,  ML  =  Machine Learning,  SL = Supervised Learning,  SSL  =  S elf-Supervised Learning,  U =  Unlabeled data,  UL = Unsupervised Learning Mathematical Subsets/ Layers  of AI Four Concentric Circles of: AI = broadest field (with rule engines). computers appear as human behavior or reasoning.   ML = computer makes predictions or decisions without coding every rule     Neural Nets = technical family of learned models inspired by human brain     DL =  ML that uses multi-layer neural networks.       Gen AI =  generates content.  runs on pre-trained FMs that can run multiple tasks.  ML     ML model  =  is trained and makes predictions or decisions. most ...
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