SQA Group’s Post

How many times you discovered incomplete, wrong, contradicting or untestable requirements? Did you estimate the impact on project delivery and on your own work?    Poorly specified functionality results in wasted development time, missed deadlines, and blown-up testing activities.    Can AI help to validate Business Requirements and Acceptance Criteria for accuracy, quality and enhanced testability?    Yes, it can. Come to hear Scott Aziz, CTO of AgileAILabs, talking about the methods of applying validations techniques using AI - to generate clean and concise specifications, and how it helps Agile software delivery, better testing and risk mitigation. Scott is joined by Peggy Knox, COO of AgileAI Labs - sharing practical tips from operations point of you. December 13th, 12:00-1:00 PM EST   Register for the free webinar: https://lnkd.in/eDNZZmxv Scott will show how his new developed product Spect2TestAI™ analyzes requirements (user stories, acceptance criteria), rates those existing requirements, and generates enhanced requirements suggestions in real time balanced against 32 different industry standard measures including: • INVEST • SMART • COMPLETENESS • REAL.   The result is improved clarity, testability, and impact across Stakeholder teams.    Learn how to: - Apply methods of validation to requirements, user stories, acceptance criteria using AI - Improve requirements and code testability. - Enhance automated tests maintenance - eliminate manual work from adjusting tests to ever-changing requirements.  #ai #aiadoption #aiadvancement #businessanalysis #businessautomation #agile #dod #devops #operationsmanagement

If you’re involved in SDLC you should attend and discover how to reduce costly errors beginning with your requirements using our Spec2TestAI platform

Agile planning is an iterative and collaborative approach to software development that emphasizes adaptability and responsiveness to change. To enhance this process, generative artificial intelligence (AI) is being integrated into agile planning methods to automate and facilitate decision-making. This allows for more accurate estimation of project timelines, identification of potential pitfalls, and optimization of resource allocation, ultimately resulting in faster and more efficient software development processes. #agility #projectmanagement  

🚀 Embarking on a Digital Odyssey: Crafting Tomorrow with AI Mastery! 🌐 ✨ Once upon a time in the realm of tech, a group of digital pioneers set sail on a transformative journey. 🚢 Their vessel? The boundless sea of AI and Digital Transformation. 🌊 As the winds of change swept through the landscape, they unfurled the sails of competence, anchored in the profound understanding of key realms. 🎯 Digital Planning and Design: The Navigator's Blueprint 🗺️ In the heart of their voyage, the crew encountered the mystical art of Digital Planning and Design. Here, the Navigator took the helm, steering with strategic insight. With the compass of foresight and the map of UX/UI design, they charted a course that intertwined technology with the dreams of the organization. Key skills, like their ability to anticipate market trends, were the stars that guided their way. 📊 Data Use and Governance: The Keepers of the Digital Oracle 📜 As the ship sailed deeper into uncharted waters, the Keepers of the Digital Oracle emerged. Masters of Data Use and Governance, they held the sacred scrolls of insights. Their expertise in data science and governance ensured the crew navigated the sea of information responsibly. Every decision became a thread in the tapestry of digital success, weaving data into a strategic asset. 💼 Digital Management and Execution: The Architects of Tomorrow's Fortresses 🏰 On the horizon of the digital horizon, the Architects of Tomorrow's Fortresses took center stage. With project management as their cornerstone, they orchestrated the seamless execution of digital initiatives. Agile methodologies were the secret passages through which they navigated the ever-changing landscape, while change management skills ensured the crew embraced transformation with grace. 🌐 The Competence Chronicles: Unlocking the Power Within 🔐 Strategic Thinking: A beacon that aligned digital initiatives with organizational goals. UX/UI Design: The artists who painted the canvas of user-centric solutions. Data Science: Sorcerers who turned data into the elixir of innovation. Governance Expertise: Guardians of ethical and compliant data use. Project Management: Architects of the digital fortresses, ensuring every stone served a purpose. Agile Methodologies: Navigators through the ever-changing tides of the digital sea. Change Management: Guides who led the crew through the transformative storms with agility and empathy. 🤝 Join the Crew: Forging Legends in the Digital Odyssey! 🚀🌐 As the saga unfolds, we invite fellow adventurers to join this digital odyssey with NEXTEC DIGITAL SOLUTIONS. Share your tales, your challenges, and the victories that define your journey. Together, let's craft tomorrow, fueled by the winds of innovation and the compass of competence! 🌟🚀 #DigitalOdyssey #AIAdventures #CraftingTomorrow #LeadershipTales Share the post with other digital transformation Enthusiats.

#datascience #machinelearning #mlops Sunny Savita 🚀 Just embarked on a deep dive into the realms of MLOps and Agile Methodology, and the insights are nothing short of transformative! MLOps, short for Machine Learning Operations, is a set of practices that aim to streamline and automate the end-to-end machine learning lifecycle. It involves collaboration between data scientists, who develop machine learning models, and operations teams, responsible for deploying and maintaining these models in production. MLOps helps address challenges such as model versioning, reproducibility, monitoring, and scaling. It ensures a smooth transition from experimentation to deployment and ongoing model maintenance. Learning about MLOps involves understanding tools and practices for: Version Control: Managing different versions of machine learning models and their associated code. Continuous Integration/Continuous Deployment (CI/CD): Automating the process of testing and deploying models to production seamlessly. Monitoring and Logging: Implementing systems to track model performance, detect issues, and log relevant information. Scalability: Ensuring that models can handle increased workloads and adapt to changing data distributions. Agile Methodology is a project management and product development approach that prioritizes flexibility, collaboration, and customer feedback. It encourages iterative development and delivery, allowing teams to respond quickly to changing requirements and deliver a minimum viable product (MVP) in short cycles. Agile is widely used in software development but has found applications in various industries. Key principles and practices of Agile include: Scrum: An Agile framework that divides work into time-boxed iterations called sprints, typically two to four weeks long. Kanban: A visual management method for defining, managing, and improving services that deliver knowledge work, such as software development. Cross-functional Teams: Teams composed of individuals with different skills and expertise, fostering collaboration and reducing silos. User Stories: Short, simple descriptions of a feature or functionality from an end user's perspective. By embracing Agile, teams can adapt to changing requirements, improve communication, and deliver value to customers more consistently.

If you're striving for increased efficiency, reduced costs, and faster time-to-market, artificial intelligence is on the path to becoming indispensable in the software delivery lifecycle. Recent research has shown that AI can cut the time needed to document code functionality by 45 to 50% and can reduce completion time for writing code by 35 to 45%. From automated testing to predictive project management, AI is ushering in a new era of development where speed, precision, and innovation go hand in hand. Click here to read Chief Engineer Walter McAdams' latest blog exploring 3 ways AI is transforming the SDLC ➡ https://lnkd.in/eEXYSY53

Has Agile been working? Has it been effectively utilized and embraced by DevOps? This report brings answers to these questions and more including the impact of AI. #agileprojectmanagement #ai #genai #agiletrends #projectmanagement #devops #devsecops

Will AI help move DevOps efforts from fragile to agile? There is speculation across the industry that AI can greatly accelerate not just code generation for software, but all the details that follow -- including specifications, documentation, testing, deployment, and more. Integrating AI with "all phases of the DevOps feedback loop -- plan, code review and development, build, test, deploy, monitor, measure -- increases collaboration in teams and positively improves results," Billy Dickerson points out. With planning, "AI can make the project management process more efficient by autogenerating requirements from user requests, detecting non-aligned timelines, and even identifying incomplete requirements." #AIML #wearesas #DevOps

"What are we learning from the past to prepare us for the future? Nvidia's new Blackwell chip has 208 billion transistors. In 1970 1GB of storage cost $300,000, today it is 4 cents. Microsoft and OpenAI are planning a billion dollar AI computer. What are we learning? 🤣 Technology advances are still growing exponentially. 🤣 Organizational transformations not so much. Initiatives such as digital transformations, big data, or agile development have disappointed many. 😀 That said, agile and agility initiatives have fared better, often much better. 😀 Organizations with an agility mindset dominate the world's most valuable companies. The RA initiative, Reimagining Agile: Back to Basics, Forward to the Future, prepares us for the future, by extracting the best from the past and pioneering our way into the future." Classic bait and switch, folks - don't fall for it! You don't need some fancy launch group to reimagine agile. Be wary of grandiose promises about transforming your organization. Focus on the fundamentals, stay nimble, and continuously improve rather than chasing after the latest overhyped trend or noise some consultants are trying to sell you. Real agility comes from within, and real AI learning starts from you! Go and spend time learning AI. Join us at aiagile.org the OG reimagine agile group :) Edited: Maybe I was a bit harsh calling the AIC con artists. Respectfully take it back. However doesn’t change the fact that one member was originally part of a group and they can take our ideas and present it to the rest as if they created it. The call out here is to address it and why we don’t need a launch group to reimagine agile.

Scrum and Daily standups won't cut it for a successful AI project. AI projects require a unique approach, distinct from traditional software development. What follows are the 6 stages of the AI/ML project lifecycle you need to focus on: 1. BUSINESS UNDERSTANDING: • Determine Business Objectives: Understand what the customer wants to accomplish from a business perspective. • Assess Situation: Identify resources, project requirements, and risks, and conduct a cost-benefit analysis. • Determine AI Model Goals: Define success from both a business and technical viewpoint. 2. DATA UNDERSTANDING: • Collect, Describe, EDA, and Verify Data Quality: This ongoing process should take about 15% of project time. CHECKPOINT: After the first two phases, a Data and Business Understanding Document was agreed upon by all parties, outlining objectives, success measures, timelines, risks, and resource allocation. Get a free template to follow: https://lnkd.in/e_78NzeQ 3. DATA PREPARATION: • Expect 50-80% of the Project Time Here: Includes data selection, cleaning, construction (e.g., inferring city names from zip codes), and formatting. Warning: Be cautious when filling in missing information, as it can impact reporting and model accuracy. • Create New Datasets: Combine different sources and construct data for comprehensive analysis. 4. MODELING: • Fun, but often only ~20% of project time. Select modeling techniques and architectures (e.g., LSTM, Convolutional Networks, Inception, ResNet50). • Iterative Development: Constantly refine the model by adjusting the data, aiming for the best performance. • Model Selection: Based on performance on test datasets or resampling techniques like k-fold cross-validation. • Statistical Measures: Consider Akaike and Bayesian Information Criteria for model complexity assessment. 5. EVALUATION: • Evaluate Results: Do models meet business criteria? NOTE: This is where we go back to the Data and Business Understanding document for validation Which is best for the business? • Review Process: Ensure all steps were executed correctly. • Determine Next Steps: Decide whether the model is ready for production or further development (MVP, Pilot phase). • Important Metrics: Translate the model performance (Accuracy, Precision, and Recall etc.) into the actual business performance metrics. Are you saving 2 out of 8 hours a day? If so, then the model performance could be 75% precision, and still be valuable. 6. DEPLOYMENT AND REPORTING: • Handover to Agile Teams: For API development and infrastructure deployments. • Monitoring and Maintenance: Plan for model drift (Model, Concept, Data, Feature), operational issues, feedback loop effects, and regulatory changes. For a more in-depth discussion, check out: https://lnkd.in/esWYMgYX #AIProjectManagement #DataScience #Modeling #AIChallenges #BusinessAlignment