RiskSpotlight的动态

Best Practices for Data Labeling Project Management How can we achieve the Project highest q... https://lnkd.in/gCSnzWkQ #24x7offshoring #Data #guidelines #Project How can we achieve the Project highest quality in our AI/ML? The answer, many scientists believe, is high-quality?training data?.?But securing such high-quality work may not be so easy.?So the question is “What are the best practices for data labeling? One might think of?data labeling?as a tedious job that requires no strategic thinking.?Annotators simply process their ... Read more

Completing a Generative AI certification is a great accomplishment and adds value to your role as a Business Analyst in AI-focused projects. Here’s how it helps: ?? ??? ?? 1. Understanding AI: You’ll know how AI tools like chatbots or content generators work and where they can benefit the business. 2. Defining Requirements: You can clearly explain what is needed for AI projects to work successfully. 3. Finding New Uses: You can identify innovative ideas like personalized suggestions, automated content, or smarter customer support. 4. Improving Data: AI works best with good data. You can help check if the data is ready and suggest ways to improve it. 5. Quick Prototypes: AI tools let you create test versions or try out business ideas faster. 6. Simplifying for Others: You can make complicated AI ideas easy to understand for non-technical team members. 7. Ensuring Ethics: You can address concerns like fairness, privacy, and compliance with rules. 8. Focusing on Results: AI isn’t just cool tech—it should deliver real business value, and you’ll help make that happen. 9. Standing Out: Knowing Generative AI makes you a standout professional in your field. 10. Staying Updated: Since AI is always advancing, your certification keeps you ready for the future. ?? #GenerativeAI #Learning #AICertificate #AITool

When managing AI projects, understanding the difference between training data and test data is critical for creating robust and reliable models. Here’s a breakdown tailored for product managers, with actionable insights to prevent failure: Training Data: Building the Model Training data is the dataset used to teach the AI system. The model analyzes patterns, relationships, and features within this data to learn how to make predictions or decisions. ? Purpose: To help the model learn. ? Key Considerations for Success: 1. Quality Over Quantity: More data doesn’t always mean better results. Prioritize clean, relevant, and diverse data. 2. Bias Risks: Training data often reflects real-world biases. For example, if your dataset skews toward one demographic, the AI will too. Build processes to detect and mitigate bias early. 3. Business Alignment: Ensure training data mirrors the scenarios the AI will encounter in production. Misaligned data leads to inaccurate models. Test Data: Evaluating the Model Test data is a separate dataset used to evaluate how well the model performs on unseen data. This simulates real-world conditions to ensure the AI is ready for deployment. ? Purpose: To validate accuracy and generalization. ? Key Considerations for Success: 1. Separation is Essential: Never use training data as test data—this creates false confidence. 2. Edge Cases Matter: Include diverse and challenging examples in your test set to evaluate the model’s robustness. 3. Metrics-Driven Evaluation: Define what success looks like. Are you optimizing for accuracy, precision, recall, or something else? Why the Distinction Matters Using training data as test data is one of the most common mistakes in AI projects, leading to overfitting. Overfitting happens when the model performs well on known data but struggles with new, real-world scenarios. This directly contributes to AI project failures by producing unreliable models. Takeaways for AI Product Managers: ? Establish clear guidelines to separate training and test data during the data preparation phase. ? Collaborate with data scientists to set realistic expectations for model performance based on test results. ? Make testing iterative—use multiple test datasets over time to ensure the model evolves effectively. By recognizing the distinct roles of training and test data, AI product managers can build products that perform reliably and responsibly in real-world conditions. How do you handle training and test data in your projects? Let’s discuss! #AIProductManagement #DataQuality #EthicalAI #Agile

Best Practices for Data Labeling Project Management How can we achieve the Project highest q... https://lnkd.in/dD2tE47H #24x7offshoring #Data #guidelines #Project How can we achieve the Project highest quality in our AI/ML? The answer, many scientists believe, is high-quality?training data?.?But securing such high-quality work may not be so easy.?So the question is “What are the best practices for data labeling? One might think of?data labeling?as a tedious job that requires no strategic thinking.?Annotators simply process their ... Read more

Best Practices for Data Labeling Project Management How can we achieve the Project highest q... https://lnkd.in/gXhC-qCZ #24x7offshoring #Data #guidelines #Project How can we achieve the Project highest quality in our AI/ML? The answer, many scientists believe, is high-quality?training data?.?But securing such high-quality work may not be so easy.?So the question is “What are the best practices for data labeling? One might think of?data labeling?as a tedious job that requires no strategic thinking.?Annotators simply process their ... Read more

Prepare for 2025 with key skillsets like AI & Machine Learning, Data Analytics, Project Management, and Agile Methodology.? And if you are seeking a better career prospect or wish to further expand your career, you can check in with us. #HRSG #HRSGMiddleEast #CareerGrowth #Skills2025 #AI #DataAnalytics #ProjectManagement #ProfessionalDevelopment

MIT Researchers Released a Robust AI Governance Tool to Define, Audit, and Manage AI Risks https://lnkd.in/dABnQMgj Practical Solutions for AI Risk Management Unified Framework for AI Risks AI-related risks are a concern for policymakers, researchers, and the public. A unified framework is crucial for consistent terminology and clarity, enabling organizations to create thorough risk mitigation strategies and policymakers to enforce effective regulations. AI Risk Repository Researchers from MIT and the University of Queensland have developed an AI Risk Repository that compiles 777 risks from 43 taxonomies into an accessible online database. This resource offers a comprehensive framework to understand and manage the various risks posed by AI systems. Comprehensive AI Risk Database A comprehensive search was conducted to classify AI risks, resulting in an AI Risk Database with two taxonomies: Causal Taxonomy and Domain Taxonomy. This database aids policymakers, auditors, academics, and industry professionals in filtering and analyzing specific AI risks. Structured Foundation for AI Risk Mitigation The study offers detailed resources, including a website and database, to help understand and address AI-related risks. The AI Risk Database categorizes risks into high-level and mid-level taxonomies, aiding in targeted mitigation efforts. Value of AI Governance Tool Robust AI Governance Tool MIT Researchers have released a robust AI governance tool to define, audit, and manage AI risks. This tool provides a foundation for discussion, research, and policy development, aiding in targeted mitigation efforts. AI Integration for Business Advancement Discover how AI can redefine your way of work, identify automation opportunities, define KPIs, select an AI solution, and implement gradually. For AI KPI management advice and insights into leveraging AI, connect with us at [email protected] or stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom. AI for Sales Processes and Customer Engagement Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom #AIRiskManagement #AIGovernance #AIIntegration #AIforBusiness #AIforSales #productmanagement #ai #ainews #llm #ml #startup #innovation #uxproduct #artificialintelligence #machinelearning #technology #ux #datascience #deeplearning #tech #robotics #aimarketing #bigdata #computerscience #aibusiness #automation #aitransformation

Model registering and versioning are essential practices in the lifecycle of machine learning (ML) models. These processes ensure the organization, reproducibility, and effective deployment of models. Model registering involves cataloging models in a centralized repository. This allows data scientists and ML engineers to keep track of all models developed within an organization. Registered models are tagged with metadata, such as version, training data, and performance metrics. This information is crucial for tracking the evolution of models and ensuring transparency. Model versioning is the practice of maintaining multiple iterations of a model. As models are updated and improved, new versions are created. This is particularly important for continuous integration and continuous deployment (CI/CD) in ML workflows. Versioning helps in comparing different model performances and understanding the impact of changes. It also aids in rolling back to a previous version if a new model version fails. These practices are necessary to address several challenges in ML projects. They facilitate collaboration among team members by providing a clear history of model changes. This prevents redundant work and ensures consistency in the model development process. Additionally, regulatory requirements often mandate the tracking of model lineage for auditing purposes. Furthermore, model registering and versioning enhance the deployment process. They provide a reliable way to manage and deploy models in production environments. For instance, in A/B testing, different model versions can be deployed to evaluate their performance in real-world scenarios. This helps in selecting the best-performing model with confidence. Overall, these practices are vital for maintaining the integrity, reproducibility, and success of ML projects. #ModelRegistering #ModelVersioning #MachineLearning #AI #DataScience #MLWorkflow #CI_CD #ModelDeployment #Reproducibility #Collaboration #Transparency #PerformanceTracking #Auditing #ModelManagement #VersionControl #ModelCataloging #Innovation #DataDriven #Tech #Automation

What do you think are the three gaps that companies need to close to make AI useful? To make artificial intelligence (AI) truly useful within organizations, companies must address several critical gaps. Here are three key areas that need to be closed: 1. Skills Gap The rapid advancement of AI technologies has outpaced the current skill sets of many employees. A significant portion of the workforce lacks the necessary digital and AI-related skills, which hinders the effective implementation and utilization of AI solutions. Training and Development:?Organizations need to prioritize continuous learning and upskilling programs. Research indicates that 38% of employees may need retraining to adapt to new technologies within three years. Companies like Johnson & Johnson are already using AI to identify skills gaps and tailor training programs accordingly, focusing on skills that are future-ready such as data management and automation. Attracting Talent:?The demand for AI and data skills is high, making it challenging to attract and retain qualified talent. Companies must create an environment that fosters professional growth and offers competitive benefits to retain skilled workers. 2. Data Management and Quality Effective AI systems rely heavily on high-quality data. However, many organizations struggle with data-related challenges, including: Data Availability:?Companies often face difficulties in accessing the right datasets necessary for training AI models. Poor data quality or incomplete datasets can lead to ineffective AI solutions. Data Security and Infrastructure:?As AI applications require large volumes of data, organizations must ensure robust data management practices to protect sensitive information and optimize data storage solutions. This includes upgrading outdated infrastructure to support AI capabilities effectively. 3. Strategic Implementation and Roadmap Many organizations lack a clear strategy for AI adoption, which can lead to underwhelming results. Lack of Clear Roadmap:?Companies need to develop a comprehensive AI strategy that outlines specific use cases, expected outcomes, and the steps necessary to achieve these goals. A McKinsey survey highlighted that 39% of executives cited strategy and scaling issues as major obstacles to capturing AI’s value. Integration with Existing Systems:?Successfully integrating AI into existing business processes is crucial. Organizations often struggle with this integration due to legacy systems and a lack of understanding of how AI can enhance current operations. Establishing a clear plan for how AI will fit into the existing framework is essential for maximizing its potential. Addressing these three gaps—skills, data management, and strategic implementation—will significantly enhance the utility of AI in organizations, enabling them to leverage its full potential for innovation and efficiency.