Smart Tech LLC的动态

?? ???????????????????? ?????????????????? ?????? ????: ?? Hello everyone! ?? Ready to optimize your operations with the power of Artificial Intelligence??Machine Learning (ML), Deep Learning (DL) and AI are revolutionizing industries, and operational excellence is the key. What is all this? ?? In essence, ML allows machines to learn from data without being explicitly programmed. DL, a subset of ML, uses deep neural networks for more complex tasks. AI, in general, seeks to emulate human intelligence. Its application in industry It is crucial for decision making, automation and prediction. Benefits of AI for Operational Excellence: ?? Task automation: Free your team for higher value tasks. ?? Trend prediction: Anticipate problems and opportunities. ?? Resource optimization: Maximize efficiency and reduce costs. Expected Results: ?? Increased productivity. ?? Reduction of errors. ?? Cost savings. Areas of Improvement with AI: ?? Production processes. ?? Supply chain management. ?? Data analysis. Key KPIs for Success: Model precision (P): P = (Correct / Total) 100 Error rate (E): E = (Errors / Total) 100 Training time (T): Time in seconds/minutes. AI models: Linear regression: A simple model for predicting continuous values. Neural networks: Complex models for classification and prediction tasks. Support Vector Machines (SVM): Robust models for classification and regression. OKRs for AI: Objective: Improve inventory management efficiency by 20%. Key result: Reduce order waiting time by 15%. Metrics: Number of orders processed per hour. Methodologies and Tools: Agile Methodology: For rapid iterations and adaptation to changes. Tools: Python, R, TensorFlow, PyTorch, etc. Tools: Cloud Computing Platforms: AWS, Azure, GCP. Data visualization tools: Tableau, Power BI. Simple example of a model (Linear Regression): Imagine you want to predict the price of a house. A linear regression model looks for the relationship between the size of the house and its price. If a larger house tends to have a higher price, the model will learn that relationship and be able to predict the price. price of a new house based on its size. Softwares and Systems: Google Cloud Platform (GCP) Amazon Web Services (AWS) Microsoft Azure #AI #MachineLearning #DeepLearning #OperationalExcellence #Technology #Innovation #BigData #Artificial Intelligence #Automation #Optimization

AI - A product that needs no marketing. Sure! Here are some of the top AI tools and how they work: **1. Google Cloud AI Platform:** Offers a suite of machine learning services to help build and deploy ML models. It provides tools for data preprocessing, model training, hyperparameter tuning, and model serving. Users train models with TensorFlow, Scikit. The platform also offers AutoML services for custom model training without extensive machine learning expertise. ???? #GoogleCloudAI #MachineLearning **2. IBM Watson:** An AI platform that enables businesses to build and deploy AI-powered applications. It offers a range of services such as natural language processing, image recognition, and predictive analytics. Watson uses deep learning algorithms to understand and process large amounts of unstructured data to extract insights and patterns. It also provides APIs for developers to integrate AI capabilities into their applications. ???? #IBMWatson #AIapplications **3. Microsoft Azure Cognitive Services:** A collection of AI services that enable developers to add various cognitive capabilities to applications without needing in-depth AI expertise. These services include vision, speech, language, and decision APIs that can analyze and interpret data using advanced machine learning algorithms. Developers can easily integrate these services into their applications via REST APIs. ???? #AzureCognitiveServices #AIintegration **4. TensorFlow:* TensorFlow is an open-source machine learning library developed by Google for building and training deep learning models. It provides a flexible framework for numerical computations and supports various neural network architectures. TensorFlow allows users to define, train, and deploy machine learning models efficiently. It also offers tools like TensorBoard for visualizing model performance and debugging. ???? #TensorFlow #DeepLearning Each of these AI tools plays a crucial role in enabling businesses and developers to leverage the power of artificial intelligence for various applications and use cases.

?? ????! ?? Artificial intelligence (AI), machine learning (ML) and deep learning (DL) are revolutionizing the industry.?These technological advances make it possible to automate tasks, predict results and improve operational efficiency in all sectors.??? Why are they important? These fields allow large volumes of data to be analyzed to identify patterns, trends and opportunities for improvement that were previously impossible to detect.??? 5 Benefits of AI in Operational Excellence: 1. Automation: Reduces human errors and speeds up processes. ?? 2. Prediction: Anticipate problems and optimize resources. ?? 3. Personalization: Offer tailored experiences for customers and employees. ?? 4. Scalability: Adapt solutions to different business sizes. ?? 5. Predictive Analysis: Identify trends and make strategic decisions. ?? Expected Results: Increased productivity. Cost reduction. Quality improvement. Greater customer satisfaction. Data-driven innovation. Areas of Improvement with AI: Inventory management. Demand forecast. Fraud detection. Supply chain optimization. Customer service. Key KPIs for Success: Accuracy: % of correct predictions. Response time: Data processing speed. Cost per transaction: Resource optimization. Return on investment (ROI): Measurement of financial impact. Customer satisfaction: Evaluation of the experience. 5 AI Models: 1. Linear regression. 2. Neural networks. 3. Support vector machines. 4. Decision trees. 5. KNearest Neighbors. 5 OKRs for AI: 1. Increase operational efficiency by 15% in 6 months. 2. Reduce operating costs by 10% in 1 year. 3. Improve customer satisfaction by 20% in 12 months. 4. Implement 3 new AI models in 2024. 5. Increase productivity by 10% in 2024. Methodologies and Tools: Agile, DevOps: Agile methodologies for implementation. Tools: Python, R, TensorFlow, PyTorch, AWS, Azure, GCP. Simple example of a model (Decision Tree): Imagine a tree with branches that represent different options.?Each branch leads to another decision until a conclusion is reached.?Like a map to make decisions! ??? Softwares and Systems: Google Cloud Platform (GCP) Amazon Web Services (AWS) Microsoft Azure IBM Watson #AI #MachineLearning #DeepLearning #OperationalExcellence #DigitalTransformation #Technology #Innovation #BigData #Artificial Intelligence #Opportunities #Efficiency #Automation #Data

In AI, shift-left still means garbage-in-garbage-out

Businesses are getting serious about AI implementation. In many cases, the digital transformations and cloud adoptions of the previous years are beginning to pay off. That being said, there are numerous hardware restrictions when it comes to deep learning algorithms executing millions of operations related to even a single instance of a given dataset. To strategically prioritize growth, optimization, and reliable operation, leaders are choosing to put Data Center Engineering efforts at the center of corporate innovation. #Leadership #BusinessGrowth #AI

FedFixer: A Machine Learning Algorithm with the Dual Model Structure to Mitigate the Impact of Heterogeneous Noisy Label Samples in Federated Learning https://lnkd.in/dKUTX2cc Introducing FedFixer: A Practical AI Solution for Federated Learning In today's world, data privacy is crucial. Federated Learning offers a solution for companies to collaborate on training machine learning models without sharing sensitive data. However, this approach can face challenges when dealing with imperfect data labels, leading to inaccurate predictions. FedFixer is an innovative algorithm that addresses this issue by using a dual model structure - a global model and personalized model. This approach helps in adapting to unique data characteristics and noisy label patterns, improving the model's ability to generalize and make accurate predictions. To avoid overfitting, FedFixer integrates two key regularization techniques - confidence regularizer and distance regularizer. These techniques help in reducing the impact of noisy label samples and preventing overfitting to local noisy data, ensuring more reliable predictions. The practical applications of FedFixer have been extensively validated, showing potential in critical domains such as healthcare. By using FedFixer, companies can mitigate the impact of heterogeneous noisy label samples in Federated Learning, ensuring more accurate and reliable predictions. For companies looking to leverage AI solutions, consider implementing FedFixer to address issues related to label noise in Federated Learning. Additionally, explore the practical AI solution of using an AI Sales Bot to automate customer engagement and manage interactions across all customer journey stages. To stay updated on leveraging AI effectively and for AI KPI management advice, connect with us at [email protected] or follow our Telegram and Twitter channels for continuous insights into leveraging AI. Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - MarkTechPost - Twitter – @itinaicom #ai #ainews #llm #ml #startup #innovation #itinai #uxproduct

I'm thrilled to share that I've successfully completed Level 1 of the AI Odyssey Challenge! ?? Through this journey, I've gained valuable AI applied skills and learned how to leverage Azure AI services to develop innovative solutions. ?? Specifically, I've explored: 1.Create computer vision solutions with Azure AI Vision_N 2.Develop solutions with Azure AI Document Intelligence_N 3.Develop NLP solutions with Azure AI_N This experience has not only expanded my knowledge but also equipped me with practical skills to address real-world challenges in AI development. A huge shoutout to the #AIChallenge and #MSLearn communities for their support and resources throughout this journey. ?? Excited to continue advancing in my AI journey and exploring new opportunities with Microsoft. Let’s keep growing together! ??Microsoft Learn. #aichallenge #ai #azureai #artificialintelligence #skillsdevelopment #innovation #techlearning #microsoft #challenge #mslearn #aichallenge #azure #ms

?? Day 26 of 100 in my Data Analyst journey! ?? Excited to share that I've earned the **Google AI Essentials Certificate**! ???? In this course, I gained valuable skills in the AI space: ?? **Generative AI Tools**: Leveraging AI for idea development, content creation, and streamlining daily tasks. ?? **Prompting Techniques**: Writing clear, specific prompts to generate precise outputs, like summaries and taglines. ?? **Responsible AI**: Learning how to identify AI biases and ensure ethical AI usage. ?? **Staying Ahead**: Developing strategies to keep up with the ever-evolving world of AI. This is just the beginning—I'm excited to continue exploring the power of AI! ?? #DataAnalyst #GoogleAI #AICertificate #GenerativeAI #PromptEngineering #ResponsibleAI #LearningJourney #100DaysOfLearning #CareerGrowth #AIinData #StayCurious #SkillCircle ?? Follow me for more updates and insights!!

?? Optimize your Operation with AI! ?? Artificial intelligence (AI), machine learning (ML) and deep learning (DL) are revolutionizing the industry.?These technological advances allow us to automate tasks, predict results and improve operational efficiency.??? Why are they important??Because they help companies make better decisions, reduce costs and increase productivity.??? 5 Benefits of AI in Operational Excellence: 1. Automation: Reduces human errors and speeds up processes. ?? 2. Prediction: Anticipate problems and optimize resources. ?? 3. Data Analysis: Extract valuable insights for decision making. ?? 4. Personalization: Offer tailored experiences for customers and employees. ?? 5. Scalability: Adapt solutions to different sizes and needs. ?? Expected Results: Increased productivity. Cost reduction. Quality improvement. Greater customer satisfaction. Making more informed decisions. Areas of Improvement with AI: Production processes. Inventory management. Customer service. Marketing and sales. Product development. Key KPIs to Measure Success: Precision: % of correctness of the model. Accuracy: % of correct predictions. Response time: Processing speed. Cost per transaction: Cost optimization. Return on investment (ROI): Measurement of the benefit obtained. 5 AI Models: 1. Linear regression. 2. Neural networks. 3. Support vector machines. 4. Decision trees. 5. KNearest Neighbors. 5 OKRs for AI: 1. Increase efficiency by 20% in 6 months. 2. Reduce errors by 15% in 3 months. 3. Improve customer satisfaction by 10% in 12 months. 4. Implement 3 new AI models in 6 months. 5. Increase ROI by 10% in 12 months. Methodologies and Tools: Agile: Scrum, Kanban. Tools: Python, R, TensorFlow, PyTorch, AWS, Azure, GCP. Simple example of an AI model (Decision Tree): Imagine a tree with branches that represent questions.?Each question takes you to another branch until you reach a final answer.?For example, is the customer new? Yes, so... you bought product A? Yes, then... Congratulations! Softwares and Systems: Google Cloud Platform (GCP) Amazon Web Services (AWS) Microsoft Azure IBM Watson Tableau #AI #MachineLearning #DeepLearning #OperationalExcellence #DigitalTransformation #Technology #Innovation #Business

“A chain is only as strong as its weakest link” Your AI is only as good as your ML…your ML is only as good as your data! Building and improving your model is important just as making sure it’s accurate and stable. In the blog post learn tips on making your model accurate and stable to ensure your model will perform better even on unseen data. https://lnkd.in/gsQdgjfS #ai #artificialintelligence #ml #data