Joel Elias Rodriguez’s Post

Consume Generative AI Models You consume a generative AI model by sending a request to the endpoint {{deploymentUrl}}/chat/completions?api-version={{api-version}}. LLMs can carry out natural language-related tasks such as answering questions, summarizing text, and extracting information from a body of text. #AIcore #BTP #GenAI #GenerativeAIHub

Data Attribute Recommendation "model template" See this help to learn about the model templates in "Data Attribute Recommendation" a service of #AI Business Services in #BTP

Join our webinar on July 17th for an engaging discussion on how AI is reshaping industries and revolutionizing business processes. Visit here for more information and to register: https://okt.to/V8ZIsu

Join our webinar on July 17th for an engaging discussion on how AI is reshaping industries and revolutionizing business processes. Visit here for more information and to register: https://okt.to/GUItYe

Join our webinar on July 17th for an engaging discussion on how AI is reshaping industries and revolutionizing business processes. Visit here for more information and to register: https://okt.to/84zTU5

Join our webinar on July 17th for an engaging discussion on how AI is reshaping industries and revolutionizing business processes. Visit here for more information and to register: https://okt.to/bQfOFr

Join our webinar on July 17th for an engaging discussion on how AI is reshaping industries and revolutionizing business processes. Visit here for more information and to register: https://okt.to/ucoeL3

🚀 Choosing the Right Machine Learning Algorithm: Naive Bayes vs. Support Vector Machine (SVM) 🚀 Are you wondering which machine learning algorithm to use for your business scenario? Let’s explore the strengths and considerations of two popular algorithms: Naive Bayes and Support Vector Machine (SVM). Naive Bayes Classifier: a) Simplicity: Naive Bayes is straightforward and easy to implement, making it ideal for small datasets. b) Interpretability: It provides clear insights into results. c) Training Speed: Naive Bayes can be trained quickly. d) Sensitivity: However, it is sensitive to feature distribution and violations of the independence assumption. Support Vector Machine (SVM): a) Complexity: SVM is more complex and challenging to interpret. b) High-Dimensional Spaces: It excels in high-dimensional spaces and can capture complex relationships. c) Robustness: SVM is robust against overfitting. The Choice Depends on Your Context: Naive Bayes: Use it for small datasets and when interpretability matters. SVM: Opt for it in high-dimensional scenarios with complex relationships. Remember, there’s no one-size-fits-all solution. Evaluate both algorithms based on your specific use case, data, and business problem. 🤖💡 #MachineLearning #DataScience #NaiveBayes #SVM #AI #sapcommunity #sapea 🔗 Learn more: Naive Bayes Classifier: https://lnkd.in/g_GsKVpc SVM Overview: https://lnkd.in/gwKUuYzk Feel free to share your thoughts and experiences in the comments! 📝👇

#Relevant #Reliable #Responsible Just Ask is a natural language query interface used to query data. Powered by artificial intelligence, Just Ask enables you to search your data easily and efficiently using business terms you are familiar with. Simply ask your question using natural language, and Just Ask will instantly provide answers as simple charts and tables. https://lnkd.in/d9TStCgn

A few AI/ML bits from my side for SAP Folks - Supervised machine learning can be used for classification or regression tasks. When a machine learning model is employed to segregate data into various categories, it is referred to as classification. For instance, consider the scenario where we want to classify pictures into two categories, such as images of oranges or bananas; this is known as binary classification. In the SAP world, an example of classification involves assessing bank loan applications as either safe or risky. Beyond binary classification, we also have machine learning algorithms for multiclass classification and multilabel classification. Let’s explore these further: Multiclass Classification: In multiclass classification, the output has more than two categories. For instance, we might classify fruits into categories like apples, oranges, bananas, and pears. An SAP example of multiclass classification could be the classification of business objects into multiple categories. Multilabel Classification: In multilabel classification, a single picture can have multiple labels. For example, an image might be labeled as containing both apples and bananas. In SAP, multilabel classification is used for document classification. Interestingly, this SAP Help Documentation is showing that it has APIs to provide binary, multi class and multilabel classification services for documents. Link : https://lnkd.in/gJypRNCV For Binary Classification tasks, one of the algorithms that can be used is Support Vector Machine. SVM divides a high-dimensional or infinite-dimensional space into two parts by creating a hyperplane called the decision boundary. In a 2D space, this is akin to drawing a line to separate points in the X-Y plane into distinct areas. Support vectors are the points nearest to this decision boundary, forming the basis for creating the hyperplane. The hyperplane is designed to maximize the distance between the closest points of different classes. SVM classifiers are robust to outliers. SVM can also be used when the data is nonlinear #AI/ML #SAP #sapbusinessai #sitblr #sapcommunity