Why Data Scientists Should Learn Software Engineering Principles
Learn essential software engineering principles for data scientists to improve code quality, modularity, and readability. Enhance project scalability and model deployment capabilities.
AI Operations Without Fundamental Engineering Discipline - MLOps Podcast #250
Explore the anti-AI hype trend and learn how to bridge the gap between management and tech for effective AI implementation. Gain insights on balancing engineering fundamentals with AI initiatives.
BigQuery as a Feature Store for AI/ML Applications
Discover how to transform BigQuery into an effective feature management system for AI/ML applications. Learn about designing feature tables, addressing challenges, and optimizing workflows without additional software.
Visualize - Bringing Structure to Unstructured Data - MLOps Podcast #258
Explore techniques for structuring and visualizing unstructured data in machine learning, from classification to Retrieval-Augmented Generation, using tools like UMAP and Renumics Spotlight to uncover patterns and evaluate model performance.
Document Summarization: Langchain Summarization Chains, Clustering and LLMs
Explore document summarization using Langchain, Hugging Face Transformers, and clustering techniques for efficient text analysis and insights.
Document Similarity: Langchain Sentence Transformers and Chroma
Explore document similarity using Langchain, HuggingFace Transformers, and Chromadb. Learn to create embeddings and implement vector storage for efficient text analysis and classification.
Installing and Using Ollama Framework for Data Science and Machine Learning
Explore Ollama framework installation, usage, and Python integration for generating, embedding, and chatting with AI models.
DALL-E and Stability.AI: Text-to-Image Models for Data Science and Machine Learning
Explore DALL-E and Stability.AI text-to-image models, learning to generate, edit, and save AI-created images using practical notebooks and hands-on examples.
Computer Vision: OCR Comparison - Tesseract vs PaddleOCR
Explore OCR techniques using Tesseract and alternatives, comparing offline solutions for computer vision and text recognition in data science and machine learning.
Azure AI Computer Vision OCR: Image and Document Intelligence Services
Aprende a utilizar Azure AI Services para OCR de imágenes y documentos con Computer Vision y Document Intelligence.
Google Vertex AI Python SDK and Gemini for Data Science and Machine Learning
Explore Google Vertex AI, SDK, and Gemini through practical demonstrations and hands-on coding examples for machine learning and data science applications.
Langchain Document Summarization Using Fine-Tuned T5 and Clustering
Aprenda a utilizar Langchain para resumir textos usando T5 ajustado e clustering, com demonstração prática e notebooks disponíveis.
Azure ML: Pipelines with Python SDK for Data Science and Machine Learning
Create ML pipelines with Azure Machine Learning Studio's Python SDK for efficient data science and machine learning workflows on Azure.
Azure ML: Creating and Using Pipelines with Batch Endpoints
Create and deploy a multi-component pipeline with Azure ML, including component registration, data management, and Batch Endpoint setup for efficient machine learning workflows.
Fine-Tuning Foundation Models for Document Classification with Azure ML - LLama2
Fine-tune LLM for document classification using Azure ML Studio's Python SDK. Learn to adapt Llama2, Mistral, or other models for text classification tasks.
Neo4J Graph Data Science: Centrality Algorithms
Explore centrality algorithms in Neo4J Graph Data Science Library for advanced network analysis and machine learning applications.
Neo4J GDS: Similarity Algorithms for Data Science and Machine Learning
Explore Neo4J GDS library's similarity algorithms for data science and machine learning, with practical demonstrations and code examples.
Neo4J GDS - Machine Learning Feature Engineering
Explore data pre-processing and feature engineering using Neo4J's Graph Data Science Library for enhanced machine learning workflows.
Neo4J GDS: Machine Learning Node Classification Pipelines
Crea pipelines de clasificación de nodos con Neo4J y Graph Data Science Library para aprender técnicas avanzadas de machine learning en grafos.
Neo4J as Vector Store with Langchain, OpenAI, and Wikipedia
Explore Neo4J as a vector store using Langchain, OpenAI embeddings, and Wikipedia data for advanced data science and machine learning applications.
Rapids Machine Learning Training and Feature Engineering in GPU with CuML and CuDF
Accelerate ML training and feature engineering using GPU-powered CuML and CuDF libraries for enhanced performance and efficiency.