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.
Deploying a Multimodal Chatbot with Gemini Pro and Streamlit on Google App Engine
Desarrolla un chatbot multimodal con Vertex AI, Gemini Pro y Streamlit, y despliégalo en Google App Engine para aplicaciones de aprendizaje automático.
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.
Vertex AI: Model Garden and Deploying LLaMA 3 8B to Inference Endpoint
Explore Vertex AI's Model Garden, deploy LLama3 8B to an Inference Endpoint, and consume it using Python SDK. Gain hands-on experience with advanced machine learning tools.
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.
Google App Engine: Deploy Machine Learning Applications in Google Cloud
Deploy machine learning applications on Google Cloud using App Engine. Learn to leverage cloud infrastructure for scalable ML solutions.
MLOps MLflow: Installing a MLflow Tracking Server in Docker Containers
Install and configure MLflow Tracking Server with MariaDB backend using Docker containers for efficient MLOps workflows.
MLOps MLflow: Time Series Forecasting with SkTime and Prophet
Explore time series forecasting using SkTime and Prophet libraries in MLflow, enhancing your MLOps skills with practical implementation.
MLOps MLflow: Deep Learning Frameworks - PyTorch and TensorFlow Integration
Explore integrating deep learning frameworks Pytorch and Tensorflow with MLflow for enhanced model management and tracking.
MLOps: Integrating HuggingFace Transformers with MLflow
Integrate HuggingFace Transformers with MLflow for efficient MLOps, enhancing model tracking and deployment in machine learning projects.
ML and MLOps with Databricks: LLMs and HuggingFace Transformers
Explore LLMs using HuggingFace Transformers for summarization, translation, sentiment analysis, and advanced prompting techniques on Databricks.
Neo4J Knowledge Graph: Topic Extraction with Langchain and Phi3 - Part 1
Explore topic extraction for knowledge graphs using Neo4J, Langchain, and Microsoft Phi3. Learn to enrich movie databases with themes from TMDB.org overviews.
Azure AI Studio and OpenAI Studio - Python SDK for Machine Learning and Data Science
Explore Azure AI and OpenAI Studio, learn Python SDK usage, and discover practical examples for machine learning and data science applications.
MLOps: Inference ONNX Model in Azure ML Managed Endpoint AKS
Deploy ONNX models to Azure ML Managed Endpoints (AKS) for efficient inference. Learn step-by-step process using Python SDK v2 to optimize machine learning operations.
CPU Inference with ViT ONNX Model in Azure ML Managed Endpoint - AKS
Deploy a Vision Transformer model for CPU inference on Azure Kubernetes Service using ONNX format and Azure ML SDK v2. Learn to create managed endpoints in Azure ML Studio.
MLOPS: Inference ViVIT ONNX Model in Azure ML Managed Endpoint (AKS)
Aprenda a implementar un modelo ViViT en ONNX para inferencia en CPU utilizando Azure Kubernetes Services y onnxruntime con Python SDK v2.
The Science of LLM Benchmarks: Methods, Metrics, and Meanings
Explore LLM benchmarks, evaluation metrics, and critical assessment techniques to understand model strengths and limitations in this comprehensive talk on AI performance evaluation.
Building Multi-Modal Search and RAG with Vector Databases
Explore open-source multimodal embeddings for cross-modal search and MM-RAG. Learn techniques for integrating diverse data types, enhancing decision-making with real-time processing and LLMs.