Model Experiments Tracking and Registration Using MLflow on Databricks
Automate ML model experimentation and tracking using MLflow on Databricks. Learn to streamline data preparation, model development, and artifact management for efficient machine learning workflows.
Frequently Bought Together Recommendations Using Embeddings - Challenges and Solutions
Explore embedding-based "frequently bought together" recommendations for e-commerce, covering model training, productionization, metrics, and serving strategies. Learn practical tips for large-scale implementation.
Cohere vs. OpenAI Embeddings - Multilingual Search
Explore multilingual semantic search using Cohere's new model, comparing it with OpenAI's GPT 3.5. Learn implementation, data preparation, vector indexing, and query techniques for enhanced search capabilities.
LangChain Data Loaders, Tokenizers, Chunking, and Datasets - Data Prep
Learn to prepare text data for LLMs using LangChain tools: data loading, tokenization, chunking, and dataset creation. Practical guide for efficient text processing in AI applications.
Preparing to Implement and Secure Microsoft 365 Copilot
Explore key tasks for implementing Microsoft 365 Copilot, including prerequisites, data preparation, licensing, and security measures to protect sensitive business information.
Vector Search and State-of-the-Art Retrieval for Generative AI Apps
Explore vector search, hybrid search, and reranking models for improved knowledge retrieval in Generative AI apps. Includes live code demos and evaluation insights on retrieval quality.
CNN Training Loop Explained - Neural Network Code Project
Learn to build a convolutional neural network training loop using Python and PyTorch. Gain practical skills in implementing deep learning algorithms for image processing tasks.
Introduction to XGBoost - Machine Learning Tutorial
Comprehensive tutorial on XGBoost: from basic principles to hands-on implementation. Learn gradient boosting, parameter tuning, and model evaluation for real-world machine learning applications.
Object Detection Using Detection Transformer (DETR) for Bone Fracture Dataset
Learn to build an advanced object detection system using DETR, covering data preparation, model implementation, training, and inference with PyTorch, Transformers, and PyTorch Lightning.
How to Get Started with MLOps in Azure Databricks
Explore the machine learning lifecycle and operationalize models using Databricks Machine Learning, focusing on scaling deployments with MLOps for company-wide implementation.
Machine Learning Made Easy on Kubernetes - DevOps for Data Scientists
Explore DevOps for ML on Kubernetes: streamline data prep, model training, testing, and deployment using tools like Tensorflow, Kubeflow, and Pachyderm. Ideal for data scientists and SRE teams.
Enabling the Business for AI
Explore IBM's approach to AI, leveraging Red Hat OpenShift and Cloud Pak for Data as an Enterprise Insights Platform. Learn about data preparation, collection, and organization for AI implementation.
Building and Managing a Centralized ML Platform with Kubeflow at CERN
Explore CERN's centralized ML platform using Kubeflow, covering data preparation, model training, and serving. Learn about resource optimization, integration challenges, and cloud expansion possibilities.
Do We Know Our Data, as Good as We Know Our Tools?
Explore data analysis challenges and techniques for cleaning, preparing, and understanding datasets before model training. Learn to detect issues and apply solutions for better accuracy.
Machine Learning Made Easy With PyCaret
Discover how PyCaret simplifies machine learning workflows, automates processes, and boosts productivity. Learn to integrate this low-code library for faster, more efficient ML experiments.
SQL Server Big Data Clusters Architecture
Explore SQL Server 2019 Big Data Clusters architecture, including containers, Kubernetes, Spark, and HDFS. Learn to unify data sources, run big queries, and implement data preparation in an end-to-end scenario.
Connected Cars - IoT Robodrive
Explore RoboDrive Technology for geo-distributed analytics in autonomous driving. Learn about technical challenges and solutions for setting up platforms in this emerging field.
Serverless with Vue and Node.js
Explore serverless architecture, JAM Stack, and static site benefits with Vue.js and Node.js. Learn practical implementation techniques for scalable and efficient web development.
Using Scikit-Learn's Interface for Turning Spaghetti Data Science into Maintainable Software
Learn to structure data science code using Scikit-Learn's estimator and meta-estimator interfaces. Discover how to transform spaghetti code into maintainable, testable, and reusable machine learning models.
Deep Learning with PyTorch for Fun and Profit
Explore practical deep learning applications using PyTorch, including style transfer, speech generation, and text generation. Learn about data preparation, hardware setup, and creating neural network models for creative projects.
Boosting Research with Machine Learning
Explore machine learning applications in research, from neuron recognition to EEG-based movement prediction. Learn Python techniques for data preparation and analysis using various ML methods.