Predictive and Generative AI Projects with Vertex AI Feature Platform
Explore Vertex AI Feature Store's next-gen capabilities for ML projects, including real-time serving and vector embedding support for generative AI.
Real-Time Machine Learning: Features and Inference - MLOps Podcast #135
Explore real-time ML features and inference with LinkedIn experts. Gain insights on implementation, challenges, and best practices for moving from batch to interactive near real-time machine learning.
MLOps Build or Buy - Startup vs. Enterprise Perspectives
Explore MLOps strategies for startups and enterprises, focusing on build vs. buy decisions, data privacy, and recommender systems. Gain insights from Slack's ML engineers on platform development and integration challenges.
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.
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.
Scalable Feature Engineering with Hamilton on Ray
Explore scalable feature engineering using Hamilton and Ray at StitchFix. Learn declarative dataflow, team scaling, and efficient computation for thousands of time-series features.
Introduction to a Basic Machine Learning Workflow for Predicting Materials Properties
Learn a basic machine learning workflow for predicting material properties, covering data cleaning, feature engineering, model training, and optimization using decision trees to forecast material bandgaps from compositions.
The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction
Automate machine learning model development for materials property prediction using MAST-ML. Learn data import, feature engineering, model evaluation, and uncertainty quantification through hands-on activities.
Data-Driven Materials Innovation: Where Machine Learning Meets Physics
Explore how machine learning and physics-based methods synergize to revolutionize materials science, addressing challenges in data-driven design across various technology fields with Schrödinger's innovative tools.
NextGen Medical Affairs - Medical Response and Literature Review with GenAI
Revolutionizing medical insights with NLP and GenAI: Extract, analyze, and visualize unstructured data for efficient pattern identification and strategic decision-making in pharmaceutical research.
Building a Modern Marketing Team with Machine Learning and Databricks
Discover how Rogers leverages ML and Databricks to revolutionize marketing, featuring a reusable framework for data science across industries and insights on implementing key marketing models.
Supervised Learning: The Setup and Hypothesis Spaces - Lecture 2
Dive into supervised learning fundamentals through interactive examples, exploring instance spaces, label spaces, and hypothesis spaces while understanding their crucial roles in machine learning.
AI Innovations: The Power of Feature Platforms - MLOps Mini Summit
Explore feature platforms for production AI, including Tecton's unified approach, Cleo's custom solution, and DIY strategies for building scalable, flexible ML infrastructures.
ML-Based Time Series Regression - Concepts from Demand Forecasting
Explore ML-based time series regression for demand forecasting in retail. Learn to optimize replenishment and pricing using AI for accurate predictions and automated decision-making at scale.