How to Deploy Machine Learning Models in Production
Learn to train, deploy, and maintain machine learning models in production environments, covering real-time decision-making and scalable frameworks for experimentation and deployment.
Your First Data Science Project: Planning for Success
Learn effective project management techniques for data science, covering agile and waterfall methodologies, autoML, model monitoring, and data drift to ensure successful implementation of data-driven initiatives.
To Buy or Not to Buy: Predicting Shopper Behavior in Grocery Retail
Explore demand modeling in grocery retail: forecasting sales, diagnosing performance, and optimizing pricing strategies. Learn key requirements, challenges, and solutions in this high-level overview.
From Code to Compliance - An Overview of AI Regulations for Data Practitioners
Explore AI regulations, standards, and responsible development practices to drive organizational value and foster innovation in the evolving regulatory landscape.
AI Solutions for a Greener Future: Advancements in Earth, Energy, and Environment
Explore AI advancements in earth, energy, and environmental solutions for a sustainable future through expert insights and innovative approaches.
Machine Learning Applications in Petroleum Production Data - Lecture 1
Explore machine learning applications in petroleum production data through Dr. Rami Aloush's comprehensive lecture series, focusing on energy transition and engineering concepts.
Machine Learning Applications in Petroleum Production Data - Lecture 2
Explore machine learning applications in petroleum production data analysis, focusing on energy transition and geological insights.
Machine Learning Applications in Petroleum Production Data - Lecture 3
Explore machine learning applications in petroleum production data analysis, focusing on energy transition and geological insights.
Bi-directional Lessons Between Traditional Evolutionary Biology and Cancer
Explore evolutionary biology's impact on cancer research and vice versa, uncovering insights that bridge these interconnected fields.
Modeling and Analyzing Cell-Cell Communication from Single-Cell Data
Explore CellChat, an integrated method for inferring and analyzing cell-cell communication from single-cell sequencing data using systems biology and machine learning approaches.
Modelling, Data Analytics, and AI for Investigating Heterogeneous Substances
Explore advanced techniques for studying heterogeneous materials, integrating physics-based modeling, data analytics, and AI to unlock new insights and accelerate research in material science.
Prediction of Multi-Class Peptides by T-Cell Receptor Sequences with Deep Learning
Explore deep learning techniques for predicting multi-class peptides using T-cell receptor sequences, enhancing immune system understanding and potential disease treatments.
Statistical and Machine Learning Approaches for Investigating Viruses and Virus-Host Interactions
Explore statistical and machine learning methods for identifying novel viruses and virus-host interactions in metagenomic studies, enhancing understanding of microbial communities.
Fine-Tuning Language Models with Declarative ML Orchestration
Gain hands-on experience using Flyte to declaratively specify infrastructure for fine-tuning language models on your own data, overcoming compute resource challenges.
Building a Fraud Detection Model with Feature Stores - Includes Shopify Case Study
Develop a real-time fraud detection system using ML data pipeline tools. Learn to build, deploy, and serve data pipelines with Feast and Tecton. Explore architectural patterns and model stages.
An Introduction to Drift Detection
Explore drift detection in machine learning, covering types, detection methods, and practical applications using Alibi Detect. Gain hands-on experience with real-world examples and state-of-the-art techniques.
Introduction to Data Analysis Using Pandas
Learn to effectively analyze data using pandas in Python. Master data manipulation, aggregation, and visualization techniques for extracting valuable insights from tabular datasets.
D-SOS: Dataset Shift with Outlier Scores
Explore a scalable two-sample test for dataset shift using outlier scores, focusing on low-density regions and comparing contamination rates across samples.
Deep AutoViML for TensorFlow Models and MLOps Workflows
Explore Deep AutoViML for building powerful TensorFlow models and MLOps workflows. Learn to fast prototype data pipelines and models using TF 2.4+ and Keras preprocessing layers.
Modeling, Querying and Seeing Time Series Data within a Self-Organizing Mesh Network
Explore graph database modeling for dynamic power grid networks. Learn to query and analyze sensor data, triage at-risk components, and understand network healing processes.
Quantum Classical Machine Learning for Medical Applications
Explore hybrid machine learning in healthcare, discussing quantum computing applications with EHR data and medical images. Gain insights into emerging technologies for healthcare professionals.