From Pandas to Production: ELT with dlt
Explore data load tool (dlt) to streamline data science workflows, overcome roadblocks, and transition smoothly from exploration to production. Learn best practices for data handling and managing failures with real-life examples.
Building a MovieLens Recommender System
Explore recommendation systems and build your own using collaborative and content-based filtering techniques in Python with MovieLens dataset.
Why ML in Production is Still Broken
Explore challenges in deploying ML projects to production, focusing on the disconnect between development and real-world application. Learn key aspects distinguishing ML from traditional software engineering.
How to Monitor Machine Learning Stacks - Detecting and Addressing Special Issues
Discover advanced monitoring techniques for ML stacks, addressing unique challenges beyond traditional metrics to ensure robust performance and early issue detection.
Machine Learning with PyCaret - Workshop
Explore rapid prototyping and ML pipeline development using PyCaret, an open-source, low-code machine learning library for Python.
Team Roles in a Machine Learning Project and Project Flow
Develop context for data science projects, understand team roles, and build a data-driven culture. Learn about machine learning techniques and their appropriate applications.
Knowledge Graph Recommendation Systems for COVID-19
Explore knowledge graph recommendation systems for COVID-19, leveraging Python skills to enhance understanding of pandemic-related data and insights.
Managing Data Science in the Enterprise
Learn effective strategies for managing data science teams, processes, and technologies in enterprise settings, avoiding pitfalls and implementing best practices for breakthrough results.
Turbo-Charging Data Science with AutoML
Explore open-source AutoML tools and cloud provider options to enhance data science workflows. Hands-on demonstration of installation, usage, and unique features for improved efficiency.
Computer Vision in Practice - Building an End-to-End Pipeline for Object Detection and Segmentation
Build an end-to-end image segmentation pipeline using deep learning. Learn dataset customization, model training, validation, testing, and mobile deployment techniques.
Holistic Health Metrics for ML-Based Products
Explore holistic health metrics for ML-based products. Learn where and when to collect data, model, post-model, and business-level metrics to ensure system accuracy and reliability.
Build, Train, and Deploy Models with Amazon SageMaker
Learn to build, train, and deploy machine learning models using Amazon SageMaker through hands-on experience. Gain practical skills in ML model development and implementation on AWS platform.
Women in AI - Transitioning Careers, Challenges, and Opportunities
Explore career transitions, challenges, and opportunities for women in AI through insights from industry experts and leaders.
Validate and Monitor Your AI and Machine Learning Models
Discover techniques for validating and monitoring advanced AI and ML models, ensuring long-term performance and business adoption in various industries.
Simplify ML Pipeline Automation and Tracking Using Kubeflow and Serverless Functions
Simplify ML pipeline automation and tracking with Kubeflow and serverless functions. Learn to streamline your machine learning workflows for improved efficiency and productivity.
Introduction to Machine Learning with Python
Explore machine learning fundamentals using Python, covering key concepts and practical applications in this hands-on workshop led by a field engineer from Domino.
ML Monitoring ML: Scalable Monitoring of ML Models in Production Environments
Explore scalable ML model monitoring in production. Learn key performance measures, anomaly detection techniques, and an open-source agent for automated tracking and alerting of model issues.
Automated Pipeline for Large-Scale Neural Network Training and Inference
Explore cloud-native ML pipeline for automated anomaly detection using BiLSTM models. Learn to manage thousands of models with data collection, training, validation, and deployment techniques.
Reaching Lightspeed Data Science - ETL, ML, and Graph with NVIDIA RAPIDS
Accelerate data science workflows using GPUs and RAPIDS. Learn performance gains, easy migration, and new possibilities in ETL, ML, and graph analytics.
MLOps at Scale: Predicting Bus Departure Times Using 18,000 ML Models
Explore MLOps at scale through a case study on predicting bus departure times using 18,000 ML models. Learn practical insights for implementing large-scale machine learning projects.
MLOps and Automation Workshop - Bringing Machine Learning to Production in a Few Easy Steps
Learn to deploy ML/AI to production faster using MLOps technologies. Explore automation techniques and best practices for efficient machine learning operations.