Intro to Machine Learning
Dive into machine learning fundamentals with engaging videos covering models, data exploration, validation, and advanced concepts like random forests and AutoML.
Automated Machine Learning Tuning with FLAML
Explore automated machine learning and tuning using FLAML, a fast Python library. Learn to automate ML tasks, perform general tuning, and conduct zero-shot AutoML efficiently.
CyclOps: A Framework for Data Extraction, Model Evaluation and Drift Detection in Clinical Machine Learning
Explore CyclOps, a unified framework for healthcare ML, enabling data extraction, model evaluation, and drift detection in clinical settings. Streamline development of robust AI models.
Hugging Face on Azure - Partnership and Solutions Announcement
Explore Hugging Face's integration with Azure, featuring state-of-the-art ML models, enterprise capabilities, and open-source collaboration for responsible machine learning.
MLOps Platforms From Zero - Databricks, MLFlow-MLRun-SKLearn
Comprehensive guide to building MLOps platforms using Databricks, AutoML, SKLearn, and AWS. Covers end-to-end process from project setup to model deployment, including CI/CD, experimentation, and containerization.
MLOps Masterclass - Theory to DevOps to Cloud-Native to AutoML
Comprehensive guide to MLOps: from theory to implementation, covering DevOps, cloud-native solutions, and AutoML. Learn to build, deploy, and manage machine learning systems across various platforms and technologies.
MIT 6.S191 - Deep Learning Limitations and New Frontiers
Explore deep learning limitations, adversarial attacks, uncertainty quantification, and AutoML in this lecture. Gain insights into neural network challenges and emerging frontiers in AI research.
What's New with Databricks Machine Learning
Explore Databricks' latest ML innovations, from data preparation to model deployment, in a detailed demo showcasing simplified lifecycle management and enhanced productivity features.
Apache Spark, Data Science, and Machine Learning Advances - Data + AI Summit 2021 Keynote
Explore AI advancements, data science, MLOps, and Databricks Lakehouse Platform innovations. Featuring industry leaders and Bill Nye, this keynote covers MLFlow updates, AutoML, and practical machine learning solutions.
AutoML Toolkit Deep Dive - Automating Feature Engineering and Model Optimization
Explore Databricks Labs' AutoML Toolkit for automating feature engineering, model selection, tuning, and deployment. Learn to accelerate machine learning workflows and enhance productivity.
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
Explore how Databricks Labs AutoML Toolkit simplifies and optimizes machine learning processes, from data preparation to model optimization, using financial loan risk data examples.
Low-Code Machine Learning on Databricks with AutoML
Discover how to build, train, and deploy ML models using Databricks AutoML and low-code tools, empowering users of all expertise levels to create predictive analytics solutions.
EvalML AutoML Library to Automate Feature Engineering, Feature Selection, Model Creation and Tuning
Explore EvalML, an AutoML library automating feature engineering, selection, model creation, and tuning. Learn to streamline your data science workflow and improve project efficiency.
AutoML Frameworks Accuracy Comparison with Kaggle Data
Explore the performance of popular AutoML frameworks across 10 Kaggle competitions, analyzing their accuracy and effectiveness in tackling real-world machine learning challenges.
Building an AutoML Web App with Python, Pandas Profiling, PyCaret, and Streamlit
Build an AutoML web app using Python, Pandas Profiling, PyCaret, and Streamlit in this hands-on tutorial. Learn to create a powerful machine learning tool quickly and efficiently.
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.
Simplifying Machine Learning Lifecycle Management in Healthcare
Explore end-to-end ML lifecycle in healthcare using Azure services. Learn data ingestion, model creation, deployment, and visualization for real-time patient predictions and batch scoring.
AutoML Time Series Forecasting for Infrastructure Resources
Spark-based AutoML framework for scalable time series forecasting of IT infrastructure resources, enabling efficient long-term planning and daily dynamic provisioning.
Automated Machine Learning on Azure - Comparing Azure ML Service and Azure Databricks
Explore AutoML on Azure Machine Learning Service and Azure Databricks to quickly build and deploy ML models, reducing time and expertise required for preprocessing and model creation.
Model Creation in Azure AutoML and Ingestion Through Excel
Explore Azure AutoML for machine learning, from model creation to Excel integration via VBA, with a focus on practical applications and time-saving techniques.
Auto-Train a Time-Series Forecast Model with AML & ADB
Learn to auto-train time-series forecast models using Azure Machine Learning and Azure Databricks for large-scale enterprise solutions in supply chain, healthcare, insurance, and finance.