Machine Learning with Kubernetes
Explore MLOps with Kubernetes: Learn to build scalable ML pipelines using Kubeflow, optimize cluster design, and leverage Istio for enhanced observability and security in production environments.
Hyperparameter Tuning Using Kubeflow
Explore automated machine learning with Katib, a Kubernetes-native platform for hyperparameter tuning and neural architecture search, enhancing model performance efficiently.
Angel 3.0 - A Full Stack Machine Learning Platform
Explore Angel 3.0, a comprehensive machine learning platform featuring auto feature engineering, hyperparameter tuning, model serving, and PyTorch integration for enhanced graph embedding and GNN algorithms.
AutoML and Training Working Group Updates - Kubeflow Developments
Explore AutoML and Training Working Group updates for Kubeflow, including Katib's V1 API graduation, advanced parameter distribution, and LLM tuning, plus Training Operator's roadmap for enhanced ML framework support.
MLOps: Databricks MLFlow and Optuna Hyper-parameter Tuning
Optimize XGBoost models using Databricks, MLflow, and Optuna for efficient hyperparameter tuning and improved machine learning workflows.
MLOps: Databricks and MLFlow Hyper-parameter Tuning for XGBoost Models
Optimize machine learning models using Databricks and MLflow for hyperparameter tuning with XGBoost and HyperOpt.
Overview of Automated ML June 2019
Explore automated machine learning for accelerating AI development, with demos on Azure ML Service and Databricks, focusing on Python implementation and recent Azure technologies.
Tensorflow, Deep Learning and Modern Convolutional Neural Nets, Without a PhD
Explore modern convolutional neural networks and deep learning with TensorFlow. Learn practical tips, engineering best practices, and recent advances in image recognition without needing a PhD.
A Taste of Random Decision Forests on Apache Spark
Explore Random Decision Forests on Apache Spark with Sean Owen. Learn about decision trees, feature vectors, model interpretation, and hyperparameter tuning for machine learning in distributed environments.
Hydro - Surrogate-Based Hyperparameter Tuning Service in Datacenters
Innovative hyperparameter tuning service for deep learning models, optimizing efficiency and resource utilization in datacenters through surrogate models and adaptive resource management.
Probabilistic Forecasting with DeepAR and AWS SageMaker
Explore probabilistic forecasting with DeepAR, combining deep learning and time series analysis for accurate predictions with uncertainty estimates across multiple related datasets.
Automated Machine Learning With Keras
Explore automated machine learning with Keras Tuner and AutoKeras, enabling efficient development of high-performance deep learning models while requiring less expertise and effort.
How to Track Progress and Collaborate in Data Science and Machine Learning Projects
Practical guidelines for organizing, tracking, and collaborating on data science projects, covering code management, metrics tracking, and effective communication between teams.
From Notebook to Kubeflow Pipelines with HP Tuning - A Data Science Journey
Explore Kubeflow for effortless ML pipeline creation, scaling, and hyperparameter tuning. Convert Jupyter notebooks to deployable workflows and leverage Kubeflow components for efficient data science journeys.
A Day in the Life of a Data Scientist - Conquer ML Lifecycle on Kubernetes
Explore a data scientist's daily workflow, from data prep to model deployment, using Kubeflow and Kubernetes for efficient machine learning lifecycle management.
Life is Not Fair, but Your ML Pipeline Can Be
Explore techniques to evaluate and enhance fairness in multi-cloud Kubeflow workflows, covering responsible AI, fairness aspects, and open-source tools for fair model learning and hyperparameter tuning.
Machine Learning with Scala on Spark
Explore machine learning with Spark and Scala, comparing frameworks, discussing advantages, and addressing challenges in data science. Gain insights into Spark.ml's capabilities and potential for predictive modeling.
An Introduction to Forecasting Using Orbit
Explore Orbit, a probabilistic forecasting tool developed by Uber, and learn its application in demand forecasting for technical support teams through a practical case study.
Interpretability Tools as Feedback Loops in Machine Learning Training
Explore machine learning interpretability techniques to optimize training sequences and develop more effective models without increasing compute or training time.
Machine Learning - Testing and Error Metrics
Explore machine learning model evaluation, including metrics, error types, cross-validation, and optimization techniques for improved performance and decision-making.
Vertex AI - Pipelines for Your MLOps Workflows
Explore Vertex AI Pipelines for MLOps workflows, covering experiment pipelining, infrastructure management, and practical examples of machine learning development and deployment cycles.