Managing the Complete Machine Learning Lifecycle with MLflow - Introduction to MLflow Tracking
Introduction to MLflow: tracking experiments, packaging projects, deploying models, and managing lifecycle. Learn to record, reproduce, and compare ML runs using MLflow's components and UI.
State of the Industry - Machine Learning Platforms and MLOps
Explore the state of MLOps and ML platforms with industry leaders, focusing on challenges, common components, and Databricks' approach to unified data analytics and machine learning.
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.
Productionizing Machine Learning with Apache Spark, MLflow and ONNX - Cloud Deployment Using SQL Server
Explore productionizing machine learning using Apache Spark, MLflow, and ONNX with SQL Server. Learn model management, lifecycle orchestration, and leveraging SQL Server for ML model storage and deployment.
Amazon SageMaker Tutorial - How to Use Amazon SageMaker for Machine Learning
Learn to build, train, and deploy machine learning models using Amazon SageMaker. Explore its architecture, features, and hands-on implementation for streamlined ML development and production.
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.
Introducing Kubeflow for Machine Learning and AI on Kubernetes
Explore Kubeflow for seamless machine learning model deployment, from laptop to production. Learn about its components, scalability benefits, and integration with tools like TensorFlow and Apache Spark.
Deploy a Customer Churn Prediction Model with Streamlit and Docker
Learn to build and deploy a customer churn prediction model using Streamlit and Docker. Discover how to package machine learning models into interactive applications for effective customer retention strategies.
Building Data Science Products - Think Business First
Learn to approach data science projects from a business perspective, focusing on key questions and considerations before diving into tools and techniques.
DevOps in Data Science - What Works and What Doesn't
Explore challenges and solutions in implementing DevOps for data science projects, focusing on balancing ML engineers' needs with data scientists' desire for simplicity using Kubeflow and other tools.
Move Data Science Beyond Experimentation with MLOps Platforms
Explore MLOps platforms to streamline data science workflows, automate tasks, and enhance collaboration. Learn best practices for efficient AI project delivery using real-world datasets.
Simplify End-to-End MLOps with PostgresML
Discover how SQL and PostgresML simplify MLOps workflows, streamlining AI/ML project management from development to deployment and monitoring in production environments.
Bringing CI/CD Practices to Machine Learning with MLOps
Learn how to apply CI/CD practices to machine learning pipelines, enhancing model development from data exploration to deployment for better, faster results in MLOps.
Deploying ML at Scale with Kubernetes and TFX
Learn to deploy machine learning models at scale using Kubernetes and TensorFlow Extended (TFX), enhancing your ability to manage and scale ML workflows efficiently.
Improving Productivity for Building ML Workloads Using Amazon SageMaker Autopilot
Automate ML model training and tuning for customer churn prediction using Amazon SageMaker Autopilot, with deployment and iteration in SageMaker Studio.
The Missing Piece of MLOps: Observability in Machine Learning Applications
Explore ML observability's role in MLOps, learn to prepare for it, and discover how it showcases business value and recognizes data scientists' work.
Building Automated Model Life Cycles to Show Data Science Business Contribution and Minimize Impact
Design and build automated model life cycles incorporating industry best practices, optimizing data science contributions and minimizing impact on business operations.
Creating an ML Model as a Business User
Validate use cases, build models, and develop business cases for ML deployment as a non-technical user.
Deploying Generative AI Models - Best Practices and Interactive Example
Explore best practices for deploying generative AI models at scale, with an interactive example using UbiOps for serverless and cloud-agnostic implementation.
De-Risk Your AI Efforts by Removing Friction From Your MLOps Processes
Remove friction from MLOps processes to deliver trusted, agile, and controlled AI models faster. Learn strategies to overcome industrialization challenges and accelerate value delivery from analytics.
Accelerating Public Health Research: Why Healthcare Needs MLOps
Explore MLOps applications in healthcare to accelerate public health research and improve patient outcomes through advanced machine learning techniques.