Training Overview
Disclaimer: This course is independently developed and not affiliated with Microsoft. It covers concepts and skills that closely align with the objectives of Microsoft’s DP-3014: Build machine learning solutions using Azure Databricks training, making it a strong preparatory or complementary learning experience.
Building Machine Learning Solutions with Azure Databricks: From Data to Deployment
This One-Day intermediate-level custom training This intermediate-level training empowers data scientists, Analysts, Data, AI and ML engineers with the skills to build scalable solutions using Azure Databricks, assuming proficiency in Python and familiarity with frameworks such as Scikit-Learn, PyTorch, or TensorFlow. Learners will gain hands-on experience navigating the Databricks environment, developing and assessing machine learning and deep learning models, managing workflows with MLflow, leveraging AutoML and hyperparameter optimization, and deploying models for production-scale decision-making.
Before enrolling in this custom training, participants should ideally have familiarity with using Python to explore data and train machine learning models with common open-source frameworks such as Scikit-Learn, PyTorch, and TensorFlow.
Module Breakdown:
Explore Azure Databricks: Introduces the Databricks platform and its integration with Apache Spark for scalable analytics.
Use Apache Spark in Azure Databricks: Covers how to run Spark jobs for data transformation, analysis, and visualization.
Train a Machine Learning Model in Azure Databricks: Walks through preparing data, training models, and evaluating performance using built-in ML frameworks.
Use MLflow in Azure Databricks: Demonstrates how to track experiments, manage models, and streamline the ML lifecycle with MLflow.
Tune Hyperparameters in Azure Databricks: Introduces automated hyperparameter optimization using the Optune library.
Use AutoML in Azure Databricks: Explains how AutoML simplifies model building by automatically selecting algorithms and tuning parameters.
Train Deep Learning Models in Azure Databricks: Focuses on neural networks for advanced AI workloads like NLP, computer vision, and forecasting.
Manage Machine Learning in Production: Guides learners through deploying models, monitoring performance, and enabling real-time decision-making at scale.