Databricks ML Engineer Skill Path
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
End-to-end industry-focused skill path to help gain essential skills for running machine learning workloads on Databricks
Pre-Requisites
✅ Model Pre-procesisng
✅ Model building and Evaluation
✅ NLP tasks and RNN
✅ Vision tasks and CNN
Key Highlights
✅Mutiple raw and clean data sources for ingestion
✅ Aligned to Databricks ML Associate certificate
✅ Intricacies of Model serving and MOps
Skill Path
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp Skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
Introduction to Databricks
This Module details the need for a Unified Analytics platform like Databricks and how to utilize it to tackle Data + AI challenges. In this Module, we will look into Databricks architecture and how it can be created in Azure Databricks. We will also understand different types of clusters needed for various Analytical workloads
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp Skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
Exploratory Analysis and data preparation using Databricks
This module emphasizes techniques for data exploration and visualization using Spark and Python within Databricks.
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp Skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
AutoML using Databricks
This module explores Databricks' AutoML capabilities, which automate parts of the machine learning workflow.
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp Skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
Feature Engineering using Databricks
This module focuses on the process of preparing data for machine learning, including handling missing values and encoding categorical features, engineering domain-specific features, and other data pre-processing techniques
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp Skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
Feature Management with Feature stores
This module covers the Databricks Feature Store, which helps in storing and accessing features for machine learning pipelines.
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp Skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
Feature Management with Unity catalog
This module explores the use of Unity Catalog for managing and sharing features across different teams and projects within Databricks.
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp Skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
Model Logging with MLFlow on Databricks
This module delves into MLflow, focusing on experiment tracking, model management, and deployment within Databricks.
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp Skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
ML Model Building
This module covers the training of machine learning models, including hyperparameter tuning and parallelization techniques.
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp Skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
Distributed Model training
This module discusses the challenges and techniques for scaling machine learning models using Spark.
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp Skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
Model Serving
This module covers the deployment and serving of machine learning models in Databricks, ensuring models are accessible for predictions in production environments.
![2f06907e-0c74-4ee9-b16d-219199af6778_3135f616-5436-41b3-adc1-310a6cfc4645_Databricks-data engineering.webp Skill path cover image](/_next/image?url=https%3A%2F%2Fmentorskool-platform-uploads.s3.ap-south-1.amazonaws.com%2FstrapiUploads%2Fuploads%2F2f06907e_0c74_4ee9_b16d_219199af6778_3135f616_5436_41b3_adc1_310a6cfc4645_Databricks_data_engineering_6e99c7359d.webp&w=3840&q=100)
Model Drift Analysis
This module focuses on techniques and tools for monitoring and analyzing model drift in machine-learning applications. It covers the detection of changes in data and model performance over time to ensure ongoing model accuracy and reliability.