1. Databricks Machine Learning Integration

• Assess scenarios for deploying standard versus single-node clusters
• Integrate Databricks Repos with external Git repositories for version control
• Manage branching, commits, and synchronization between Databricks Repos and external Git platforms
• Orchestrate complex machine learning workflows leveraging Databricks Jobs

2. Databricks Runtime for Machine Learning

• Configure and deploy clusters utilizing Databricks Runtime for Machine Learning
• Implement and manage Python libraries across Databricks notebooks

3. AutoML Capabilities

• Comprehend the machine learning pipeline automated by AutoML
• Retrieve and evaluate source code and performance metrics from AutoML-generated models
• Utilize the AutoML data exploration notebook to analyze dataset attributes

4. Feature Store Utilization

• Articulate the advantages of Feature Store for managing machine learning features
• Create and populate Feature Store tables, and integrate features into model training and scoring

5. Managed MLflow Operations

• Employ the MLflow Client API for experiment tracking and management
• Log metrics, artifacts, and models; implement nested runs for detailed tracking
• Register and transition model stages using MLflow Client API and Model Registry interface

1. Exploratory Data Analysis (EDA)

• Execute summary statistics and outlier detection on Spark DataFrames using `.summary()` and dbutils

2. Feature Engineering Techniques

• Implement indicator variables for imputed or replaced missing values
• Analyze and apply methods for handling missing data, including mode, mean, and median imputation
• Conduct one-hot encoding of categorical features and understand its impact on model performance

3. Model Training Strategies

• Apply random search and Bayesian optimization for hyperparameter tuning
• Navigate challenges associated with parallelizing iterative models and leverage Hyperopt with SparkTrials for optimization

4. Model Evaluation and Selection

• Execute cross-validation and grid-search for model evaluation
• Utilize metrics such as Recall, F1 Score, and RMSE, with considerations for log-transformed labels

1. Distributed Machine Learning Concepts

• Address challenges in scaling machine learning models and identify Spark ML’s role in distributed learning
• Differentiate between Spark ML and scikit-learn in the context of distributed versus single-node solutions

2. Spark ML Modeling APIs

• Perform data splitting, model training, and evaluation using Spark ML
• Develop and troubleshoot Spark ML Pipelines, understanding key considerations and potential issues

3. Hyperopt for Hyperparameter Tuning

• Utilize Hyperopt for parallelized and Bayesian hyperparameter optimization in Spark ML models
• Analyze the relationship between the number of trials and model performance accuracy

4. Pandas API on Spark

• Compare Spark DataFrames with Pandas on Spark DataFrames, and address performance considerations
• Convert between PySpark and Pandas on Spark DataFrames and leverage Pandas API for scalable data processing

5. Pandas UDFs and Function APIs

• Implement Apache Arrow for efficient Pandas-to-Spark conversions
• Utilize Pandas UDFs for parallel model applications and function APIs for group-specific model training

1. Model Distribution Techniques

• Understand the methodologies for scaling linear regression and decisiaon tree models within Spark

2. Ensemble Learning Distribution

• Explore ensemble learning methodologies including bagging, boosting, and stacking, and their application in distributed environments