Course Details
Harnessing
Machine Learning
A 2-Day Immersive Training on Databricks
Additionally, it delves into distributed machine learning with Spark ML, using Hyperopt for hyperparameter optimization, and scaling models with ensemble learning techniques.
Course Overview
Machine Learning with Databricks provides a thorough examination of advanced machine learning techniques using Databricks, focusing on optimizing machine learning workflows and leveraging Databricks’ capabilities.
It covers configuring and managing machine learning clusters, integrating Git repositories, and orchestrating multi-task workflows. Participants will explore AutoML for automating pipeline creation, utilize the Feature Store for feature management, and apply MLflow for experiment tracking. The program also addresses machine learning workflows, including exploratory data analysis, feature engineering, hyperparameter tuning, and model evaluation.
Course Modules
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
FAQ's
The course focuses on using Databricks for machine learning workflows, including data preparation, model training, hyperparameter tuning, and deployment.
Participants should have a background in data science, knowledge of Python or Scala, and familiarity with basic machine learning concepts.
The course typically spans 2 to 3 days, with a mix of theoretical content and hands-on labs.
Yes, the course usually includes real-world projects and case studies to provide practical experience in applying machine learning techniques using Databricks.
The course covers a range of models, including supervised learning, unsupervised learning, and deep learning techniques, depending on the course content.
Many courses offer a certificate of completion, which can be used to demonstrate your skills and knowledge gained during the training.
Support options may include access to course materials, online communities, and follow-up resources provided by the training organization.
Course Features
Our course offers a comprehensive machine learning workflow, covering the entire lifecycle from data preparation to model deployment on Databricks. It features deep integration with MLflow for experiment tracking, model versioning, and scaling. Participants will work with scalable machine learning models using Spark MLlib, XGBoost, and scikit-learn, along with advanced data preparation using Delta Lake. The course includes practical labs on real-time and batch processing, model deployment as REST APIs, and leveraging Databricks AutoML. Collaboration tools, cloud integration with Azure and AWS, and version control complete the hands-on learning experience.
Career Advancement
Equips participants with key skills needed for the growing demand in data science and machine learning roles, enhancing career prospects.
Cloud-Native
Leverages cloud environments, which is critical for scalable and distributed ML workflows, positioning participants to work on large-scale machine learning solutions
Time Efficiency
Automated ML processes and scalable infrastructure reduce model training time, allowing for faster iteration and innovation.
Elevate Your Skills
Join our courses to enhance your expertise in data engineering, machine learning, and advanced analytics. Gain hands-on experience with the latest tools and techniques that are shaping the future of data.
