Accelerate your data project pipelines with Saagie using Vertica’s in-database machine learning capabilities. Another ML platform that you can now use with Vertica. A win-win situation!

Saagie is a Dataops platform that offers various ready-to-use technologies and advanced pipelines that enable you to manage all your data projects through a single interface. Select the technology of your choice for each step of the data project – from data extraction to data visualization.

VerticaPy is a python library to leverage Vertica’s machine learning capabilities.

Vertica’s Analytics and Machine learning functions and algorithms let you work with vast dataset to easily explore, prepare data, analyze, build and train ML models and perform data science operations, all in one place using Saagie modules!

Introducing an end-to-end machine learning solution with Vertica and Domino Data Lab that enables you to explore, analyze, and model your data in Vertica using VerticaPy.

Domino Data Lab is a data science platform to build and deploy machine learning models, monitor performance, and collaborate with one another.

VerticaPy is a Python library to perform data science operations in Vertica.

This solution simplifies collaboration on data science projects and allows you to perform in-database machine learning in Vertica. You can now easily connect Domino Data Lab to Vertica using VerticaPy. Build and train your model and share the models within your organization!

Using Vertica’s analytics and machine learning algorithms, you can optimize your models for efficiency and identify trends. Let us walk you through how you can successfully perform all your data science operations in Vertica while using Domino’s in-built Jupyter notebook.

Data Preparation is a crucial step to ensure accurate analysis. VerticaPy offers a comprehensive set of tools for streamlining your data. From cleaning and transforming features to handling categorical variables, VerticaPy simplifies the process. Normalize, scale, and encode features for model readiness, and extract insights from text and time data. With VerticaPy's Data Preparation capabilities, your dataset is primed for effective modeling and valuable insights.

• Data Cleaning:
  Streamline your dataset by identifying and handling duplicates, inconsistencies, and errors.
• Feature Engineering:
  Create new meaningful features or transform existing ones to enhance model performance.
• Normalization and Scaling:
  Standardize features for balanced model training and accurate comparisons.
• Categorical Encoding:
  Convert categorical variables into numerical representations suitable for analysis.
• Text and Time Feature Extraction:
  Extract insights from text and time data for richer analysis.
• Data Splitting:
  Divide your dataset into training, validation, and test sets for model evaluation.

Scalable Modeling is where VerticaPy excels, seamlessly combining advanced analytics with Vertica's performance. Choose from a variety of machine learning algorithms, fine-tune parameters, and leverage parallel processing for rapid training. VerticaPy's built-in evaluation tools ensure your model's accuracy, while feature importance analysis adds transparency to predictions. With easy deployment and in-database scoring, VerticaPy's Scalable Modeling capabilities empower you to put your insights into action.

• Machine Learning Algorithms:
  Access a wide range of powerful machine learning algorithms for diverse use cases.
• Hyperparameter Tuning:
  Optimize model performance by fine-tuning hyperparameters for best results.
• Parallel Processing:
  Leverage Vertica's parallel processing capabilities for lightning-fast model training.
• Model Evaluation:
  Assess model performance through various metrics and techniques to ensure accuracy.
• Feature Importance Analysis:
  Identify key contributors to model predictions for enhanced interpretability.
• Model Deployment:
  Easily deploy models within Vertica for in-database scoring and real-time predictions.