The Fifth Elephant 2023 Monsoon

About me

I am working in ABInBev as Senior Manager – Data Science with experience in Statistical/Machine Learning and Predictive Modelling and analytics consulting. With a passion for machine learning and data-driven solutions, I have been actively involved in the development and implementation of advanced analytics frameworks. Currently working as Product Owner and DS lead for AlgoVault.
I am master’s in computer science and have 12+ years of experience in conceptualization and delivery of actionable models across Finance, Media, Retail, Wholesale and Logistics industries.

AlgoVault - Region and platform-Agnostic Machine Learning Framework

Objective

In recent years, the widespread adoption of machine learning has led to significant advancements in various domains. However, developing machine learning solutions that are both scalable across regions and modular has remained a significant challenge. Here, we present a machine learning framework called AlgoVault that not only addresses the challenges of scalability and modularity but also ensures platform-agnostic compatibility. Our framework enables users to effortlessly switch between platforms such as ADB (Azure Databricks), AML (Azure Machine Learning), or any other platform, while preserving the integrity and functionality of their machine learning models.

Details

With a focus on flexibility, AlgoVault supports back testing, hyperparameter optimization, and automated machine learning (AutoML) features, including feature selection. By integrating these capabilities, we empower users to streamline their model development process, enhance predictive accuracy, and drive decision-making with confidence.
Furthermore, AlgoVault framework incorporates distributed computing capabilities and optimized algorithms, ensuring efficient handling of large datasets and seamless scalability across platforms. This scalability allows users to seamlessly transition their models from one platform to another, leveraging the strengths of each platform and adapting to changing requirements without the need for extensive reconfiguration.

Outline/Discussion points

Architecture and Modularity

1. Description of the modular architecture designed for the framework
2. Importance of modularity in enabling platform-agnostic compatibility

Scalability Across Regions and Platforms

1. Overview of the optimized algorithms implemented to ensure efficient scalability
2. Discussion of how the framework seamlessly adapts to different platforms (e.g., ADB, AML)

Backtesting, Hyperparameter Optimization, and AutoML

1. Integration of Backtesting and hyperparameter optimization capabilities for fine-tuning models and to assess and compare model performance
2. Explanation of automated feature selection methods to enhance predictive accuracy

Value Added

The framework’s versatility and adaptability empower users to quickly iterate, test, and refine their models, ultimately accelerating the development process.
Different regions inside organization can quickly backtest, tune hyperparameter and deploy their own models and compare results with existing models as well. The framework serves as a valuable tool for data scientists, enabling them to unlock the full potential of machine learning in region-agnostic applications and deliver impactful results.

Conclusion

In summary, AlgoVault offers a scalable, modular, and reusable solution for region or platform-agnostic applications. By seamlessly accommodating model customization, backtesting, hyperparameter optimization, and AutoML capabilities, it empowers data scientists to build and deploy robust and accurate machine learning models with ease. We believe that AlgoVault will significantly benefit the machine learning community internally, enabling data scienctists to efficiently address the challenges of region-specific modeling and deployment.