Unlock Insights and Efficiency at CMS Data Summit 2022
Table of Contents
- Introduction
- Challenges Faced by Data Teams in Operationalizing Analytics
- Small Scale and Ad Hoc Approach
- Transitioning from Development to Production
- Silos and Narrow Metrics
- The Databricks Approach to Operationalizing Modeling
- Holistic Approach
- Data Ops Challenges and Best Practices
- Model Ops Challenges and Best Practices
- DevOps Challenges and Best Practices
- Databricks Capabilities for Model Development
- Enabling Non-Coders with Bamboo Lib Integration
- Simplifying Data Acquisition and Transformation with Delta Live Tables
- Accelerating Model Development with AutoML
- Complete Model Lifecycle Management with MLflow
- Empowering Data Teams with Self-Service Analytics and Model Development
- Conclusion
Challenges and Best Practices in Operationalizing Analytics
In today's data-driven world, data teams face numerous challenges when it comes to operationalizing analytics and extracting the full value from their analytics and model development efforts. These challenges often hinder the ability of data teams to achieve self-service analytics and model development, resulting in delays and limitations.
Small Scale and Ad Hoc Approach
One of the primary challenges faced by modeling teams is starting at a small scale and in an ad hoc fashion. In the initial stages, data teams may begin with a handful of models and static datasets, working in a bespoke technical stack. However, to achieve greater value, it is crucial to scale up and transition from a small-scale approach to a more standardized and production-ready environment.
Transitioning from Development to Production
As modeling teams progress in their Journey, they encounter the challenge of transitioning from model development to production. This shift requires operationalizing hundreds or even thousands of models, dealing with changing data, retraining models, and standardizing conventions and technical stacks. It is essential to streamline this transition to ensure smooth and efficient model deployment.
Silos and Narrow Metrics
Data teams often operate in separate silos, with different teams being responsible for data operations, analytics, and DevOps. Each team has its own set of metrics and focuses on specific aspects. Data engineering teams may prioritize data access and quality, analytics teams may focus on connecting model performance with business metrics, and DevOps teams may prioritize service reliability and deployment. However, this siloed approach leads to a loss of sight of the bigger picture and hinders collaboration and coordination.
The Databricks Approach to Operationalizing Modeling
Databricks, as a data and AI company, provides a unique approach to operationalizing modeling. With their pioneering concept of the data lake house, Databricks combines the strength of data governance and management typically found in data warehouses with the flexibility and scalability of data lakes. This enables individual data teams to overcome the challenges faced in operationalizing analytics and model development.
Databricks offers a holistic approach to tackle the challenges encountered in each phase: data ops, model ops, and DevOps. By integrating different capabilities and solutions, Databricks ensures that data teams can achieve self-service analytics and model development, empowering them to deliver results efficiently.
Data Ops Challenges and Best Practices
In the data ops phase, teams often face challenges related to data access, data quality, and connecting model performance with business metrics. Databricks addresses these challenges through a data-centric approach. By providing a governed way to access and share data, data teams can collaborate effectively. Additionally, Databricks offers solutions for live linking business metrics with model performance and an in-platform feature store to tackle data quality issues.
Model Ops Challenges and Best Practices
Model ops phase involves challenges in reproducing and managing models, sharing and accessing models across different teams, and tracking metadata. Databricks addresses these challenges by standardizing open formats, removing proprietary and closed formats that hinder collaboration, and providing capabilities for model tracking, registration, and lifecycle management. With Databricks, managing and deploying hundreds or thousands of models becomes streamlined and efficient.
DevOps Challenges and Best Practices
DevOps teams often act as a bottleneck in the model development process due to the back-and-forth communication required between data teams and DevOps teams. To address this challenge, Databricks provides capabilities like model scoring, model observability, and self-service deployment options through MLflow. These features enable data teams to scale impact by sharing results and easily deploying data applications, reducing reliance on DevOps teams.
By addressing the challenges faced in each phase, Databricks offers a comprehensive solution that empowers data teams throughout the model development process.
Databricks Capabilities for Model Development
To enable self-service analytics and model development, Databricks provides various capabilities and solutions. These capabilities cater to different user needs, whether they are comfortable with coding or prefer a low code approach.
Enabling Non-Coders with Bamboo Lib Integration
Databricks acknowledges that not all users are comfortable with coding but possess valuable business knowledge and subject matter expertise. By integrating with tools like Bamboo Lib, Databricks allows non-coders to contribute to the data analytics and model development process. This integration enables tasks such as data exploration, data prep, and data transformation in a no-code manner, expanding the involvement of individuals with business expertise.
Simplifying Data Acquisition and Transformation with Delta Live Tables
Data acquisition and transformation are critical steps in the model development process. Databricks simplifies these steps by introducing Delta Live Tables, which provide a declarative approach to building ETL pipelines. This simplification streamlines data access, ensures data quality, and offers built-in data quality metrics and lineage capabilities. Delta Live Tables simplify the data ops phase and enhance the efficiency of model development.
Accelerating Model Development with AutoML
To accelerate model development, Databricks provides AutoML, a low code solution that automates the model development process. AutoML offers a quick way to develop baseline models and generates the corresponding code, allowing users to inspect and modify the code for further refinement and optimization. This low code solution empowers both experienced coders and individuals with less coding experience.
Complete Model Lifecycle Management with MLflow
To manage the entire model lifecycle, Databricks offers MLflow. MLflow provides functionalities for model tracking, Artifact logging, model registry, and deployment. With MLflow, data teams can easily track and manage models from development to deployment, ensuring reproducibility, collaboration, and scalability.
Empowering Data Teams with Self-Service Analytics and Model Development
By integrating these capabilities and solutions, Databricks empowers data teams to achieve self-service analytics and model development. With a single platform that caters to every phase of the model development process, data teams can speed up time to value, remove bottlenecks caused by silos, and collaborate efficiently. Databricks enables data teams to overcome the challenges faced in operationalizing analytics, unlocking the full potential of their data.
Conclusion
Operationalizing analytics and model development is a complex task, and data teams often face challenges related to scale, transitions, and silos. Databricks offers a holistic approach to tackle these challenges, providing capabilities and solutions for data ops, model ops, and DevOps. By simplifying data access, offering low code solutions, and providing complete model lifecycle management, Databricks empowers data teams to achieve self-service analytics and model development. With Databricks, data teams can overcome operationalization challenges, streamline their workflows, and unlock the full value of their analytics and model development efforts.
Highlights
- Databricks provides a holistic approach to operationalizing analytics and model development.
- Challenges faced by data teams include small-scale approaches, transitioning from development to production, and operating in silos.
- Databricks' capabilities address challenges in data ops, model ops, and DevOps phases.
- Solutions like Bamboo Lib, Delta Live Tables, AutoML, and MLflow empower non-coders, simplify data acquisition and transformation, accelerate model development, and manage the complete model lifecycle.
- Databricks enables self-service analytics and model development, removing bottlenecks and enhancing collaboration among data teams.
FAQ
Q: What are the main challenges faced by data teams in operationalizing analytics?
A: Data teams often struggle with starting at a small scale, transitioning from development to production, and operating in silos.
Q: How does Databricks address these challenges?
A: Databricks provides a holistic approach, offering capabilities and solutions for data ops, model ops, and DevOps. Their solutions streamline processes, enable collaboration, and empower data teams with self-service analytics and model development.
Q: Can non-coders contribute to the model development process with Databricks?
A: Yes, Databricks integrates with tools like Bamboo Lib, allowing non-coders to perform tasks such as data exploration, data prep, and data transformation in a no-code manner.
Q: How does Databricks simplify data acquisition and transformation?
A: Databricks introduces Delta Live Tables, which provide a declarative approach to building ETL pipelines. This simplifies the data acquisition and transformation process, ensuring data access, quality, and lineage.
Q: What is AutoML, and how does it accelerate model development?
A: AutoML is a low code solution offered by Databricks. It automates the model development process, providing a quick way to develop baseline models. Users can inspect and modify the generated code to refine and optimize their models.
Q: How does MLflow help with model lifecycle management?
A: MLflow enables complete model lifecycle management, offering functionalities for model tracking, artifact logging, model registry, and deployment. It allows data teams to track and manage models from development to deployment, ensuring reproducibility and scalability.