Databricks MLOps Stack for Scalable Machine Learning

The Databricks MLOps Stack is a powerful tool for scalable machine learning. It's designed to help data scientists and engineers build, deploy, and manage machine learning models at scale.

Databricks' MLOps Stack is built on top of the Apache Spark platform, which provides a unified analytics engine for large-scale data processing. This allows for seamless integration with existing data pipelines and infrastructure.

With the Databricks MLOps Stack, you can automate many of the tedious tasks associated with machine learning, such as data preparation, model training, and model deployment. This frees up your time to focus on more strategic and creative work.

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MLOps is a useful approach for the creation and quality of machine learning and AI solutions. By adopting an MLOps approach, data scientists and machine learning engineers can collaborate and increase the pace of model development and production.

It allows for continuous integration and deployment (CI/CD) practices with proper monitoring, validation, and governance of ML models. This is essential for keeping all processes synchronous and working in tandem.

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Productionizing machine learning is difficult, requiring collaboration and hand-offs across teams. It also requires stringent operational rigor to keep all these processes synchronous and working in tandem.

MLOps encompasses the experimentation, iteration, and continuous improvement of the machine learning lifecycle. This includes data ingest, data prep, model training, model tuning, model deployment, model monitoring, explainability, and more.

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MLOps Stacks provide a customizable stack for starting new ML projects on Databricks that follow production best-practices out of the box.

This stack includes three modular components: ML Code, ML Resources as Code, and CI/CD (GitHub Actions or Azure DevOps). These components work together to quickly get started iterating on ML code for new projects while ops engineers set up CI/CD and ML resources management.

The default stack includes example ML project structure, unit tested Python modules and notebooks, and ML pipeline resources defined through Databricks CLI bundles. This allows data scientists to quickly iterate on ML problems without worrying about refactoring their code into tested modules for productionization later on.

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Here are the three modular components of MLOps Stacks:

MLOps allows data teams to achieve faster model development, deliver higher quality ML models, and faster deployment and production.

The primary benefits of MLOps are efficiency, scalability, and risk reduction. MLOps enables data teams to achieve faster model development and deliver higher quality ML models, making it a valuable approach for creating and quality of machine learning and AI solutions.

MLOps also enables vast scalability and management, where thousands of models can be overseen, controlled, managed, and monitored for continuous integration, continuous delivery, and continuous deployment. This scalability is a key advantage of MLOps, allowing for more tightly-coupled collaboration across data teams.

MLOps provides reproducibility of ML pipelines, enabling more tightly-coupled collaboration across data teams, reducing conflict with devops and IT, and accelerating release velocity. This reproducibility is a crucial aspect of MLOps, making it easier to manage and maintain complex machine learning models.

Machine learning models often need regulatory scrutiny and drift-check, and MLOps enables greater transparency and faster response to such requests, ensuring greater compliance with an organization's or industry's policies. By adopting an MLOps approach, organizations can reduce the risk associated with deploying machine learning models.

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MLOps Stacks provide a customizable foundation for starting new ML projects on Databricks that follow production best-practices out of the box.

The Databricks MLOps Stacks repo offers a default stack with three modular components: ML Code, ML Resources as Code, and CI/CD (GitHub Actions or Azure DevOps). These components work together to enable data scientists to quickly iterate on ML problems while ops engineers set up CI/CD and ML resources management.

The ML Code component provides an example ML project structure with unit-tested Python modules and notebooks, allowing for quick iteration on ML problems without worrying about refactoring code for productionization later on.

The ML Resources as Code component defines ML pipeline resources, such as training and batch inference jobs, through Databricks CLI bundles, enabling governance, audit, and deployment of changes to ML resources through pull requests.

The CI/CD component uses GitHub Actions or Azure DevOps workflows to test and deploy ML code and resources, ensuring that only tested code is deployed to production and that all production changes are performed through automation.

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Here are the three modular components of the Databricks MLOps Stacks:

Databricks asset bundles and Databricks asset bundle templates are also in public preview, providing additional customization options for MLOps Stacks.

Developing ML pipelines with Databricks MLOps Stacks involves defining the structure and workflow of your machine learning project. You can use the default stack in this repository, which includes three modular components: ML Code, ML Resources as Code, and CI/CD (GitHub Actions or Azure DevOps).

These components enable data scientists to quickly iterate on ML problems, while ops engineers set up CI/CD and ML resources management. The default stack includes example ML project structure, unit-tested Python modules and notebooks, and ML pipeline resources defined through Databricks CLI bundles.

To get started with developing ML pipelines, see the detailed description and diagrams of the ML pipeline structure defined in the default stack. This will give you a clear understanding of how to organize your project and set up the necessary components for a successful MLOps workflow.

Developing ML pipelines can be a complex task, but with the right tools and approach, it can be made more efficient. An ML pipeline structure and development loops are crucial for managing the development, validation, and deployment of machine learning models.

To develop an ML pipeline, you can use a Databricks MLOps pipeline, which encapsulates the three basic steps of building, testing, and promoting a model. These steps are contained in different notebooks, such as create_model_version, test_model, and promote_model.

A Databricks MLOps pipeline can be implemented using Databricks Workflows and GitHub Actions. Every time a push in the GitHub repository's main branch or a PR is opened, a job run is created to execute these notebooks consecutively.

Here are the key components of a Databricks MLOps pipeline:

By following this pipeline structure and using Databricks Workflows and GitHub Actions, you can ensure that your machine learning models are developed, validated, and deployed efficiently.

Developing ML Pipelines requires a well-organized project structure. This involves setting up folders and files in a way that makes sense for your project.

The project has a total of 132 commits, indicating a significant amount of development work has gone into it.

The project contains several folders, including .github/workflows, doc-images, hooks, library, template, and tests. Each of these folders has a corresponding file with the same name.

One of the most important files in the project is the LICENSE file, which outlines the terms and conditions under which the project can be used.

Here's a breakdown of the folders and files in the project:

Training large language models (LLMs) requires a different approach compared to traditional MLOps. This is due to the unique characteristics of LLMs, which demand specialized hardware, transfer learning, and human feedback.

Training and fine-tuning LLMs involves orders of magnitude more calculations on large data sets, making computational resources a crucial factor. Specialized hardware like GPUs can speed up the process, but comes with a cost.

Transfer learning is a key concept in LLMs, where a foundation model is fine-tuned with new data to improve performance in a specific domain. This approach allows for state-of-the-art performance with less data and compute resources.

Human feedback is essential for evaluating LLM performance, particularly in open-ended tasks. Integrating feedback loops within LLMOps pipelines can increase model performance.

Hyperparameter tuning is critical in LLMs to reduce computational power requirements and training costs. Tweaking batch sizes and learning rates can dramatically change the speed and cost of training.

LLMs require different performance metrics, such as BLEU and ROGUE, which are more complex to calculate compared to traditional ML models.

Databricks MLOps is a customizable stack for starting new ML projects that follow production best-practices out of the box.

The default stack includes three modular components: ML Code, ML Resources as Code, and CI/CD (GitHub Actions or Azure DevOps). These components enable data scientists to quickly iterate on ML problems, while ops engineers set up CI/CD and ML resources management.

The ML Code component provides an example ML project structure, with unit-tested Python modules and notebooks, allowing data scientists to quickly iterate on ML problems without worrying about refactoring their code for productionization later on.

The ML Resources as Code component defines ML pipeline resources, such as training and batch inference jobs, through Databricks CLI bundles, enabling governance, audit, and deployment of changes to ML resources through pull requests.

CI/CD (GitHub Actions or Azure DevOps) workflows test and deploy ML code and resources, ensuring all production changes are performed through automation and only tested code is deployed to production.

Here are the three basic steps in a Databrops MLOps pipeline:

These steps are contained in different notebooks and correspond to the steps outlined in the solution overview.