MLflow and Databricks as a Comprehensive Solution to AI/ML Workflows

How MLflow and Databricks Address AI/ML Challenges?

Author: Andrew McQueen

01 January, 2025

Today, organizations face several challenges regarding implementations of artificial intelligence (AI) and machine learning (ML) solutions at scale. Some key pain points might include fragmented workflows, reproducibility issues, scaling limitations, model management and governance, and deployment bottlenecks. This blog will discuss how MLflow and Databricks offer a comprehensive solution to streamline these processes, improve collaboration, enable scalability, and turn organizations’ efforts into effective, tangible outcomes. 

Pain Points in AI/ML Solutions 

• Fragmented Workflows: When teams rely on disjointed tools and platforms for different stages of the ML lifecycle, issues in data preprocessing, experimentation, or model deployment can create inefficiencies in—and poor collaboration among—teams. 
• Reproducibility Issues: Without proper experiment tracking, it becomes challenging for teams to replicate results or track down specific configurations and data used in the modeling process. 
• Scaling Limitations: In the cases of large datasets or lacking the infrastructure to scale well, limitations can hinder efficiency in solutions. 
• Model Management and Governance: This is can—and often is—an issue that arises at multiple levels, whether it be ingestion, preprocessing, modeling, or later in the workflow. 
• Deployment Bottlenecks: These often arise when transitioning ML models from development to production. Lengthy manual processes for preparing and deploying models, inconsistent environments, and more can delay the time it takes to deliver actionable insights. 

MLflow 

MLflow is an open-source platform that simplifies the ML lifecycle by addressing experimentation, reproducibility, and deployment challenges. It comprises four main components: Tracking, Models, Model Registry, and Projects. This blog will go through each component and will later discuss how the previously mentioned pain points are addressed. 

MLflow Tracking is organized runs and experiments. Runs are code executions which automatically or manually log metadata and artifacts regarding the code that was executed. For example, when training a regressor, we could log model parameters, metrics, and a residual plot. Experiments are groups of related runs, such as runs for training the previously mentioned regressor using different sets of parameters. Through the CLI, we can use any of the logged information later in our code, whether it is the same notebook or not. The UI is easily navigable, especially when runs and experiments are organized in a thoughtful way, which can be helpful for users who are not familiar with the code used in a run or programming in general. Most changes can be made through both the UI and programmatically. Tracking with experiments and runs allows us to easily compare models and have an organized history of our modeling processes. 

MLflow Models allow you to manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms by formatting the models to be understood by downstream tools. Model flavors act as a bridge between models and deployment tools to help avoid manual integration of the two. Built-in deployment tools support exists for popular libraries and frameworks (e.g., Scikit-learn and PyTorch), as well as default model interfaces for Python and R functions. 

The Model Registry is a centralized repository for organizing, versioning, reviewing, approving, and deploying models. This serves as a collaborative and governable environment for data scientists, ML engineers, and stakeholders. Versions are assigned to models when they are registered, helping with model lineage and reproducibility. As development goes on, models will be moved between four different stages: None, Staging, Production, and Archived. Registered models retain the information from their experiments, making it easy to track down the source code, see previous iterations of the model, and view any logged metadata or artifacts. The Model Registry adds options for improved documentation, like descriptions for models and details on the modeling process.  

MLflow Projects are used for packaging reusable and shareable code. These ensure that the code can be executed consistently across different environments. In line with the previously described components, Projects offer many benefits regarding reproducibility, scalability, and version control. 

These components all come together to create a collaborative environment that businesses can use to scale machine learning development while maintaining transparency and version control. Databricks and MLflow are a good match because they are focused on avoiding many similar issues teams may face in development. 

Databricks 

This blog will not cover Databricks in depth, though you can find more information from our other blogs, here. Databricks integrates MLflow into the Lakehouse architecture. The Lakehouse is a unified solution for storing data of all types without having isolated warehouses and lakes. Databricks is built on Apache Spark and provides distributed computing to enable fast processing of larger-scale datasets, as well as Databricks SQL. The platform integrates with cloud storage services and common tools for ingestion. Data at all levels is governable through Unity Catalog, which has fine-grained access control down to the column and row level. 

When it comes to ML development, Databricks uses workflows and notebooks, enabling teams to collaborate on data preparation, feature engineering, and exploratory analysis. Its ability to handle both real-time and batch processing gives organizations the flexibility to address a diverse range of challenges. The integration with many cloud providers, like AWS, Azure, and Google, allows teams to scale their workloads while keeping their data secure and compliant. Databricks provides solutions to more of the common pain points, making it useful for organizations looking to develop these systems. 

MLflow and Databricks’ Shared Goals 

Both MLflow and Databricks are built to address the critical challenges of ML workflows. While MLflow is focused on the machine learning lifecycle, Databricks provides the resources for achieving scalability. They each have shared goals regarding collaboration, reproducibility 

Conclusion 

Modern organizations face numerous challenges regarding machine learning workflows. From managing fragmented workflows to ensuring compliance and reproducibility, these challenges can significantly slow development and innovation and diminish the value of the solution. The combination of MLflow and Databricks can be used to help avoid the issues. 

MLflow streamlines the machine learning lifecycle by offering tools for tracking experiments, managing models, and ensuring reproducibility across environments. This allows data scientists to focus on building impactful models without worrying about operational inefficiencies. Databricks complements MLflow with the Lakehouse architecture and a unified platform that offers scalable compute, integration tools, collaborative workspaces, and Unity Catalog for data governance. 

Together, the two deliver a comprehensive ecosystem for developing, deploying, and governing machine learning solutions at scale. At Xorbix, we use these tools for developing AI/ML workflows to ensure quality results in manufacturing, insurance, healthcare, and more. 

Read more related to this blog: 

1. The Benefits of Agile Development for Small Businesses 
2. Data Analytics Strategies for Enhanced Growth in Small Businesses 
3. The Role of Automation in the Digital Transformation of Small Businesses