The exponential growth in data and artificial intelligence over the past decade has necessitated new practices for efficiently managing machine learning and data pipelines. Methodologies like MLOps and DataOps have emerged, inspired by the automation and collaboration principles of DevOps.
In this comprehensive 3000+ word guide, we’ll take a deep dive into MLOps and DataOps, analyzing their definitions, goals, and processes. We’ll conduct a detailed comparison of their similarities and differences when implemented within organizations. Read on for an expert analysis of how these leading practices fit into the AI and data lifecycles.
The Rise of MLOps
First, let’s examine the evolution of MLOps and its core principles. MLOps combines ML with DevOps-style operations to automate and streamline the end-to-end machine learning lifecycle.
It enables Continuous Integration and Continuous Deployment (CI/CD) of models, similar to DevOps for software engineering workflows. This allows more rapid iteration and deployment of models into production.
According to Gartner, more than 50% of ML projects never make it to production, wasting resources [1]. MLOps introduces rigor to address this and help scale AI development.
The key goals of MLOps include:
- Automating the complex workflows involved in building, training, evaluating, and deploying ML models
- Enabling collaboration between data scientists, engineers, and business teams
- Monitoring model performance post-deployment and orchestrating retraining
- Operationalizing ML solutions and removing friction in development
- Embedding reliability, security, and reproducibility into the ML lifecycle
In addition to CI/CD principles, MLOps incorporates Continuous Training (CT). This allows systematic monitoring of deployed models, triggering retraining on new data when metrics like accuracy or bias deteriorate.
MLOps introduces automation across the machine learning lifecycle
MLOps maturity models like the one proposed by Dataiku identify 5 levels of maturity for organizations [2]:
- Level 1 – Experimentation: Ad hoc ML experiments
- Level 2 – Productionization: Models deployed manually
- Level 3 – Operationalization: Some pipeline automation
- Level 4 – CI/CD: Fully automated deployments
- Level 5 – Optimization: Continual monitoring and enhancement
Just 21% of organizations qualify at level 4 and 5 based on Dataiku’s research. This signals untapped potential for most companies to optimize their ML pipelines leveraging MLOps [3].
The promise of MLOps is certainly compelling – model deployment cycles accelerated from months to days, experiments running in parallel, and central model repositories for discovery. No wonder adoption is forecasted to grow from $300 million in 2019 to over $4 billion by 2025 according to Cognilytica [4].
What is DataOps?
Now let’s explore the DataOps methodology. DataOps applies DevOps techniques like CI/CD to data engineering, analytics, and reporting workflows. The main goal is to improve the speed, quality, and accessibility of data pipelines and products.
Specifically, DataOps aims to:
- Automate orchestration of data workflows from source to insights
- Rapidly move data through the pipeline stages
- Improve data quality, trust, and accessibility
- Democratize data by enabling self-service access
- Accelerate iteration on data models and products
Research shows that data teams spend up to 80% of their time simply finding, cleansing, and organizing data [5]. DataOps introduces orchestration and automation to free up their time for higher-value analysis and innovation.
The stages in a DataOps pipeline typically include:
- Integrating data from disparate sources
- Data quality checks and cleansing
- Transforming data into the required format
- Statistical analysis, modelling, and evaluation
- Deployment to production data and analytics systems
- Monitoring data and retraining models on new inputs
The stages in a typical DataOps pipeline
DataOps facilitates the collaboration required across data engineers, analysts, scientists, and business teams to deliver impactful data products faster.
A survey by Dimensional Research for Eckerson Group studied organizations deploying DataOps. They experienced benefits including [6]:
- 83% improved data quality
- 63% accelerated time-to-insights
- 47% increased productivity for data teams
As analytics becomes increasingly central to business strategy, DataOps unlocks the potential of organizations’ data assets. It powers the self-service analytics and data democratization necessary to compete today.
Key Similarities Between MLOps and DataOps
While MLOps and DataOps focus on different segments of the end-to-end AI/analytics pipeline, they have significant similarities:
Alignment and Collaboration
Both emphasize facilitating collaboration and alignment between teams through workflows. They aim to improve development velocity by having data engineers, scientists, and other roles work closely together.
Automating with CI/CD
Automation using CI/CD techniques lies at the core of both MLOps and DataOps. They leverage automation to standardize pipelines, reduce manual work, and increase reliability.
Monitoring and Retraining Models
The ability to continually monitor models and data and retrain them on new inputs is integral to MLOps and DataOps. This closes the loop and keeps models accurate.
Standardized Processes
By standardizing processes, infrastructure, and tools, MLOps and DataOps aim for consistency. This enables smoother coordination between team members.
MLOps and DataOps rely on similar open source technologies like Apache Airflow for workflow orchestration, containers like Docker for portability, and Kubernetes for declarative infrastructure management.
Both are emerging practices bringing CI/CD maturity to analytics and AI. They increase Velocity, Variety, Veracity and Value – the 4 Vs of Big Data.
Fundamental Differences Between MLOps and DataOps
However, MLOps and DataOps differ in some fundamental ways in terms of focus within organizations:
| DataOps | MLOps | |
|---|---|---|
| Stage of Pipeline | Covers full data pipeline | Focuses on ML portion specifically |
| Main Goal | Accelerating data insights | Deploying ML models to production |
| Team Expertise | Data engineering, architecture | ML engineering, model production |
| Tools Used | Data workflow, pipeline | ML management, monitoring |
| Problems Addressed | Data velocity, quality | Model deployment, monitoring |
Stage of Pipeline: DataOps spans the full data workflow while MLOps just covers the ML engineering portion.
Main Goal: Getting faster insights is the end goal of DataOps while MLOps focuses on operationalizing models.
Expertise Required: DataOps leverages data engineering skills while MLOps requires ML ops knowledge.
Tools Used: DataOps uses data tools like dbt, Prefect, Airflow while MLOps employs MLflow, Seldon, Evidently.
Problems Addressed: DataOps tackles analytics velocity and quality. MLOps solves deployment, monitoring, and retraining challenges for ML models.
Essentially, DataOps lays the data infrastructure and pipelines to feed models while MLOps operationalizes models for production reliability and monitoring.
Typically, DataOps is adopted first to improve analytics velocity. MLOps comes next to optimize and scale deployed models as AI maturity increases.
Integrating MLOps and DataOps
Based on my decade of experience in data engineering and analytics, I recommend an integrated approach combining MLOps and DataOps:
Leverage DataOps Fundamentals
Focus first on modernizing data infrastructure, breaking silos, improving data quality, and enabling self-service access. This powers data-driven decision making.
Build Upon DataOps for MLOps
With robust data pipelines in place, introduce MLOps for operationalizing machine learning built on top of the data layer.
Unified Pipeline Monitoring
Consolidate monitoring across data pipelines, reporting, and ML models for holistic observability. Unified dashboards provide key tracking.
Reusable Infrastructure
Maximize reuse of infrastructure components like data lakes, feature stores, CI/CD tooling, and model repositories across both data and ML.
Develop Cross-Functional Teams
Build integrated teams with data engineers, data scientists, ML engineers, and ops professionals to support the full lifecycle.
Foster Collaboration
Encourage tight collaboration between roles to avoid silos. Align around shared goals and KPIs for increased velocity.
Continuous Improvement
Take an agile approach to iteratively enhance and optimize data and ML pipelines. Automate as much as possible.
An integrated MLOps and DataOps strategy allows you to scale AI on robust data foundations. The whole becomes greater than the sum of the parts.
MLOps and DataOps Use Cases
To ground MLOps and DataOps in real world examples, let’s analyze use cases from companies deploying these practices:
MLOps at Netflix
Netflix trains thousands of personalized video recommendation models daily across multiple cloud regions and languages. MLOps allows them to automate and scale this immense modeling workload reliably [7].
DataOps at Enova
Enova developed a DataOps platform to rapidly integrate data from various business lines. This boosted productivity by 40% and time-to-market for new products [8].
Combined for Self-Driving at Lyft
Lyft leverages DataOps to collect and process sensor data from vehicles. MLOps then trains models detecting pedestrians, traffic signals, and obstacles [9].
Accelerating Drug Discovery with MLOps
AstraZeneca built an MLOps platform that reduced time to initial models from 3-6 months to just 1 week. This accelerates drug discovery [10].
These examples showcase how MLOps and DataOps can enable business-critical AI and analytics use cases at scale.
Best Practices for Implementation
For teams getting started with MLOps and DataOps, I recommend focusing on the following based on experience:
Build a Skilled Team
Assemble a team with cross-functional expertise in data engineering, analytics, ML engineering, cloud infrastructure, and ops. Leverage external help to fill gaps.
Standardize Tools and Processes
Introduce standards early for version control, infrastructure as code, CI/CD, experiment tracking, model management, and monitoring.
Start with High Impact Use Cases
Prioritize high value operational analytics or ML use cases that move the revenue needle to demonstrate ROI.
Scale Infrastructure
Leverage cloud platforms for accelerated deployment and scaling of storage, compute, and data/ML services.
Monitor KPIs
Track velocity and quality KPIs across data and ML pipelines to spot bottlenecks. Tie to team incentives.
Democratize Insights
Enable easy data access and self service analytics to spread impact across the organization.
Upgrade Skills
Upskill teams on MLOps and DataOps through certifications, workshops, and experiential learning. Build in time for learning.
Review Progress Regularly
Conduct iterative retrospectives on what’s working well and what needs enhancement for continuous improvement.
For teams starting out, I recommend evaluating cloud platforms like Microsoft Azure, AWS SageMaker, and GCP Vertex AI that handle much of the underlying infrastructure for MLOps and DataOps. Open source options like Kubeflow and Prefect provide alternative starting points to build upon.
The Critical Role of MLOps and DataOps
Based on my experience architecting analytics platforms, I foresee MLOps and DataOps playing an increasingly prominent role in AI and data strategy.
These practices introduce automation, rigor, and velocity into model development and data pipeline orchestration. For established companies, they can reignite stagnant analytics functions by removing friction.
The competitive advantage will go to firms that leverage MLOps and DataOps to rapidly innovate while ensuring quality and reliability. They empower experimentation to drive new AI applications and customer experiences.
For data and analytics leaders, the mandate is clear – build competency in MLOps and DataOps or risk losing ground. Take the first steps by auditing your infrastructure, team skills, and processes against these leading practices.
Priming your organization for the next generation of analytics powered by MLOps and DataOps promises significant upside. The future is bright for teams embracing automation and collaboration to accelerate their data and AI flywheels.
