The emergence of machine learning and AI has placed new demands on organizations to rapidly build, deploy, monitor and update predictive models. New disciplines like MLOps and DataOps have arisen to meet these needs by extending DevOps principles to machine learning and data workflows.
In this comprehensive guide, we will explore the key tenets of MLOps and DataOps, examine their similarities and differences, and look at how the two methodologies can work together to enable organizations to maximize the business value of their data and ML initiatives.
What is MLOps?
MLOps applies DevOps best practices like continuous integration and deployment to machine learning model development, management and monitoring. The key goal of MLOps is to accelerate the end-to-end lifecycle of ML applications, from model building to production deployment.
Specifically, MLOps aims to:
- Automate repetitive tasks involved in ML workflow including data preprocessing, model training, evaluation etc.
- Continuously integrate new code changes to shared repositories
- Continuously deploy models to staging and production environments
- Monitor model performance and drift over time
- Retrain and update models dynamically based on performance triggers
In this sense, MLOps introduces two additional concepts on top of continuous integration and deployment – continuous training and automated rollout. Together, these capabilities enable a disciplined, reliable approach for releasing ML applications.
An effective MLOps architecture requires several components working together:
- Data store – Serves as the source of truth for preprocessing and feeding data to train models
- Feature store – Manages transformations and relationships of features used for model training
- Model registry – Central repository of model versions, experiments and lineage
- Model building tools – Automates repetitive coding tasks for building models
- CI/CD pipelines – Enables smooth transition of models from development to production
- Instrumentation – Logs key performance metrics around models to inform retraining
Key components of an MLOps architecture (Image source: Valohai)
Several commercial MLOps platforms available provide these capabilities out-of-the-box including tools like Amazon SageMaker, Comet ML, and Seldon Core. Per the 2022 State of Enterprise Machine Learning report, 37% of organizations now employ MLOps platforms – indicating growing enterprise adoption.
Overall, MLOps provides the framework to transition machine learning models to live applications efficiently at scale. It binds together various parts of the ML lifecycle into an end-to-end pipeline amenable to automation.
MLOps connects steps in ML model development and deployment (Image source: Valohai)
MLOps in Action
ING, a Netherlands-based international bank, employs MLOps to accelerate churn model deployment for its 9 million customers. By operationalizing its ML pipeline with a GitHub and Jenkins based CD workflow, ING reduced deployment time to under an hour versus 2 weeks previously. The enhanced velocity enabled much more dynamic model updates and significant churn reductions.
Another adopter, Maximus, provides government health and human services programs to over 20 million beneficiaries in the U.S. Using an ML platform with MLOps embedded, Maximus can now deploy around 160 churn and propensity models seamlessly to accurately predict changing needs. This allows timely intervention and personalized support for some of society’s most vulnerable.
These examples showcase how MLOps powers measurable business impact – from financial savings to societal good. Organizations across functions stand to derive massive productivity gains as MLOps streamlines and scales the application of advanced analytics.
What is DataOps?
Whereas MLOps focuses specifically on operationalizing machine learning, DataOps deals with the orchestration, automation and observability of the broader data pipeline.
The main objectives of DataOps include:
- Automate manual steps in the data lifecycle like extraction, cleansing and transformation
- Quickly propagate any data changes downstream to analytics/reporting outputs
- Monitor data quality and lineage end-to-end
- Foster collaboration between different teams through shared self-service analytics environment
Some key components of a DataOps pipeline include:
- Data sources – APIs, databases, cloud storage, analytics where data resides
- Ingestion – Mechanisms to bring together and filter data
- Transformation – Tools and logic to reshape and enrich data
- Orchestration – Schedule, sequence and manage data microservices
- Storage and analytics – Data lakes, warehouses and visualization tools
End-to-end DataOps pipeline, its stages and underlying infrastructure components (Image source: Cast AI)
Per IDC forecasts, over 50% of large enterprises will invest in DataOps platforms like Palantir Foundry, SAS, Informatica Axon by 2025. This mirrors a growing enterprise trust and reliance on DataOps to maximize business intelligence value.
Ultimately, DataOps enables organizations to accelerate time-to-insight from their data assets. It removes inefficient silos and human latency from the data-to-knowledge pipeline.
DataOps connects and orchestrates the end-to-end data pipeline (Image source: Altexsoft)
So while MLOps deals with model and application staging, DataOps manages the interconnected data flow that feeds those models and applications.
DataOps in Action
Waste management company Republic Services manages over 15 million customers across 40+ states in the U.S. By adopting a commercial DataOps solution, the company created a 360-degree customer view – integrating data from routing, equipment, facilities and third parties. Republic unlocked sharper analytics and propensity models to predict customer needs which led to over $100 million in savings.
Another innovator, Snowflake, serves 500+ enterprise customers via its cloud-based data platform. By instituting DataOps as a core business methodology, Snowflake achieves superior agility in enhancing its platform while maintaining extreme high availability – delivering close to 0 downtime for consumers of its data/analytics offerings.
These examples illustrate the downstream value DataOps creates by harmonizing data stacks and accelerating analytics velocity for both providers and consumers of data services.
Similarities between MLOps and DataOps
Though their precise focus differs, MLOps and DataOps share some key similarities:
Collaboration
- Both promote collaboration between teams through shared data and models
- Break down isolated work between data engineers, data scientists and DevOps
- Provide self-service access with guardrails to downstream users
Automation
- Automate repetitive tasks through workflow orchestration and scheduling
- Dynamic updates in response to source code and data changes
- Lower human latency by enabling faster retraining and rebuild of assets
Standardization
- Standardize interfaces, protocols and frameworks for easier interoperability
- Treat data, models and applications as products with SLAs
- Common language around development, deployment and monitoring
These core commonalities derive from the DevOps roots shared by both MLOps and DataOps. The key difference lies in the parts of the pipeline they focus on optimizing.
Key Differences between MLOps and DataOps
While MLOps and DataOps share some high-level similarities, they diverge in some important aspects:
Domain
- MLOps deals with model building, deployment and management
- DataOps deals with data extraction, processing and distribution
Stage of Machine Learning Lifecycle
- MLOps executes after initial data preparation and cleanup
- DataOps operates on the raw data source itself
Main Actors
- MLOps: data scientists, ML engineers
- DataOps: data engineers, analytics engineering
Tools and Technologies
- MLOps: ML experiment tracking, model registries, CI/CD
- DataOps: ETL, data cataloging, data quality
Use Cases
- MLOps: operationalize and monitor predictive models
- DataOps: accelerate reporting and analysis
End Goals
- MLOps aims to improve reliability and minimize technical debt of ML systems at scale
- DataOps strives to enable faster analytics and shorter time-to-insight for end users
So while the two approaches share high-level DevOps ties and integration opportunities, they tackle different challenges in the data-to-decision pipeline.
MLOps and DataOps work at different levels with some overlap (Image source: Tecton)
Comparing Adoption: MLOps vs DataOps
Based on surveys conducted among senior data practitioners at large enterprises, DataOps enjoys broader implementation over MLOps currently:
| Methodology | Percent Adoption | Main Drivers |
|---|---|---|
| DataOps | 76% | Accelerate analytics velocity, unify data |
| MLOps | 58% | Operationalize models, enhance ML visibility |
However, MLOps adoption is estimated to eclipse DataOps by 2026:
| Year | DataOps Market | MLOps Market |
|---|---|---|
| 2022 | $1.4 billion | $0.3 billion |
| 2026 | $6.3 billion | $6.9 billion |
Two key factors account for the above forecasts:
- MLOps solutions are nascent and require organizations to reach higher maturity with ML before implementing MLOps
- Investments in managing and optimizing ML workflows will massively rise as models proliferate
In summary, while DataOps serves as an essential precursor, MLOps adoption will accelerate as enterprise ML initiatives advance towards peak effectiveness and scale.
Integrating MLOps, DataOps and AIOps
While MLOps and DataOps optimize the machine learning and data workflows respectively, an emerging discipline called AIOps focuses on model monitoring and observability.
AIOps deals with aggregating log data, tracking key performance metrics, and setting up alerts around model drift and degraded performance. In that sense, it can provide the monitoring layer on top of MLOps and DataOps:
AIOps provides monitoring and observability for MLOps and DataOps pipelines (Image source: Unraveldata)
Together, AIOps, MLOps and DataOps facilitate an integrated ML stack:
- DataOps prepares quality, harmonized data
- MLOps trains, deploys and manages machine learning models
- AIOps monitors system health and feedback signals to trigger retraining
This combined workflow powers the rapid development and continuous improvement of intelligent data products.
Though integrating the tools and systems underlying MLOps, DataOps and AIOps can prove challenging, the benefits outweigh the effort needed. Aligned together, these approaches can accelerate deploying impactful ML applications at scale.
Integrating MLOps and DataOps – In Action
Leading telecom provider Liberty Global operates a massive data stack servicing 20+ million customers worldwide. By integrating MLOps capabilities natively into their DataOps foundations, Liberty Global created an automated feedback loop between their customer data pipelines and product recommendation models. This powers faster, continuously improving ML personalization to enhance customer lifetime value – thereby boosting revenue.
Another innovator, Openspace, provides SaaS to digitize construction project quality assurance and safety processes on jobsites. By unifying DataOps and MLOps tooling with AIOps monitoring, Openspace achieves high efficiency in managing 1000s of ML experiments to meet clients‘ dynamic needs. This facilitates massive improvements in ensuring building construction standards are met systematically yet flexibly.
These examples highlight the power of aligning ML infrastructure – the speed, experimentation and customization benefits are immense.
Adopting MLOps and DataOps
For teams looking to operationalize machine learning and data workloads, MLOps and DataOps provide powerful frameworks to enable scalable, reliable delivery of predictive applications.
Here are some best practices to consider when adopting these methodologies:
Start small: Focus on automating one or two pain points rather than a wholescale rewrite
Prioritize use cases: Which models or data will derive maximum value from automation?
Phase capabilities: Rollout MLOps for experimentation first before tackling full deployment
Foster collaboration: Break down data and model ownership early between teams
Install guardrails: Control access and enable trust with testing, staging and approval gates
Instrument everything: Ingest broad telemetry into AIOps platforms to spot issues early
Evaluate tools: Proprietary ML platforms vs open source vs custom – make conscious trade-offs
Reinforce with organizational support: Changes in team structure, roles and responsibilities
Upskill teams: Training in DevOps, data and ML engineering to align with new paradigms
Key Takeaways
- MLOps focuses on operationalizing machine learning models; DataOps on streamlining data pipelines
- Both employ automation, collaboration, standardization based on DevOps principles
- DataOps enjoys broader adoption currently but MLOps projected to grow higher
- Integrating with AIOps monitoring completes the ML application optimization loop
- Aligned together, MLOps + DataOps + AIOps can multiply the organizational value of AI/ML and analytics
Conclusion
MLOps and DataOps represent two sides of the same coin – harnessing practices from DevOps to streamline the development and delivery of machine learning applications. MLOps focuses on operationalizing the model itself once data has been prepared, while DataOps deals with optimizing the sourcing, processing, and routing of data towards its various consumers.
By combining automation, collaboration, and standardization, both disciplines aim to accelerate time-to-value – either from predictive insights via MLOps or analytical intelligence via DataOps. While their tools and techniques may differ, adopting MLOps and DataOps together can help organizations scale their data and ML ambitions to drive higher ROI.
