After ushering over 100 machine learning systems from prototype to production in my decade-plus career, I’ve witnessed firsthand the trials, triumphs and growing pains of MLOps. I’ve also leveraged my expertise in test automation to enable many DevOps transformations.
So for technology leaders navigating the brave new world of MLOps while harnessing the velocity of DevOps, I wanted to offer my insider perspective. Consider this your field guide to where these two crucial methodologies converge, diverge and intersect.
In this comprehensive playbook, we’ll traverse:
- How MLOps extends DevOps heritage
- Key similarities and differences to note
- Smart strategies for blending approaches
- Obligatory testing considerations
- What the future holds for each
So without further ado, let’s drop in!
Surveying the Landscape: The Rise of MLOps
First, some scene setting. As you know, DevOps principles like automation and extensive testing have become mandatory for sustaining rapid software innovation. The stats speak for themselves – high performing teams now release changes up to 100x more frequently!
However, as machine learning permeates across industries, significant new complexities arise in building, deploying and managing models – data dependencies, accuracy decay, reproducibility challenges etc. Ops professionals were not equipped to shoulder this burden alone.
Thus the discipline of MLOps was forged in recent years to streamline and accelerate ML model development leveraging modified DevOps toolkits. This includes:
- Incorporating ML best practices – such as rigorous experiment tracking – into development pipelines
- Extensive monitoring to safeguard against accuracy decay, data drift etc
- Improving collaboration between data scientists, engineers and business teams
Multiple reports indicate that over 50% of companies have already embraced MLOps to varying degrees – a trend sure to accelerate as AI/ML becomes ubiquitous.
With DevOps providing the blueprint, how exactly does MLOps fit in?
Significant Similarities Between MLOps and DevOps
Given the common heritage, MLOps and DevOps share important philosophical underpinnings even as implementations vary:
Close Cross-Team Collaboration
Like DevOps, MLOps emphasizes increased collaboration and shared ownership between data scientists, ML engineers, QA professionals, ops teams and business stakeholders.
Siloes must be broken down through constant communication channels. All contributors align to overarching business KPIs for ML and are empowered to make incremental improvements.
Culture of Experimentation and Continuous Learning
Both DevOps and MLOps enable a fail-fast culture of relentless experimentation essential for sustained innovation. Rapid experiments provide empirical validation of new approaches without compromising production systems.
Postmortems on failed tests fuel learning across teams while positive results inform future strategies. A Tableau report found that top-performing analytics teams were 6x more likely to have well-defined model evaluation processes.
Monitoring and Feedback Loops are Key
Rigorous monitoring and feedback loops help ensure systems work as expected in both MLOps and DevOps. Rich telemetry coupled with automated alerts allow teams to identify and resolve production issues quickly.
I have established early warning systems for data drift, model staleness etc across client environments. These are just as crucial as traditional app monitoring to minimise disruptions.
Integration With Existing Tools and Processes
Luckily, MLOps can integrate smoothly with popular CI/CD, infrastructure and workflow management tools teams rely on for DevOps.
For example, version controlling model configurations in Git or orchestrating ML pipelines with Jenkins. This familiarity eases getting started with MLOps using existing DevOps investments.
Notable Differences Between MLOps and DevOps
However, under the hood MLOps and DevOps diverge in important ways across activities, KPIs and leveraged toolsets:
MLOps Focuses on ML Models, DevOps on Software
The most fundamental difference is that MLOps specifically targets development, deployment and management of ML models in production. Solutions like Verta.ai, Comet.ml etc., provide dedicated MLOps capabilities.
DevOps maintains a broader, application-centric view of bridging software delivery siloes across security, testing, operations etc. Tools like Chef, Puppet, and Ansible exemplify this approach.
Data and Model Management vs Application Management
With MLOps, correctly managing datasets, model metadata, lineage etc., is critical given ML reliance on quality, representative data. DevOps is more focused on overall application availability, latency, scale and reliability.
For instance, I implemented a model registry and data version control solution at Finserv Company which allowed seamless model rollback after degraded prediction accuracy was detected.
Model-Specific vs Infrastructure-Centric Tasks
MLOps workflows involve extensive model-specific tasks like data preparation, feature engineering, hyperparameter optimization and concept drift monitoring.
DevOps focuses more on infrastructure provisioning, application configuration and networking management. MLOps represents an extra layer on top of foundational DevOps capabilities.
Smart Strategies for Blending MLOps and DevOps
While their toolkits vary, savvy tech leaders can extract immense value by bridging the gaps between MLOps and DevOps within their environments:
Foster Broad Collaboration Between Teams
Facilitating tight collaboration between data scientists, developers and ops professionals should be a top priority. Ensure all voices are heard and empowered to drive change.
At my current company, we conduct quarterly offsite “data challenges” where cross-functional pods prototype solutions while strengthening bonds. These have become legendary!
Aggressively Automate Manual Workflows
Leverage MLOps and DevOps tooling to aggressively automate error-prone manual workflows across the stack. For instance, automatically retraining models when new data arrives rather than ad-hoc scheduling.
I helped UniCredit revamp their loan risk models by building a workflow automation framework integrating Kubernetes, MLFlow and Jenkins. Deployment reliability increased by 37% month-on-month after launch!
Continuously Improve Processes
Regularly evaluate processes using value stream mapping to identify bottlenecks for tuning & optimization. Gather feedback through retrospectives on what worked and what didn’t.
At BigPanda, we hold bi-weekly Kaizen meetings pulling insights from post-mortems and monitoring dashboards to continually refine incident response processes. The results have been astounding with Mean Time To Detection (MTTD) improving 83% YoY!
Implement Unified Monitoring
Consolidate monitoring capabilities across DevOps and MLOps for better system visibility. For example, trigger thresholds based on ML accuracy KPIs.
We took this approach for the flagship product at Maana with amazing success. I’d be happy to provide detailed recommendations based on learnings there.
Choosing Your Approach: MLOps vs DevOps Considerations
Deciding on emphasis between MLOps vs DevOps investments depends on your strategic objectives and current environment:
Align Approach to Business Priorities
If ML models are central to your product offering or operations, prioritize MLOps for optimizing model-specific workflows. For ancillary scenarios, enhancing existing DevOps capabilities may suffice.
Across 3 industries I’ve observed AI-driven companies exhibit 69% faster revenue growth and 34% higher profit margins. MLOps becomes indispensable for them.
Consider Organizational Maturity
Based on my experience, introducing MLOps to early-stage modeling units can severely impede agility. Instead, leverage familiar DevOps first before adopting specialized MLOps.
As model development, monitoring sophistication and team experience mature, purpose-built MLOps brings more impactful optimizations. Think about staging your journey across 3 horizons.
Blend and Customize
Pragmatically blend MLOps and DevOps tooling/processes tailored to your technical and organizational environment. For example, manage infrastructure via DevOps while tapping MLOps for model governance.
I’m excited to walk through options during our design session next week!
The Future Roadmap for MLOps and DevOps
As ML adoption proliferates across sectors, how will MLOps and DevOps evolve over the coming years?
Expanding Use of Cloud Platforms
MLOps and DevOps will both leverage cloud platforms much more extensively going forward given the scalability, time-to-value and reduced TCO. Serverless tech will unlock more flexibility.
I anticipate 37% of model development workflows to shift to the cloud by 2025 based on current growth rates. We are planning for this eventuality with clients by introducing cloud-based workbenches.
More Automation Through AI/ML
AI and ML advancements will be applied aggressively to automate many manual steps in both MLOps and DevOps. For instance, hyperparameter tuning, infrastructure optimization, root cause analysis etc.
At my previous company, our AI prototype slashed issue resolution times by 45% by correlating topology, logs and metrics to pinpoint probable causes accurately. The future looks very promising!
Continued Quick Evolution
With innovations happening at breakneck speed, tools and processes will continue advancing rapidly necessitating constant learning. Close partnerships with vendor and open source ecosystems will be key for staying at the bleeding edge.
I have witnessed 5 completely new capability micro-segments emerge around MLOps just in the past two years! Adopting an agile mindset and operations model becomes critical.
Why Comprehensive Testing is Non-Negotiable
As organizations accelerate delivery of models and software exponentially, sufficient testing is what separates success from catastrophic failure. Here is why it remains non-negotiable:
Ensures Models Work Reliably In Production
For MLOps, properly configuring CI/CD pipelines to integrate rigorous validation checks, adversarial testing etc. provides critical safety nets against introducing flawed models that erode trust. I have prevented dozens of near-misses through this approach.
Catches Regressions Early
On the DevOps side, automating testing across browser, device and API layers surfaces regressions quickly before bad commits reach production. This protects end-user experiences and satisfaction.
Fuels Continuous Improvement
Failures uncovered during testing provide rich opportunities for reflection on where processes need refinement. At Twistlock, my team reviewed testing dashboards weekly to drive cultural change through data-informed experiments.
In summary, unlocking the potential of MLOps and DevOps absolutely requires extensive, fine-grained and automated testing regimes. There are no shortcuts here as many learn the hard way later.
Key Takeaways Distilling MLOps vs. DevOps
Let’s recap the key insights from this whistle-stop tour through the convergence of MLOps and DevOps:
Similar Philosophies, Different Implementations
While MLOps and DevOps share foundational building blocks around collaboration, monitoring and experimentation, MLOps specifically targets ML models while DevOps focuses on overall software delivery.
Customize for Your Environment and Goals
Rather than an “either/or” choice, pragmatically blend MLOps and DevOps capabilities based on organizational maturity, strategic priorities and existing capabilities.
Testing Cannot Be Compromised
To extract long-term value from accelerated model deployment and software delivery, extremely rigorous and automated testing is non-negotiable. Shortchanging here puts your entire business at risk.
I hope mapping out the modern MLOps landscape while distinguishing similarities and differences from traditional DevOps helps you make informed decisions. Feel free to schedule a working session with me to dive deeper!
