Wonder why data science projects fail to achieve desired results? Generating positive business value from machine learning is not easy. There are numerous reasons for this:
- It is becoming difficult to maintain the tools, languages, and frameworks required in your data science initiatives.
- Despite increasing your efforts and investments in ML, your new model operationalization is not taking good speed.
- You find it difficult to track your models as model version control is manual or nonexistent.
- Executing your ML projects involves a hybrid environment – multi-cloud, hybrid cloud, and on-premises.
- Your ML projects involve multiple types of data workloads- legacy, batch, and cron jobs.
- Your IT team needs to fulfill internal requirements for ML ventures such as security, cost controls, and performance metrics.
So, what next? Forget ML initiatives and desired returns on ML investments? Or is there any way to improve the situation?
Well, there’s a solution. Heard of MLOps?
MLOps refers to the combination of two terms - machine learning and operations. It brings a set of practices to deploy and maintain ML models reliably and helps you overcome the abovementioned problems.
We have compiled an in-depth MLOps guide to help you understand MLOps concepts, evolution, components, and organizational journey to adopt MLOps. Let’s start with a quick recap to understand the rise of the MLOps concept in the ML world.
The Rise of MLOps
Machine learning has tremendous potential to transform business processes. But, a decade ago, most ML was experimental due to limited computing abilities. Despite these limitations, a few(read as large) companies that were able to productionalize their ML models could grab AI success.
Many ML initiatives stumbled when ML models transitioned from research-POC to development to production environment. It happened because of the need to rewrite models into a different language for deployment, no standardization in ML deployments, and more.
Deloitte defines this phase as the era of artisanal AI marked by the lack of scalable patterns and practices. The data science and tech community considered DevOps their initial inspiration to overcome the challenges in their ML success. However, DevOps was not the complete approach to streamlining ML initiatives as software development and model development are fundamentally different. Hence, the MLOps approach was formalized, which stole the show in the ML landscape.
What is MLOps?
MLOps defines systematic practices that unify ML development, streamline continuous delivery of performant ML models, and enable seamless collaboration between ML and other teams.
It is the machine learning version of DevOps or simply an extension to DevOps to address specifically ML elements like the changing data and the addition of new roles in data science projects such as ML engineers and data architects.
MLOps helps break the silos in machine learning projects and build an agile system similar to DevOps. This new system helps define each stakeholder’s precise roles and responsibilities to enable collaborative work practices.
MLOps aids businesses with accelerated model deployments and better compliance with regulatory requirements. It fosters reproducibility in ML experiments by reusing existing components. It supports cross-functional teams, including, C-Suite leaders, ML professionals, DevOps team, operational stakeholders, and risk-compliance team.
Modern organizations consider MLOps for the following benefits:
- A sophisticated way to scale machine learning projects
- A better way to maintain, manage, monitor, deploy and scale ML models in a production environment
- Better risk and compliance management for ML projects
- Improve collaboration between teams
- Reproducible ML pipelines and models
- Helps plan and implement a competent AI strategy
Understanding MLOps Components in ML Lifecycle
The span of MLOps integration in data science projects is not very rigid but scalable and flexible as the project grows. MLOps can include all phases from data pipeline to model deployment for some projects, whereas specific ML projects might implement MLOps for only the model deployment processes. At a high level, we can summarize the following stages and MLOps practices where most enterprises plan to integrate MLOps.
MLOps Architecture
Google Cloud’s AI Adoption framework offers a great foundation with its three stages of AI Maturity. These include:
- Tactical AI - a highly manual process
- Strategic AI - a semi-automated process that supports retraining and monitoring models
- Transformational AI – a highly sophisticated process where the entire pipeline is automated, enabling testing, experimentation, and continuous development.
Enterprises can assess their MLOps adoption maturity level to plan MLOps practices. The three-stage framework by Google helps define the organizational maturity for MLOps adoption to pave the future roadmap.
MLOps level 0: Manual process with low automation
MLOps level 0 encompasses the manual process of model building and ML workflows. This level indicates the primary stage of maturity.
Features of Level 0 MLOps
- Manual and interactive processes
- Separation of ML and engineering teams
- Suitable for infrequent model changes
- Lack of active performance tracking
- No CI/CD process
MLOps level 1: Pipeline automation
This level of MLOps ensures continuous training of the ML model by automating the ML pipeline. The strategic MLOps adoption is most suitable for solutions that work in dynamic or changing environments and experience constant shifts in different indicators. The level 1 MLOps introduces automated data and model validation steps, pipeline triggers, and metadata management to automate using new data for model retraining.
Features of Level 1 MLOps
- Faster experiments
- Continuous Training(CT) of the production model
- Modularized and reusable code for components and pipelines
- Additional components such as feature store, metadata management, and ML pipeline triggers – On-demand, schedule-based, based on new data availability, and more.
MLOps level 2: CI/CD Pipeline Automation
The most sophisticated level of MLOps implementation empowers data scientists with the freedom to explore new ideas for model architecture, hyperparameters, and feature engineering. It is more suitable for companies that demand daily, hourly, and more frequent model retraining and redeployment requirements on multiple servers.
Features of Level 2 MLOps
- Experimenting in development
- Pipeline CI and CT
- Automated triggering
- Continuous delivery
- Monitoring
MLOps Adoption - Journey from Beginner to Pro
Enterprise MLOps adoption starts with the planning of three primary pillars- people, processes, and tools. Understanding the organizational MLOps maturity status is also crucial here. We can roughly divide the MLOps adoption journey for enterprises into three stages. The following table summarizes MLOps processes and their granularity at each stage - beginner, intermediate, and pro.
MLOps For People
While understanding the journey of MLOps adoption, consideration of the second pillar of MLOps - people is imperative. As MLOps has become crucial to a successful AI strategy, it positively impacts all stakeholders of the ML development system. Check the table below for different roles and concerned MLOps requirements.
MLOps Tools
Let’s move to the final pillar of MLOps implementation - MLOps tools.
MLOps tools help in the complex ML development process while driving the best possible returns on AI investments. As ML models move from local systems to a more complex production environment. The tools streamline different tasks in ML development and help save developers time.
MLOps tools could be classified into the following broader categories:
- Data Management
- Modeling
- Operationalization
- Orchestration
Additionally, certain MLOps tools serve as unified MLOps platforms with an integrated feature set to support the complete ML lifecycle. Refer to our MLOps toolkit for more information.
Complete Your MLOps Stack
ML practitioners consider various tools to adopt MLOps easily. Creating an MLOps stack as a reference model would help here. We have provided an example of one such MLOps stack that splits an ML workflow into components. Such a template will help you finalize your MLOps tooling.
While building your MLOps stack, few tools might serve your multiple requirements for MLOps implementation, whereas some tools are focused on a specific task.
While considering the cost factor for your MLOps stack, you can choose open-source options to keep costs under the cap. On the other hand, a proprietary toolset might save time and implementation efforts.
No single MLOps stack is perfect for everyone, but the choice depends on your use-case requirements. For example, model monitoring is more business-critical to the financial and healthcare sectors. For such ML ventures, it is advisable to complete their ML stack using a dedicated ML monitoring platform such as Censius.
Notable MLOps case studies
We would like to introduce you to inspirational MLOps case studies that transformed businesses incredibly.
- Uber applied machine learning to handle different applications like demand forecasting for drivers of various locations, customer support, estimation of meal arrival time, etc. Uber created its machine learning platform, Uber Michelangelo, to scale ML and standardize workflows across different teams.
- Merck Research Labs implemented MLOps to accelerate vaccine research and discovery. With Algorithmia’s MLOps platform, the pharmaceutical company scaled its processing capability to screen compound images automatically and streamlined ML operations.
- EY, a global financial firm, implemented MLOps to accelerate model deployments using diverse frameworks and libraries. The massive increase in operationalizing models helped EY empower its customers to reduce the rate of financial crimes committed over time.
- Senko Group Holdings is an integrated logistics service partner that provides logistics business as one of the core offerings for the apparel and e-commerce sector in the Tokyo metropolitan area. They chose MLOps adoption to start AI-driven shipment volume forecasts. With the integration of the H2O Driverless AI platform, they ensured automated and seamless feature engineering. MLOps implementation helped the logistics team to streamline the operational procedures.
Recommended reading: MLOps case studies
Closing Thoughts
Thank you for reading this detailed guide. We hope you can build a solid foundation of MLOps. You can also check our MLOps resources – MLOps Wiki and MLOps toolkit to learn about the different terms and tools that are usually popular in this space.
For any queries regarding MLOps stack selection and ML monitoring, you can reach us at hello@censius.ai or sign up for our AI observability platform demo.
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