An MLOps platform enables streamlining and automating the entire ML lifecycle, from model development and training to deployment and monitoring. This helps enhance collaboration between data scientists and developers, bridge technological silos, and ensure efficiency when building and deploying ML models, which brings more ML models to production faster.
When it comes to scaling your MLOps operations, a high-quality, reliable and effective MLOps platform is essential for growth. Some organizations might opt to build one themselves, while others will buy a commercial solution and yet a third group will take a hybrid approach that combines both building and buying. In this blog post, we explore what is required from you for each option and provide tools that will help you make the right choice for your organization to scale your ML and AI activities.
Building an MLOps platform might be the right choice for your organization’s growth plans in certain scenarios. Here are some considerations to take into account when you’re contemplating whether to build such a platform.
- Specialized Requirements - If your ML workflows have unique requirements that off-the-shelf solutions can't address, building your own platform will enable you to customize the solution to your specific needs.
- Complex Integrations with Existing Systems - If you have a complex ecosystem of tools and databases, building your own MLOps platform may make it easier to achieve seamless integration, as opposed to adapting a commercial solution and getting it to fit into your existing architecture.
- Data Privacy and Compliance - Building your own platform can offer more control over data handling and compliance needs.
However, there are MLOps vendors that can also accommodate these needs.
Buying an entire MLOps platform or purpose-built components can provide multiple growth and scalability advantages to organizations. These include:
- Quicker Time-to-Market - Commercial solutions enable deploying ML models quickly and efficiently, without waiting for you to build a platform (see below - how long it takes to build an MLOps platform). This can significantly reduce the time-to-market for your ML-based products.
- MLOps Expertise and Technology Quality - An MLOps platform is built by MLOps experts who have industry experience and is based on best practices. In many cases, they continue to offer support and maintenance. This lets you benefit from their expertise and wisdom when using the platform.
- Budget Constraints - The total cost of ownership of an MLOps platform is lower than building in-house. While commercial solutions have licensing fees, building costs include development, maintenance, storage and schedule delays.
- Scalability - Commercial MLOps platforms are generally designed to scale. This is enabled through features like resource allocation, load balancing and more.
- Business Focus - Buying allows organizations to focus development resources on areas that are their business’s core focus, rather than on supporting solutions.
- Vendor Ecosystem - Many commercial platforms offer integrations with a wide range of other tools and platforms, providing a comprehensive solution.
- Regulatory Compliance - Many commercial solutions are designed to meet specific regulatory standards out of the box, which can be a significant advantage if you operate in a regulated industry.
A hybrid approach to MLOps infrastructure combines the benefits of building and buying. This approach can offer a balance between the customization and control that a built solution offers, with the time saving, technical expertise, and costs that a bought platform enables. To make an informed decision about whether a hybrid approach is the right choice, conduct a thorough analysis of your requirements, budget, and available resources. It’s also recommended to consider factors like scalability, maintenance, and long-term support.
Building a quality MLOps platform requires careful planning, a range of skills, and a robust set of features. Here's a breakdown of what you'll need:
Successfully developing, launching and maintaining an MLOps platform requires a diverse and deep technological skill set. This includes ML experts who can develop, train and deploy models, DevOps engineers for the operational aspects, including CI/CD pipelines, monitoring, and ML infrastructure management, developers to build the platform's UI, APIs, and other software components, and data engineers for managing data pipelines, storage, and ensuring data quality.
An MLOps platform requires infrastructure hardware or cloud resources for model training, inference, and data processing, a reliable and scalable storage solution for both raw and processed data, and secure and efficient networking solutions to handle data transfer and communication between components.
Your MLOps platform needs the following fundamental features and MLOps technologies:
- Version Control - For both code and data to track changes and enable collaboration.
- Automated Pipelines - CI/CD pipelines for automated testing, building and deployment of ML models and the MLOps cycle.
- Monitoring and Logging - Real-time monitoring of model performance, system health and logging, for debugging and traceability.
- Model Registry - A centralized repository for storing and managing different versions of ML models.
- Data Validation - Tools to ensure the quality and integrity of incoming data.
- Scalability - A solution that enables easily scaling horizontally or vertically.
- Security - Features like access control and data encryption and the ability to comply with relevant regulations.
- Documentation - Comprehensive documentation for developers and end-users.
- UI - An intuitive UI for managing models, pipelines and data.
- APIs - Well-designed APIs for integration with other systems and tools.
- Testing and Validation - Unit test, integration tests and performance tests, to name a few.
- Monitoring - Automating drift detectionto keep models optimized and accurate in the changing environments.
- Feature Store - A central hub for producing, sharing and monitoring features.
In addition, you will need to plan a timeline and a budget (see next sections).
The time required to build a scalable MLOps platform will vary widely depending on several factors, including the complexity of the platform, your team’s skill level, and the specific requirements of your organization. It could take anywhere between six months and two years or more. Here's a general idea of what to expect:
First, make sure to properly plan the development process. This includes requirement gathering, defining the architecture, and setting up the development environment.
Most of the building time will be spent on development. The actual length of time will vary depending on the platform’s architecture and features. Development includes setting up the basic infrastructure, including compute resources and data storage, developing basic features and MLOps components, like version control, automated pipelines, and monitoring, and adding advanced features like model registry, data validation, a feature store and scalability options. In addition, if you have unique requirements, the time to develop these will need to be added to the schedule.
Once the platform is developed, it’s time to test it to ensure its robustness. This includes testing each component individually (unit testing), as an integrated whole that can work together (integration testing) and to ensure the system can handle the expected volume of data and requests (load testing).
Once everything is built and tested, you'll need additional time to deploy the platform and fine-tune its performance. This will be based on real-world usage.
Building the platform is not the end. Now, it’s time for ongoing maintenance, updates, and potentially adding new features, which is a continuous effort.
A moderately complex MLOps platform could cost anywhere from a few hundred thousand dollars to multiple millions. Here's a breakdown of some of the primary cost components you should consider:
- Salaries - For data scientists, DevOps engineers, software developers, and data engineers.
- Consultancy fees
- Hardware - Servers for data storage, model training, and running the platform.
- Cloud Services - Compute instances, data storage, and data transfer.
- Operating Systems
- Database Licenses
- Third-Party Tools - Specialized software for data preprocessing, analytics, or other tasks.
- Version Control
- CI/CD Tools
- Testing Environments
- Testing Tools
- Security controls
- Resource Scaling
When considering which vendor to choose for purchasing an MLOps platform that will support your growth, there are several key factors that can help you make an informed decision that aligns with your organization's needs. These include:
- Ensure the platform offers essential features like version control, automated pipelines, monitoring and logging.
- Look for additional functionalities like model registry, data validation, a feature store and auto-scaling.
- Check if the platform allows for customization to meet your specific requirements.
- The platform should be able to scale both in terms of computing resources and the number of models it can manage.
- Ensure the platform offers robust encryption options for data at rest and in transit.
- Look for features like identity authentication and data management.
- If you're in a regulated industry, make sure the platform complies with relevant standards.
- Understand the cost structure, including tier-based pricing or additional fees.
- Consider the costs associated with implementation, training and ongoing maintenance.
- Look for reviews or case studies that can provide insights into the platform's reliability and performance.
- Check the level of support offered and whether the vendor provides regular updates and maintenance.
- The platform should offer robust APIs and SDKs for easy integration with your existing systems.
- Check if the platform can easily integrate with other tools you're using, like data storage solutions, analytics tools, or CI/CD pipelines.
- Implement a small-scale proof of concept to test the platform in a real-world scenario.
- Ensure there is comprehensive documentation for smooth implementation and troubleshooting.
- Check if the vendor offers training resources or programs to help your team get up to speed.
With a hybrid MLOps approach, you can build your own infrastructure for certain components, such as data storage and processing, while buying other components, such as model management and deployment. This approach can give you more control and flexibility, while potentially reducing the overall cost of your MLOps infrastructure.
Here's what such a solution might look like:
- Compute and Storage - Purchase cloud services from a public cloud provider.
- Networking - Use commercial networking solutions for secure and efficient data transfer.
- Code Versioning - Use open-source tools like Git for code versioning.
- Data Versioning - Build a custom solution for versioning your unique data sets, or use commercial data versioning tools tailored to ML projects.
- Pipeline Orchestration - Use a commercial CI/CD solution that integrates with multiple platforms and tools.
- Custom Scripts - Write custom scripts for specific stages in the pipeline that require specialized handling, such as unique testing procedures or data preprocessing steps.
- Training Frameworks - Utilize open-source machine learning frameworks like TensorFlow or PyTorch for model training.
- Deployment - Use a commercial solution for model deployment that offers features like auto-scaling, monitoring, and rollback capabilities.
- Basic Monitoring - Use commercial tools for basic system and application-level monitoring.
- Advanced Analytics - Build custom analytics dashboards for monitoring model performance metrics that are specific to your business needs.
- Basic Security - Use commercial security solutions for standard security features like data encryption and access control.
- Compliance - Build custom modules to handle industry-specific compliance requirements that off-the-shelf solutions can't cover.
- Basic UI - Use commercial solutions for basic dashboard functionalities.
- Custom Features - Add custom-built features or modules that are specific to your organization's needs.
- APIs - Use commercial API gateways for handling standard API traffic.
- Custom Integrations - Build custom integrations for internal tools or specific third-party services that are crucial to your workflow.
- Feature Store - Use a commercially available feature store to reuse features.
|MLOps Expertise||Vendor, industry-wide||In-house||In-house|
|Scalability||On-demand||Requires development||Requires development|
|Ongoing Maintenance||Vendor is in charge||Business is in charge||Business is in charge|
|Customization||Depends on the vendor||Yes||Partial|
|Complex integrations||Depend on the vendor||Yes||Yes|
|Ownership and Governance||Vendor||Company||Company|
|Cost||Licensing fees||Development, deployment, maintenance||Development, deployment, maintenance|
|Risks||Vendor lock-in, vendor going out of business||Technological failure||Technological failure|
Choosing the right approach for acquiring an MLOps platform will greatly impact the way you serve models and bring data science to life. To learn more about Iguazio’s MLOps platform, click here.