Multi-cloud Machine Learning Strategies
Multi-cloud AI/ML refers to building, training, and deploying artificial intelligence and machine learning models across multiple cloud platforms and providers, and is an integral part of a robust machine learning strategy. Instead of relying on a single cloud vendor, organizations can take a multi-cloud approach to their AI initiatives, replying on general clouds like Amazon’s AWS or Google’s GCP, as well as specialty AI-specific clouds like CoreWeave for competitively priced and highly available GPUs. This has become increasingly important as AI permeates more areas of the business and cannot be confined to a single cloud environment.
There are several key reasons why multi-cloud AI/ML has become crucial:
- Avoids vendor lock-in: Organizations avoid relying too heavily on any single cloud provider, which gives them more flexibility and portability for their AI systems. If they ever need to switch providers or migrate between clouds, multi-cloud enables this.
- Leverages unique strengths: No single cloud is ideal for every AI workload. Multi-cloud allows you to leverage the unique strengths of different providers. For example, you can use AWS for serverless AI, Azure for enterprise AI capabilities, GCP for its advanced ML infrastructure, CoreWeave for GPUs and training/inference.
- Mitigates risks: By spreading your AI workloads across multiple clouds, you minimize the risks associated with any one cloud going down or losing service. Multi-cloud AI provides redundancy to keep your systems resilient.
- Enables hybrid cloud: On-premises infrastructure can be incorporated into a multi-cloud AI strategy, creating a flexible hybrid environment. This allows organizations to leverage existing on-prem data centers and infrastructure alongside the scalability of the cloud.
By taking a multi-cloud approach, organizations gain flexibility, resiliency, and choice when it comes to deploying enterprise-scale AI. The rest of this article will explore multi-cloud AI strategies and best practices in more detail.
Mitigating Vendor Lock-in
Relying on a single cloud provider for all your AI/ML workloads presents notable risks. If issues emerge with that provider - such as service outages, security vulnerabilities, price increases, or changes to their offerings - your AI initiatives could face major disruptions. You may be forced to pivot if your current provider no longer meets your needs.
A multi-cloud AI/ML approach avoids lock-in by giving you the flexibility to spread workloads across multiple cloud providers. If you need to move away from a specific vendor, you won't have to uproot your entire ML infrastructure. You can transition more smoothly by shifting workloads incrementally rather than doing a wholesale migration.
Having options to deploy models to different clouds also provides leverage when negotiating contracts with vendors. No single provider can take your business for granted if you have the capability to move between them. You have an exit option if a vendor relationship sours or their offerings are no longer competitive.
Overall, the flexibility of multi-cloud AI/ML is a key advantage in mitigating the risks of relying too heavily on any single cloud provider. It prevents vendor lock-in scenarios that could severely hamper your ability to deliver AI-driven innovations.
Enabling Best of Breed Choices
One major advantage of a multi-cloud AI strategy is the ability to choose the optimal cloud platform for each of your workloads and use cases. Rather than being limited to the AI services of a single provider, you can cherry pick the best technologies across multiple clouds.
For example, you may choose to use Azure for no-code ML with Azure Machine Learning designer, while leveraging AWS for scalable inference with SageMaker. Your computer vision models could be built in Google Cloud usingVertex AI's AutoML Vision, then serve those models from AWS Lambda functions triggered by an IoT pipeline.
The key is assembling the dream team - selecting the best, most cutting edge cognitive services, data analytics tools, and ML capabilities from various providers. This allows you to avoid getting locked into any vendor's limitations.
Each cloud has unique strengths. Azure leads in pre-built AI APIs like vision, language, and decision. AWS offers the most comprehensive machine learning feature set. Google Cloud has great leveraging power with TPUs. By combining multiple clouds, you can take advantage of each platform's capabilities.
This best-of-breed approach does require more up-front evaluation to choose the right cloud for each workload. But the flexibility and control it provides makes the multi-cloud investment worthwhile for most enterprises adopting AI. You have the freedom to evolve as new services and paradigms emerge across clouds.
Managing Increased Complexity
Adopting a multi-cloud AI/ML strategy inevitably introduces more complexity than relying on a single cloud provider. There are several key challenges that come with managing multiple cloud environments for AI/ML:
- Integrating different services and APIs: When using multiple clouds, you'll need to integrate various services and APIs which may not be designed to work together seamlessly. There can be challenges in terms of identity and access management, networking, and movement of data between cloud environments.
- Monitoring and troubleshooting across clouds: It becomes more difficult to get unified visibility into the performance, costs, and usage of your AI workloads when they are running on multiple clouds. If issues arise, it's harder to pinpoint the root cause.
- Applying security policies consistently: You'll need to make sure security practices like encryption, access controls, and compliance standards are applied properly across all the cloud services you use. This requires careful planning and governance.
- Avoiding vendor lock-in: If you rely too heavily on proprietary services from a specific cloud vendor, it can undermine the flexibility benefits of a multi-cloud architecture by creating a lock-in effect.
- Increased overhead for your team: Your IT and data science teams will need to develop skills and expertise working with multiple cloud platforms rather than specializing on just one. This increases the onboarding and training required.
There are a few key strategies you can use to help minimize the complexity of managing a multi-cloud environment:
- Invest in cloud management platforms that provide visibility and control across multiple clouds from a single pane of glass.
- Modularize your architecture using containers and microservices so workloads are portable across clouds.
- Automate as much of your infrastructure provisioning and deployment as possible using IaC tools.
- Create standardized policies, procedures, and governance models that apply regardless of the underlying cloud.
- Provide extensive training and resources to upskill your teams on various cloud platforms.
- Start small and maintain focus - only use multiple clouds where it really provides an advantage.
Adopting a multi-cloud AI/ML approach can help optimize costs in several ways, but managing spend across multiple cloud providers does introduce some additional complexity. Here are some best practices for optimizing your AI spend in a multi-cloud environment:
- Consolidate spending: Use a cloud management platform or billing software to consolidate spending across all cloud accounts in one place. This gives you visibility into total costs.
- Right size infrastructure: Make sure you are using properly sized instances for each workload. Overprovisioning is a common source of waste in the cloud.
- Leverage autoscaling: Scale resources up and down based on demand to only use what you need. Cloud providers have autoscaling capabilities to handle this automatically.
- Utilize spot/preemptible instances: Leverage discounted spot or preemptible instances for batch jobs and fault-tolerant workloads. This can significantly reduce compute costs.
- Schedule workloads: Optimize costs by scheduling workloads to run during discounted time windows, then shutting down resources when not active.
- Leverage discounts and reserved capacity: Look for discounted capacity, region-specific pricing, committed use discounts, and reserving capacity upfront where possible.
- Monitor usage and optimize: Continuously monitor cloud resource usage and spending to identity waste and optimization opportunities. Tune your architecture over time.
- Avoid data egress fees: When moving data between clouds, choose approaches that avoid data egress fees as much as possible.
- Evaluate open source options: Leverage open source technologies where possible instead of proprietary services to reduce licensing fees.
With some forethought and diligent optimization, a multi-cloud AI deployment can provide all the benefits without breaking the bank. Continually evaluate spend and don't leave savings opportunities on the table.
Ensuring Data Security
Security is a major concern when dealing with multiple cloud environments. With data flowing between different providers, special care needs to be taken to safeguard sensitive information.
Data Privacy Concerns
When using multiple clouds for AI, private data can end up stored across various platforms and regions. This fragmentation can make compliance and governance more difficult. Organizations need to evaluate the data privacy policies and agreements of each cloud provider to ensure they meet any regulatory requirements. Conducting privacy impact assessments is recommended when adopting a multi-cloud strategy.
It's also important to minimize data replication across clouds and only transfer sensitive datasets when absolutely required for a project. The more clouds that hold private data, the higher the risk of a breach.
Security Best Practices
Here are some best practices for securing data in a multi-cloud AI architecture:
- Leverage role-based access controls, multi-factor authentication, and privileged access management to restrict data access across cloud accounts.
- Implement consistent security policies, configurations, and controls across all cloud environments. Don't rely solely on individual provider defaults.
- Encrypt sensitive data in transit and at rest using provider-managed or customer-managed encryption keys.
- Use containerization and microsegmentation to isolate workloads and data across cloud accounts and VPCs.
- Monitor data access, transfers, and API calls across cloud providers using unified logging, auditing, and analytics.
- Validate security controls and configurations through audits and penetration testing of each cloud environment.
- Establish incident response plans tailored for security events spanning multiple clouds.
With thoughtful architecture, strong governance, and robust security controls, organizations can build trust and protect data privacy across a multi-cloud landscape. Monitoring, automation, and policy enforcement will be key in managing security as these hybrid environments scale up.
Enabling Flexible Deployments
One of the key benefits of a multi-cloud AI/ML strategy is the ability to flexibly deploy models across different cloud providers. There are a few key techniques that can facilitate this:
Using Containers and Orchestration
Containerization is a must for being able to take an AI/ML model and reliably deploy it across different cloud environments. By containerizing models into Docker or similar runtimes, the model can be decoupled from the underlying infrastructure. Kubernetes and other orchestration platforms can then be used to manage and deploy those containers across on-prem, public cloud, and edge environments.
Major cloud providers have their own container services like AWS ECS, Azure Container Instances, and GCP Cloud Run that can all be leveraged in a multi-cloud deployment. Independent orchestration platforms like Kubernetes also provide a unifying layer. With proper containerization and orchestration, pre-trained models can be flexibly deployed to whichever environment makes the most sense for a given workload.
Cloud-Agnostic AI Platforms
Many AI/ML platforms are now providing multi-cloud capabilities, allowing models to be trained and deployed across cloud providers. For example, tools like DataRobot support model deployment to AWS, Azure, and GCP. Other platforms may allow you to train models on Google Cloud, then deploy predictions on AWS Lambda.
Seeking out these cloud-agnostic platforms can remove a lot of the heavy lifting involved with cross-cloud AI. The platform handles the hard work of abstracting away cloud differences. This frees data scientists and ML engineers to focus on building great models, while not having to worry about cloud lock-in.
MLOps platforms provide capabilities to manage the machine learning lifecycle across multiple clouds. With a multi-cloud AI/ML architecture, MLOps can help track, monitor and govern models deployed to different cloud environments.
Key MLOps capabilities that are beneficial for multi-cloud ML include:
- Model Monitoring - Monitor model performance and drift across all cloud deployments in one centralized platform. This provides visibility into how each model is performing in its respective cloud environment.
- Model Retraining - Retrain models, from neural networks, deep learning, and other big data machine learning algorithms, as needed across different clouds. MLOps enables automated retraining pipelines across cloud boundaries.
- Model Governance - Apply governance policies for models deployed to multiple clouds. This includes model approval workflows, access controls, and ensuring models comply with regulations.
- Model Deployment - Deploy models to multiple clouds for inference from a single MLOps platform. This simplifies deployment vs managing each cloud separately.
- Cloud-Agnostic Support - Choose an MLOps platform that can work across major cloud providers. Avoid ones tied to a single cloud.
Using MLOps for multi-cloud AI enables organizations to have centralized visibility and control over a distributed ML architecture. It's a key enabler for scaling AI across multiple clouds successfully.
The key to successful multi-cloud AI is having open standards that facilitate interoperability between different cloud providers and on-premises environments. Relying solely on each cloud vendor's proprietary tools and services will lead to lock-in and make it challenging to move workloads and data.
Organizations like the Open Compute Project (OCP) and the Linux Foundation are leading the push for open standards for multi-cloud and edge computing environments. OCP launched the Open Cloud Initiative (OCI) in 2019 to help define open specifications across many aspects of cloud infrastructure.
Some areas where open standards are critical:
- Containerization frameworks like Docker and Kubernetes enable container-based applications and workloads to run across any environment.
- Open data formats like Parquet, ORC, and Avro allow data to be efficiently queried across storage systems.
- Model packaging formats like ONNX allow models to be portable across frameworks and runtimes like TensorFlow, PyTorch, and MXNet.
- Open telemetry provides a standard way to collect metrics and monitor workloads across heterogeneous systems.
- Identity management standards enable secure access controls and authentication across cloud boundaries.
While progress is being made, open standards are still emerging. Organizations should evaluate solutions not just on technical capabilities but on their commitment to openness and interoperability. As multi-cloud AI grows, open standards will be key to avoiding lock-in.
The Road Ahead
The future is bright for multi-cloud AI. As organizations continue their push towards digital transformation, leveraging multiple cloud providers to build and deploy AI models will become standard practice rather than the exception. Here are some key trends we expect to see unfold:
Multi-Cloud Becomes the Norm
Using a single public cloud provider for all workloads is already disappearing. Adopting a multi-cloud strategy for AI workloads will follow a similar path. The flexibility and specialization afforded by multi-cloud AI is too advantageous to ignore for most organizations. Workloads like training performance-hungry models may land in the cloud best suited for that task, while less intensive workloads are placed in the optimal cloud based on factors like geography, data gravity, and more.
Improved Portability and Interoperability
As multi-cloud AI grows, there will be stronger incentives for cloud providers to reduce friction between platforms. We'll see continued development of open standards and frameworks that make sharing data, models, and other artifacts between cloud environments seamless. Cloud-agnostic AI development platforms will help data scientists and machine learning engineers build models once and deploy anywhere. Portable ML pipelines and DevOps tooling will become the norm. Reduced vendor lock-in leads to happier customers.
MLOps Bridges the Multi-Cloud Gap
MLOps will be key in providing the workflows and governance needed to manage complex multi-cloud AI environments. With MLOps, teams can monitor models and data across clouds in a unified way. Retraining and deployment automation ensures models stay refreshed where they live. MLOps also enables collaboration between data scientists, DevOps engineers, and other roles in a seamless cross-cloud fashion.
Specialized AI Clouds Emerge
While general purpose clouds will continue providing a wide range of services, we may see niche AI cloud providers emerge with highly-optimized offerings tailored for ML workloads. For example, specialized compute instances for model training, autoML solutions, and other AI building blocks can differentiate these providers. They seamlessly integrate with the major clouds while providing specialized value-adds.
The future of multi-cloud AI shines brightly. As the landscape evolves, embracing a multi-cloud strategy today allows organizations to reap the benefits while maintaining flexibility for whatever comes next.