There are three areas where MLOps vendors beat CSPs: integration support, deployment options, and infrastructure cost management.
As organizations embark on their journeys to integrate AI and ML into every aspect of their businesses, there are many solutions in the market to accelerate the process. From model training platforms and frameworks, data management solutions, CI/CD pipelines, and more, there is no shortage of tools. Naturally, the cloud service providers offer a rich set of tools for each part of the ML pipeline, and for many who are already running many systems atop their infrastructure, it was easy to start using their MLOps solutions. But there are three main areas where cloud MLOps features come in second to those of MLOps vendors: integration support, deployment options, and infrastructure cost management. This blog explores the pros and cons of using a cloud MLOps solution vs. a best-of-breed MLOps solution like Modzy.
There are numerous benefits of leveraging cloud MLOps tools. The first benefit is that you can run your MLOps processes in the cloud with whatever compute and tools you need, without having to buy any new hardware or provision an environment. If your team is already using a cloud-native training tool (e.g., Amazon SageMaker or Azure ML), transitioning to production deployment can be straightforward. There’s no denying that they also have great tools for data engineering and ETL, providing a holistic solution for all parts of the ML lifecycle. Not to mention, these tools are constantly evolving and adding new innovations because they’re backed by the most well-resourced organizations in the world.
At the same time, there are a few areas where cloud MLOps solutions leave something to be desired. They require an advanced understanding of their tool suites, which can be a barrier to entry for organizations that don’t have a deep bench of tech talent. They also exclusively use their own hosting services for model deployment, which can yield problems in all three areas mentioned earlier (integration support, deployment options, and infrastructure costs.) For example, setting up a robust model deployment pipeline in Amazon Sagemaker requires a custom set up of unique hardware instances, load balancers, API gateways, lambda functions, and extensive knowledge of several other AWS services. This leads to complex integrations and time-intensive processes. Additionally, many of the inferencing and model pipelines limit the number of containers that can be used.
Perhaps most limiting factor of all is that each cloud MLOps solution only offers deployment support for its own instances, meaning they don’t natively support multi-cloud environments, a trend that is becoming increasingly popular. Additionally, cloud MLOps solutions cannot be configured for on-premises devices like an NVIDIA MIG, NEMO or TritonM systems. What’s more, cloud providers are in the business of selling compute, and therefore these solutions are designed with that in mind. To achieve any cost savings, you must either manually or build automated processes to shut off infrastructure, there’s no automatic scale-to-zero option. Some vendors even add a compute upcharge for standard commodity instances that would otherwise be used for non-ML workloads.
While there are many benefits to going with a cloud MLOps solution, for some, the infrastructure costs alone are prohibitive, and therefore a best-of-breed MLOps solution like Modzy offers an attractive alternative.
To start, Modzy offers cloud-agnostic deployment, meaning that models can be deployed and run anywhere – in the cloud, on-premises, or at the edge. Modzy supports multi-cloud and cloud portability so that inferencing can be run on any cloud or on-prem environments managed by a customer. One of the benefits of this approach is that Modzy obfuscates the requirement to know what infrastructure is being used, or any vendor-specific tools and processes. Similarly, Modzy offers direct and ‘push-button’ import for models from Amazon SageMaker, Azure ML, MLFlow, Tensorflow, PyTorch, MXNet and many other tools, and provides a model registry, repository and searchable library that contains model lineage, agnostic of model training tool. This means that organizations retain complete visibility, control, and accountability for model lineage, regardless of where models are deployed. Additionally, features like persistent endpoints come standard in Modzy, making for easy integration, while they are optional and difficult to enable in Amazon SageMaker for an additional cost.
With regards to cost management, Modzy automatically scales inferencing up and down based on demand and queue, including all the way to zero instances when usage tapers. Cloud MLOps solutions require manually turning off inference instances when not in use, and turning them back on will create new, unique endpoints unless the optional persistent endpoints are enabled at additional cost. AWS recommends writing Lambda functions yourself to enable this functionality, each function at additional cost. One recent cost comparison showed that we could save a customer 87% on its annual cloud costs by running models with us rather than with a cloud MLOps solution – no small amount!
In addition to benefits like easy integration, deployment options, and infrastructure cost management, choosing a solution like Modzy has the added benefit of unique features for MLOps. In terms of AI security, Modzy offers advanced and industry leading data anomaly detection, adversarial attack resistance, and data protection – in addition to the standard DevOps security features like encryption, private network connectivity, authorization, authentication, monitoring, and auditability offered by Amazon SageMaker. Modzy’s Model Watermarking ensures models are not stolen, copied or tampered with. Finally, Modzy Edge makes it easy to connect to and run models on any edge device including those as small as a Raspberry Pi. This enables great flexibility to run ML workloads anywhere, reducing the need for unnecessary data transfer, mitigating networking and security concerns, and ensuring super low latency for fast processing speeds.
Because the market for MLOps solutions is ever expanding, options abound, and that’s a good thing! For some customers, the rich set of tools offered by a cloud MLOPs tool will provide what’s needed to set up a robust MLOps pipeline that can scale to meet their every use case. For others, talent, budget, and timing restrictions put constraints on their selections. In any case, the ease of integration, deployment options, and infrastructure cost management offered by a platform like Modzy are attractive features for many looking to accelerate the path to production and value from AI. For a further breakdown on this topic, check out Comparing MLOps Platforms for a framework for understanding MLOps tools, as well as factors to consider when evaluating them.