Streamlining Model Production with Qwak: A Cost-Effective Alternative to Amazon SageMaker
What are the benefits of using a fully managed MLOps platform? Which platform should you evaluate for your business and how do you take into account what your team needs for model productionization? We’ll dive into the difference between Amazon SageMaker and Qwak specifically and the importance of looking at each of the platforms as a whole when making a decision for your organization’s needs and goals.
We’ll explore the benefits of Qwak as an alternative to Amazon SageMaker and the reasons why so many of our customers decided to make the move and migrate over to our platform. Using a real-time machine learning model as an example, we’ll showcase how Qwak’s end-to-end platform simplifies the process while reducing the total cost of ownership, especially when compared to using multiple managed services and tools to get to the same end result.
Amazon SageMaker is a comprehensive ML product suite that offers a wide range of tools to cover the entire ML lifecycle. However, building and deploying models in AWS’s MLOps solution can involve multiple tools and considerable time and resources, often requiring an expensive ML team.
Use Case and AWS Capabilities
For our example use case, we’ll create a real-time machine learning model that periodically trains and deploys as a real-time endpoint. We’ll protect users from undesired outcomes by gradually routing traffic to the new version based on monitoring results. The model will be trained using data from our analytical database.
To build this solution on SageMaker, we need to leverage several services, including Amazon EMR, Amazon Feature Store, Amazon managed workflows for Apache Airflow, and Amazon SageMaker training and pipelines. This setup automates data processing and model training, ensuring efficiency throughout the ML lifecycle.
Building it in Sagemaker involves setup and integration of a variety of tools and significant efforts from the engineering team that needs to build and maintain the operation.
Challenges in SageMaker and Cost Considerations
Despite its capabilities, delivering code to production in SageMaker comes with various challenges, such as managing immutable objects, versioning models, and handling errors and monitoring. Building a platform to support multiple models can require a significant investment of time and cost, with setup expenses reaching up to $1,000,000 and ongoing maintenance costs adding up to $500,000 per year.
Qwak offers a user-friendly and cost-effective alternative for model productionization. Its UI/CLI simplifies the creation of data pipelines through feature sets. Qwak Automation enables seamless model build and deployment directly from GitHub repositories, generating immutable and deployable model objects automatically.
Qwak's built-in model infrastructure and data monitoring streamline the monitoring process, allowing users to set up alerts and automations with ease. The platform supports various deployment configurations, including A/B testing, variable traffic split, and gradual deployments, making it versatile for different use cases.
Qwak was purpose-built to empower data teams in building and deploying models with ease. By utilizing an end-to-end platform like Qwak, organizations can reduce integration efforts and cut down on development resources and labor costs. The platform's built-in automation capabilities further enhance productivity, making it a more cost-effective choice for many organizations.
To conclude, Qwak offers a compelling alternative to Amazon SageMaker, providing a simplified and cost-effective approach to model productionization. Organizations should carefully consider their needs and resources before embarking on any ML project, and Qwak can be an excellent solution for those seeking streamlined, efficient, and economical model deployment.