Machine learning has become an increasingly important technology in recent years, providing businesses with valuable insights and decision-making capabilities. While embarking on machine learning initiatives, it is critical to invest in technical infrastructure that can support the project through all the stages of the lifecycle. Infrastructure is crucial in scaling a machine learning pipeline because it enables the pipeline to handle larger amounts of data, run more complex models, and deliver faster results. A well-designed infrastructure can help increase the efficiency of the pipeline and reduce the time and resources required for training and inference.
One of the main challenges of scaling a machine learning pipeline is handling the large amounts of data required for training and inference. A robust infrastructure can provide the necessary storage and processing power to handle these data-intensive tasks. This includes high-performance computing clusters, distributed file systems, and data warehouses.
Another key factor in scaling a machine learning pipeline is the ability to train more complex models. Deep learning models, for example, require significant computational resources and specialized hardware such as GPUs. An infrastructure that supports these resources can help enable faster and more accurate training of these models.
Finally, infrastructure plays an important role in delivering fast and reliable results from the pipeline. This includes optimizing the pipeline for low latency and high throughput, as well as ensuring high availability and fault tolerance to prevent downtime and data loss.
Keeping in mind the needs of our customers and their projects that are scaling rapidly, at RapidCanvas, we have built in infrastructural elements that make it possible to build and deploy models rapidly and efficiently.
With GPU support, RapidCanvas users with high productivity workloads can train and deploy models in minutes, instead of hours. Many cores are accumulated, helping use fewer resources without any decrease of efficiency or power.
A CI/CD (Continuous Integration/Continuous Delivery) pipeline, which handles an entire development workflow from beginning to end, typically runs through the steps of Source-Build-Test-Deploy. A well-structured CI/CD framework enables developers to implement code changes and create iterations rapidly. This iterative process helps to find and fix errors in production more easily.
In the context of a machine learning platform, using a CI/CD pipeline becomes even more critical. Machine learning models, once deployed, need to be continually updated and retrained with new data. Model performance and accuracy also needs to be monitored and experiments need to be carried out, as necessary.
With RapidCanvas’s automated CI/CD pipeline, it becomes simple to review and assess the model, and then to update it with new data or to carry out experiments. This becomes even more crucial as machine learning initiatives begin to scale and hundreds of experiments are required to be carried out, without any breakdowns. The model performance is constantly monitored and improvements can be seen over time.
Cloud marketplaces offering a variety of functionality and services are now commonly used by companies of all sizes. While using marketplaces like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, to name the biggest providers, organizations can take advantage of the wide range of options, ranging from storage solutions to software tools.
RapidCanvas is fully integrated with Google Cloud Platform and Microsoft Azure, and a company using one of these can easily set up and start using the RapidCanvas platform in no time. These partnerships take out the guesswork, from the infrastructure perspective, and empower organisations of all sizes to create, deploy, and manage data projects at scale with ease.