How to Create an AI Platform for Your Business
AI Platform for Your Business
Building AI for your business is a complex endeavor. It requires creative ideas and a diverse array of top-notch tools. It also requires engineering support to navigate the maintenance and security of your technology stack. To reduce development time and accelerate your journey to successful AI deployment, look for an end-to-end platform that curates a set of integrated tools and repositories for you. This helps you to focus on the constant, breakneck-pace evolution of underlying technologies rather than navigating tens of disparate tools for your project.
A good AI Platform will allow you to build, train, and deploy your models with ease. It will also make it easy to automate the process and recalibrate as your team learns more about best practices, approaches, and techniques for developing and deploying models. This will accelerate your entire AI development lifecycle and enable you to move more model iterations into production faster, increasing accuracy and effectiveness of AI applications.
While advances in open-source deep neural networks, GPUs, and TPUs have made it cost-effective to train models at massive scale, deploying these models into production is difficult. When deployed, these models require significant supporting infrastructure to serve models, route traffic, handle load balancing, and ensure data lineage is tracked. This is why Google, Uber, and other leading tech companies developed their own AI platforms to enable them to deploy their machine learning models more easily.
How to Create an AI Platform for Your Business
These platform solutions, known as ML-as-a-Service, take the burden off your IT and engineering teams to support the scale of your machine learning models. They are hosted in a cloud environment and provide on-demand access to the compute resources you need to run your model training, validation, and test runs. They also handle other aspects of the AI development and deployment lifecycle, such as model management and data orchestration.
Look for an ML-as-a-Service solution that provides APIs to connect your AI software to other applications. This will facilitate your integration with other software, and it will help you to easily manage, monitor, and secure data — especially if your organization has stringent data privacy and security regulations like the EU’s GDPR or California’s CCPA.
Consider a vendor with an extensive, open-source community for their software and tools. This will help your team stay at the forefront of emerging technologies for machine learning and will give you a more diverse pool of talent and research. This will increase the speed of your development, improve the quality of your model, and enable you to achieve a higher ROI on your investment in your AI software.
An AI software development platform should also provide a centralized repository for open-source and proprietary tools, as well as a catalog of best practices for creating, deploying, and securing your AI application. It should also include tools for managing metadata, tracking and tagging model artifacts, and for securely importing, exporting, and reusing data. In addition, it should have a strong commitment to the open-source community by providing a variety of educational and collaborative resources.