An AI platform is a set of tools that enable data scientists and other users to build, train, deploy and manage machine learning applications at scale. These solutions can apply the principles of machine intelligence to a wide range of industries, from customer service to supply chain analytics. They can also automate processes and reduce room for error by completing repetitive tasks or identifying patterns that human employees would have missed.
The best AI platforms help companies accelerate the pace at which they can produce and deploy machine learning models and ensure their deployments are secure. They offer a variety of compute acceleration features such as GPUs and TPUs that improve the performance of machine learning training and model evaluation. They also support the rapid creation of machine learning workflows that can be easily accessed by data scientists, software developers and IT administrators. This approach significantly reduces development time and allows teams to deploy more models faster.
These solutions are built and maintained by organizations and teams that stay entrenched in the open-source community, contributing research, resources and talent. This enriches the diversity of research and talent and expands the volume of innovative technologies available to data science and ML practitioners. Look for an AI Platform that provides user-friendly integrations and supports popular open-source frameworks and libraries like TensorFlow, PyTorch and Scikit-learn. Many of the major cloud infrastructure providers also offer AI-enabled solutions.
What Is an AI Platform?
In addition to providing compute acceleration, an AI platform should provide a range of security and governance capabilities that allow businesses to identify and mitigate risk. This includes a model scoring engine that can test and verify the accuracy of a model in a production environment, as well as tools for automated-feature engineering, model selection and deployment.
A centralized model repository is an essential feature for enabling the rapid reuse of models by multiple data science and ML teams. This is especially helpful for projects that involve collaboration across departments and regions or when teams need to share code or compare models and results. Look for a solution that offers easy access to a variety of tools for data science and ML, including Jupyter notebooks, Keras, Matplotlib and Scikit-learn. Some platforms also provide easy-to-use APIs that allow you to centralize your workflows within the AI platform and access your favorite tools without having to leave the dashboard.
AI-enabled solutions can have significant impacts on business efficiency, productivity and profitability. By automating processes and reducing room for error, they can help eliminate manual work and increase output by freeing up employee time. They can also help businesses save money by lowering production costs and reallocating human resources to more value-added tasks.
The most successful AI implementations are driven by a combination of data and ML expertise. In the private sector, the benefits of AI include improving customer services, increasing sales and enhancing the product development process. In the public sector, the potential is even greater, from detecting product defects that are missed by human inspection to optimizing government services and analyzing public spending. AI is poised to transform virtually every industry, but implementing the technology at scale requires a strong commitment from leaders and an understanding of how it can be leveraged effectively.