Question: I need a technical computing platform that can handle big computing jobs with high-performance computing capabilities, what are my options?

If you need a powerful technical computing foundation with high-performance computing abilities, JuliaHub is worth a close look. It's a collection of tools for modeling, simulation and collaboration using the Julia language, with support for multithreading, parallel and distributed computing. JuliaHub also has real-time collaborative coding, customizable server settings and reproducibility tracking. With products like JuliaSim, Pumas and Cedar-EDA, it's good for scientific modeling, digital twins and machine learning, with high-performance computing on tap when you need it. JuliaHub tools include CloudStation for managing virtual machines and distributed jobs.

Another strong contender is Anyscale, which is designed to build, deploy and scale AI applications. It's built on the Ray framework and supports a variety of AI models, with smart instance management, GPU and CPU partitioning for efficient use of resources. Anyscale also has native integration with popular IDEs, persisted storage and Git integration so you can run and debug code at large scale. The platform has strong security and governance controls, including user management and customizable plans for enterprise adoption.

If you want a broader platform that combines simulation, high-performance computing, data analysis and AI, check out Altair. The company's tools are aimed at industries like automotive, aerospace and heavy equipment, with AI-boosted simulation, physics-based predictions and generative design. Altair's tools are designed to speed up product design, cut waste and optimize performance, so it's a good fit for engineering and manufacturing work.

Last, IBM Cloud is a secure, resilient and high-performance foundation for applications, particularly in regulated industries. It can be scaled up for AI workloads, and it's got a number of tools like IBM Cloud for Regulated Industries and IBM WatsonX.ai for rapid deployment of machine learning models. The platform also offers financial incentives and compute credits, so it's a good choice for companies that want to build and govern AI work in their own organization.