The Right Infrastructure for Your AI Journey
Offering Manager Maria Ward discusses how to find the right infrastructure to continue your AI journey.
There’s no doubt that artificial intelligence (AI) is one of the fastest growing technologies today. The need for AI is sweeping across industries as disparate as automotive, manufacturing, transportation, communications, retail, oil and gas, media and entertainment, healthcare, and financial services—and the results can be seen in nearly every industry. In order to keep up with this trend, IT leaders are being challenged with how to use AI to turn their data into business insights and value. As the offering manager for the IBM Power System* AC922, I’ve seen some common factors being considered when choosing the right IT infrastructure for AI.
Engineered for Training
Training is the foundation of the AI pyramid. Existing business data must be processed to build and train a model, but that’s just the beginning. A model is only as accurate as the data it was trained on, and it can quickly decay as new data is introduced.
To prevent this, models must be continuously trained, which takes time and resources. Having the right infrastructure will ensure your data scientists are spending time on the output of the trained model, rather than waiting for models to be trained. This requires dedicated infrastructure that was designed to handle the most challenging AI training workloads.
Built for Data
Training a successful AI model takes large volumes of data. The infrastructure must be able to quickly and efficiently process data to train a model and ensure it provides the correct results. The average model can take many iterations to be trained to a desired level of accuracy, which requires valuable time and resources.
The infrastructure must be designed to accelerate faster data throughput with decreased latency to get the data where it needs to go faster. This means less time spent waiting for models to be trained and more time spent reaping the benefits of the trained AI model.
CPU-only infrastructure lacks the compute power needed for demanding AI workloads. Having the latest AI accelerators is the key to handling the compute-intensive and highly parallel workloads required to build and deploy an AI model and then to scale it into production. Traditional infrastructure may be sufficient for exploring and piloting small models but may be unable to grow with your business needs and scale into production.
Many AI open-source tools and frameworks are being deployed, but to be successful, infrastructure must be open and backed by a robust ecosystem of hardware and software partners. IT organizations must also be able to rely on dependable hardware and a supported software framework so they can spend time building AI solutions instead of worrying about the underlying IT infrastructure.
The IBM Power System AC922 was engineered to be the most powerful training platform for AI, providing the compute-intensive infrastructure needed to deliver faster time to insights from exploration to production with demonstrated scalability and end-to-end support.
Backed by IBM’s commitment to creating and fostering an open ecosystem across the stack, the AC922 supports hundreds of hardware and software partners including Nvidia, Mellanox, Red Hat, Tensorflow, Pytorch, Caffe and Onyx. Together, they add 100,000-plus Linux* distributions on POWER*. You can be
sure to find a solution that fits your needs.
AI can be challenging, and the market is constantly evolving. To set their organization up for AI success, IT leaders need to look past the hype, do their research and choose the right infrastructure for their AI journey.