Great teams incorporate a variety of skill sets. For example, a football team consisting of 11 quarterbacks would get crushed in a game against talented linemen, running backs and receivers. It\u2019s no different when building a team for an enterprise AI project; you can\u2019t just throw a bunch of data scientists into a room and expect them to come up with a revenue-generating or efficiency-improving project without support from other members of the enterprise.\n\nInterestingly, many companies do just that, creating a disconnect between data science teams and IT\/DevOps when it comes to AI development. This gap is a significant reason why AI pilot projects fail.\n\n\u201cAI projects are a team sport and should include a multidisciplinary team spanning business analysts, data engineering, data science, application development, and IT operations and security,\u201d according to\u00a0 Moor Insights & Strategy in a September 2021 report titled \u201cHybrid Cloud is the Right Infrastructure for Scaling Enterprise AI.\u201d\n\nThe biggest divide between data scientists and IT often centers around the tools necessary to develop AI models.\n\n\u201cMany IT organizations try to build a killer, one-stop solution that fits all needs,\u201d says Michael Balint, principal product architect at NVIDIA. For example, many prefer to develop with deep learning frameworks such as PyTorch on a dedicated system, while others schedule their work using Slurm or Kubeflow. IT is often left scratching their heads about how they can consolidate everything into one solution.\u201d\n\nYet, this can be a disaster when it comes to AI projects, Balint warns. \u201cThis is such a nascent area that if you\u2019re in IT and you try to pull the trigger on one solution, you might be missing out on functionality that a data scientist or data engineer might need to get their job done. Data scientists would really love to just build models and do real core data science. They get frustrated when they don\u2019t have the tools to do that, and the blame gets put on IT.\u201d\n\nMLOps to the rescue\n\nThe better approach is to have IT work with the data science groups on bridging the gap through processes and tools such as MLOps. These can provide enterprises with governance, security and collaboration through features such as tracking and repeatability. MLOps platforms can orchestrate the collection of artifacts, compute infrastructure and processes that are needed to deploy and maintain AI-based models. Many MLOps systems can also evaluate the accuracy of models in order to retrain and redeploy as needed.\n\n\u201cOrganizations can increase the percentage of models that are successfully deployed in production by implementing MLOps tooling, which aids in managing data science users, data, model versions, and experiments,\u201d says Moor Insights. \u201cThe tooling should also allow IT teams to manage the develop-to-deploy cycle with the same DevOps rigor as traditional enterprise apps.\u201d\n\nThis approach can help companies bridge the divide between the data and IT sides.\n\n\u201cA few years ago there was emphasis on deep learning engineers and data scientists as the heroes of the industry,\u201d says Balint. \u201cI think the unsung heroes are the DevOps and MLOps engineers that sit in the IT group, because you need to build the right solutions and stacks for everybody else to do their job. If you don\u2019t have that, you can\u2019t move very quickly.\u201d\n\nGo here to get more information about AI model development using DGXTM-Ready Software on NVIDIA DGX Systems, powered by DGX A100 Tensor core GPUs and AMD EPYCTM CPUs.