AI applications are quickly becoming table stakes for operations across a broad spectrum of industries. Whether you are trying to accelerate a global clinical trial for a breakthrough drug or keep pace with thousands of constantly changing investment banking regulations, machine learning can be the difference between leaping ahead or falling behind your competitors.
But successfully deploying AI involves complexity, and that complexity affects teams on multiple fronts. AI projects are often derailed by a lack of quality data, misaligned business and technical priorities, and most of all, insufficient resources. In a recent Deloitte report, 23% of organizations surveyed said they experienced a significant or extreme skills gap related to AI initiatives.
1. Identify the four pillars of an AI project team.
A typical AI project starts with data, lots of data. To prep and make the best use of that data and gain momentum out of the gate, your AI project team will need to possess four core skill sets:
- Your data engineers perform the vital job of harnessing quality data for analysis and insight. They integrate, model, and optimize data to create the data platforms that provided the foundation for machine learning and AI applications.
- Data scientists build on that foundation, identifying the right data sets, use cases, and algorithms for AI model training.
- AI architects look at both the big picture and how AI will fit into it. They map deployment and management of AI models in the existing technology ecosystem, providing a conduit between technology, operations, and business units. For the AI project to stay aligned with business goals and succeed, it needs architects who can bring data engineers and scientists together with the DevOps team and company leadership who sponsored it.
- Full deployment then relies on machine learning (ML) engineers who will put the AI models to work in an iterative, scalable fashion—ensuring AI platforms meet expectations.
2. Bridge the widening skills gap.
Though it sounds simple enough at a high level, there’s more to building an AI team than hiring four people with AI, ML, or data in their job titles.
AI and ML talent is expensive and hard to find. And AI project teams typically include dozens of data scientists, software engineers, and dedicated IT or DevOps personnel. There is also a need for non-technical resources that manage the legal and ethical questions, especially in industries such as healthcare and financial services. The hiring process alone can, and often does, take months — burning through budget before the team is fully assembled, trained, and working on the project.
When you consider that over half of AI projects fail during proof of concept, it makes sense to look for better ways to bridge potential resource or skills gaps and reduce the complexity of your AI projects at the outset.
AI initiatives, by their very nature, place rigorous demands on those working on them, and in a competitive market for talent, high turnover is inevitable. Once your organization is adequately staffed and the project is in motion, even a temporary gap in a key area, such as data science or engineering, can dramatically slow or stall efforts.
It is important to make resource planning and skills development a continuous effort that should be sustained throughout the project life cycle.
3. Employ the secret to supercharging your AI team.
With so much at stake, these unpredictable implementation roadblocks make the ability to augment the machine learning expertise of your organization a critical consideration in planning your AI project team.
To bridge any potential AI/ML engineering skills gaps and allow organizations to leverage AI in the face of talent scarcity, SambaNova offers a solution for augmenting the machine learning expertise of your in-house team—Dataflow-as-a-Service™. This first-of-its-kind solution accelerates AI workloads by empowering organizations in all industries to deploy natural language processing, recommendations, and high-resolution computer vision models with state-of-the-art accuracy.
Dataflow-as-a-Service can close technical capabilities gaps, eliminate repetitive tasks that overburden critical technical staff, and allow them to focus on creating innovative and competitive AI applications for the following:
- Natural language processing: SambaNova provides a flexible solution that accelerates the entire NLP pipeline across training, fine-tuning, distillation, and deployment.
- High-resolution computer vision: Analyze high-resolution images at true resolution, eliminating the need for down-sampling or tiling images into smaller pieces to process.
- Recommendation systems: Deploy flexible and complete recommendation models to deliver more personalized, relevant recommendations.
Deploy AI at scale—faster than ever before.
Building the right AI team requires more than scouring job boards and sending recruiters far and wide. Just as AI can help speed discovery in clinical trials or ensure real-time compliance for financial services institutions, the right technology solution can supercharge your AI project team.
SambaNova can help your organization rapidly deploy, iterate, and scale your AI projects. For a deeper dive into how SambaNova can reduce time to deployment by as much as 12-18 months, schedule a conversation today.