Business growth is directly tied to a company’s ability to take full advantage of its data. Companies can run more strategically, efficiently, and cost-effectively by using artificial intelligence (AI) to unlock data insights from hard-to-reach sources such as audio, video, images, and text.
However, extracting the total value of data has long been a challenge for organizations. But this problem was only compounded as data creation more than doubled during COVID. The vast majority of this data is unstructured—meaning data that doesn’t fit neatly in spreadsheet columns and isn’t easily minable for insight.
To master this data problem, companies need to understand which AI models and workflows to use and how to effectively deploy, scale, and manage them consistently over time. In short, they need to know how to implement a machine learning operation (MLOps) across the enterprise.
Redefine how you operationalize enterprise-wide AI with MLOps
In this white paper, we’ll dive into each stage of machine learning operations (MLOps) and what’s required to achieve successful AI deployments that mitigate common AI project risks and effectively solve business problems.
Crafted by enterprise AI experts Veritone, this whitepaper will equip you with:
- An in-depth overview of what MLOps is and how it can add value to your business
- A step-by-step roadmap for successfully implementing MLOps
- Examples of how real-world organizations are leveraging MLOps to scale data analytics and AI across the enterprise
- Meaningful information on how MLOps can be leveraged to rapidly operationalize insight-generating AI models, infuse AI across enterprise applications, and utilize AI with custom solutions to solve unique business challenges