Machines that teach themselves, ironically, still need lots of care and feeding.
To make machine learning (ML) succeed at scale, data science teams must standardize and streamline the ML lifecycle (also referred to as ML operations or MLOps) that spans data and feature engineering, model development, and model production. While data science teams can start ML initiatives with a piecemeal approach, as they grow they can benefit by standardizing on a platform that provides the necessary scalability, reproducibility, and governance. Traditionally, data scientists focused on model operations only and used ML platforms to achieve this.
In contrast with this model-centric approach, a new data-centric option has emerged: cloud data platforms that combine data warehouse and data lake constructs. Data Clouds offer lifecycle speed, scale of production, model governance, and support for the ecosystem of ML tools. It also excels in addressing key challenges around data offering the ability to consolidate enterprise data, collaborate across functions and organizations, and integrate ML into operational workflows.
Join Snowflake & Eckerson Analysts to learn:
- How the market has evolved, including the rise of ML platforms and emergence of cloud data platforms such as Snowflake Data Cloud
- What advantages cloud data platforms offer for ML projects, compared with do-it-yourself approaches and standalone ML platforms
- What guiding principles data science teams should apply to optimize MLOps on their cloud data platforms