- Data Science
- Machine Learning
- Artificial Intelligence
Many organizations are looking to increasingly leverage Machine Learning and Artificial Intelligence (ML & AI) to enhance their decision-making.
Cloud Computing has fundamentally altered the economics of data usage, and ushered in an era of new data ingestion and storage services that offer the ability to store more data, different forms of data, and data that is captured at varying velocities – including near real-time. There are also modern, Cloud-based tools and solutions that allow organizations to conduct more sophisticated analytics and Machine Learning, in a more robust and scalable manner, with greater accessibility and ease-of-use.
In order to capitalize on the promise of these advanced analytics breakthroughs, it is critical for organizations to capture, store, and model their data in a manner that is conducive to ML & AI. In this 30-minute recorded webinar, Thorogood Consultants John Miller and Lauren Potechin explore the modern landscape in terms of modern Cloud-based platforms and their benefits relative to older ways of approaching data warehousing and storage, the new ML & AI services available via the major Cloud vendors of Microsoft Azure, Amazon Web Services, and Google Cloud Platform, and how to approach architecting a platform that provides a foundation for Machine Learning and Artificial Intelligence.
Building an AI and ML ready Modern Data Platform (22 mins)
What we cover:
- Modern, Cloud-based Data Platforms and the benefits they offer
- The Machine Learning and Artificial Intelligence solutions available natively via major Cloud vendors
- Considerations when architecting and building scalable and productionized ML & AI-ready data platforms
Is it for you?
- Are you interested in understanding the possibility of unlocking ML & AI through Cloud-based architectures?
- Are you keen to learn about the specific analytics, MLOps, and AI solutions available on the market?
- Are you interested to hear how data engineering considerations and decisions factor into data science readiness?