About the Webinar:
Predictive Intelligence from Machine Learning (ML) has the potential to change everything in our day to day experiences, from business to leisure – making a huge difference in the way we live, work and play. Most real-world machine learning work involves very enormous – simply huge data sets that go beyond just the CPU, memory, and storage limitations of a single computer. Spark has been the go-to solution for enterprises that have to process extremely large data sets in an efficient and cost-effective manner.
In this webinar with Anirban, we learn how Apache Spark has revolutionized the way enterprises build and deploy predictive algorithms and how it has drastically reduced the time taken to operationalize ML models. With data coming in from multiple sources, an increase in the rise of new tools and technologies to process this data, the biggest challenge for organizations is to churn out the best business insights in real-time. While data scientists continue to work on these challenges and build models, much of their time is spent on supporting the data infrastructure instead of building models to solve their data problems.
Key takeaways:
- What is the speed and scalability that Spark brings for data scientists to solve and iterate through their data problems much faster than before?
- How does Spark ML alleviate many of the problems that are typically faced while moving from dev to production environments?
- How are data practitioners able to solve their ML problems at a much greater scale with Spark?
- Recognize some of the supervised and unsupervised machine learning tasks with Spark ML.
