Join Dan Sullivan for an in-depth discussion in this video Applying models, part of Advanced NoSQL for Data Science.
- [Narrator] Businesses get value…from machine learning by applying models in production.…Let's spend a few minutes describing the production…lifecycle of models.…Once a model is built, validated…and tested it must be saved.…The structure of a saved model…depends on the algorithm used.…For example, random forests have tree structures…while regression models have numeric values…and formulas for representing lines and curves.…Machine learning tools sometimes use custom formats.…These can optimize storage at the expense of portability.…For example, scikit-learn, a popular…Python machine learning library uses a format…that's optimized for large arrays.…
If you need to build a model with one tool…but use it in production with another tool,…consider using the Predictive Modelling Markup Language…which is known as PMML for short.…That's a standard format.…In many ways, machine learning models…are treated like piece of software.…They're version controlled.…We test them before we use them.…We automatically deploy them in some cases…
The course begins with an introduction to NoSQL, and then delves into the specifics of document, wide-column, and graph databases. Learn key details for performing data preparation, exploration, and extraction for each type of NoSQL database. Review case studies that show how to use various NoSQL databases with popular data science tools, including the document database MongoDB, the wide-column database Cassandra, and the graph database Neo4j.
- NoSQL compared to traditional relational databases
- Performing common data science tasks
- Preparing data with document databases
- Manipulating data in NoSQL
- Preparing, exploring, extracting, and model building
- Working with document, wide-column, and graph databases
- Reviewing case studies using MongoDB, Cassandra, and Neo4j
Skill Level Advanced
1. Why NoSQL?
Types of NoSQL databases2m 20s
2. Perform Common Data Science Tasks with NoSQL Databases
3. Document Databases for Data Science
4. Wide-Column Databases for Data Science
5. Graph Databases for Data Science
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