Course Outline
Snowflake Data Cloud Architecture and Overview
- Snowflake Data Cloud overview
- Three-tiered architecture
- Snowflake UI and core capabilities, including elasticity, workload separation, data security and simplicity
of performance
Data Exchange and Data Marketplace
- Private and Public Data Exchange
- Data Marketplace with ready-to-use and third-party datasets for data augmentation
- Diverse data including customer demographic data, time-series data, geospatial data
- Exploration and visualization using Snowsight
Data Lake for Machine Learning and Analytics
- Raw and external data sets in object stores
- External tables and direct queries in data lakes
- Native data formats of such as CSV, JSON, Parquet
Data Ingestion service and Continuous Data Pipelines
- Serverless continuous ingestion service Snowpipe
- Data ingestion best practices
- Bulk ingestion and scheduling data loads with tasks
- Table stream for capturing change data
Working with Semi-Structured Data
- Ingesting into native semi-structured data types without pre-processing
- Built-in functions for traversing, flattening, and nesting of semi-structured data
- Leveraging semi-structured state data for JavaScript Stored Procedures
- Complement learning with topics like geospatial data
Data Science and Machine Learning Concepts and Applications
- Data science applications
- Common machine learning vocabulary
- Machine learning workflow and pipeline
- Supervised and unsupervised machine learning
Data Science Toolset and Ecosystem
- Seamless connectivity using Snowflake connectors for languages such as Python, Spark, and R
- Notebook-based data science development environments
- Open source and many machine learning libraries including Scikit-Learn and more
- Partner platforms for data science automation and democratization around AutoML
- Partner platforms for deployment and practices with MLOps
Exploratory Data Analysis and Feature Engineering
- Descriptive exploratory data analysis using statistical and analytic functions
- Visual exploratory data analysis using popular and relevant libraries
- Employ common feature selection and feature engineering techniques
- Advanced SQL functions for data transformation at scale
Machine Learning Model Development and Tuning
- Supervised learning: linear regression with popular ML libraries
- Supervised learning: classification using techniques such as logistic regression, random forests, gradient boosts and more
- Identifying, using, and interpreting metrics to evaluate models and performance
- Unsupervised learning
Model Management and Deployment at Scale
- Deploying machine learning models using scalable framework
- External functions to support prediction and data augmentation through APIs
- Extensive partner ecosystem for automation around AutoML and operationalization using MLOps practices
- Using Snowflake capabilities including Snowpipe, table stream, and tasks for continuous data pipelines to update machine learning models
- Storing machine learning results in Snowflake
Visualizing and Collaborating on Data Science and Machine Learning Results
- Seamless connectivity to BI tools for reporting and analytics
- Communicating machine learning results
- Collaborating on models by sharing results with data sharing techniques
- Replicating your raw and processed data across region and cloud providers including AWS, Azure, and GCP
Course Objectives
By the end of this class you will be able to:
- Collect and access data from Snowflake Data Marketplace and other sources
- Manage and architect data lakes and real time streams
- Employ Snowflake best practices for developing or querying semi-structured and other data types
- Work with supervised and unsupervised machine learning models using some of the most relevant open source framework and libraries
- Formulate data science and machine learning workflow and data pipelines
- Manage and deploy machine learning models at scale with APIs
- Visualize and collaborate on machine learning results
Target Audience
Who should attend this course?
- Data scientists who build and train machine learning models
- Data scientists and data analysts who use the machine learning models to conduct predictive and prescriptive analytics
Inclusions
With CCS Learning Academy, you’ll receive:
- Instructor-led training
- Training Seminar Student Handbook
- Pre and Post assessments/evaluations
- Collaboration with classmates (not currently available for self-paced course)
- Real-world learning activities and scenarios
- Exam scheduling support*
- Enjoy job placement assistance for the first 12 months after course completion.
- This course is eligible for CCS Learning Academy’s Learn and Earn Program: get a tuition fee refund of up to 50% if you are placed in a job through CCS Global Tech’s Placement Division*
- Government and Private pricing available.*
*For more details call:Â 858-208-4141Â or email:Â training@ccslearningacademy.com; sales@ccslearningacademy.com