Course Overview
This 3-day, role-specific course is intended for participants interested in developing skills and experience using Snowflake Cloud Data Platform for data science workloads. The participant will gain exposure to the rich features of Snowflake, diverse machine learning datasets, relevant and popular open-source ML frameworks and libraries, and model deployment practices that will provide practical skills applicable to data science jobs. This course consists of lectures, demos, hands-on labs, and discussions.
Prerequisites
- Basic knowledge of SQL is required
- Foundational knowledge of databases
- Python or some other object-oriented programming language
Audience Profile
- Data scientists who build and train machine learning models
- Data scientists and data analysts who use machine learning models to conduct predictive and prescriptive analytics
Learning Objectives:
- Describe Snowflake's key features and architecture.
- 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 frameworks and libraries.
- Formulate data science and machine learning workflows and data pipelines.
- Manage and deploy machine learning models at scale with APIs.
- Visualize and collaborate on machine learning results.
Content Outline
- Data science applications
- Common machine-learning vocabulary
- Machine learning workflow and pipeline
- Snowflake Cloud Data Platform Overview
- Three-tiered architecture
- Snowflake UI and core capabilities, including elasticity, workload separation, data security, and simplicity of performance
- Built-in functions for traversing, flattening, and nesting of semi-structured data
- Seamless connectivity using Snowflake connectors for languages such as Python, Spark, and C++
- Notebook-based data science development environments
- Raw and external data sets in object stores
- External tables and direct queries in data lakes
- Native data formats such as CSV, JSON, and Parquet
- Ingesting into native semi-structured data types without pre-processing
- Snowflake data ingestion best practices
- Serverless continuous ingestion service Snowpipe
- Diverse data, including customer demographic data, time-series data, geospatial data
- Private and Public Data Exchange
- Data Marketplace with ready-to-use and third-party datasets for data augmentation
- Sampling data
- Cloning data and utilizing Time Travel
- Data cleansing techniques to address duplicates, missing values, and outliers
- Bulk ingestion and scheduling data loads with tasks
- Table stream for capturing change data
- Exploration and visualization using Snowsight
- Descriptive exploratory data analysis using statistical, analytic, and approximation functions
- Visual exploratory data analysis using popular and relevant libraries
- Employ standard feature selection and feature engineering techniques
- Advanced SQL functions for data transformation at scale
- Transform data and perform feature engineering with Snowpark
- 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
- Developing models using a variety of popular machine learning libraries, including Scikit-Learn and more
- Communicating machine learning results
- Integrating with partner platforms for data science automation and democratization around AutoML
- Storing machine learning results in Snowflake
- Deploying machine learning models using scalable frameworks
- Creating external functions to support prediction and data augmentation through APIs
- Utilizing partner platforms for deployment and practices with ML Ops
- Operationalizing models with Snowflake’s extensive partner ecosystem using automation (AutoML) and ML Ops practices
- Using Snowflake capabilities, including Snowpipe, table stream, and tasks for continuous data pipelines to update machine learning models
FAQs
A: Snowflake Training students need...
- To Bring A Laptop
- A Snowflake-Compatible Browser
A: We would always recommend you attend the live session to practice & clarify the doubts instantly and get more value from your investment. However, if, due to some contingency, you have to skip the class, Radiant Techlearning will help you with the recorded session of that particular day. However, those recorded sessions are not meant only for personal consumption and NOT for distribution or any commercial use.
A: The learners will be enthralled as we engage them in the natural world and Oriented industry projects during the training program. These projects will improve your skills and knowledge and give you a better experience. These real-time projects will help you a lot in your future tasks and assignments.
A: It will be an added advantage if you have any technical knowledge. However, we will arrange a few additional sessions to train you on the basics.
A: You can request a refund if you do not wish to enroll in the course.
A: Yes, you can.
A: We adhere to the highest Internet security standards. Any data that is kept is not disclosed to outside parties.
A: It is recommended but optional. Being acquainted with the primary course material will enable students and the trainer to move at the desired pace during classes. You can access courseware for most vendors.
A: You can buy online from the page by clicking on " Buy Now". You can view alternate payment methods on the payment options page.
A: Yes, students can pay from the course page.
A: The course completion certification will be awarded to all the professionals who have completed the training program & the project assignment given by your instructor. Using the certificate in your future job interviews will surely help you to l& your dream job.