Snowflake Cortex Masterclass Hands-On

Snowflake Cortex Masterclass Hands-On, available at $54.99, has an average rating of 4.68, with 201 lectures, 9 quizzes, based on 25 reviews, and has 1472 subscribers.

You will learn about Everything about Snowflake Cortex, the new AI & ML platform from Snowflake How to implement end-to-end ML pipelines using both Snowpark and Snowpark ML How to develop ML experiments with Snowflake using notebooks and code snippets How to use the ML-powered classes and functions from Snowflake Cortex How to call the new LLM functions from Snowflake Cortex How to use Snowflake Copilot and other super-new LLM UI features in Snowsight How to integrate Snowflake with ChatGPT using the OpenAI REST API How to use Snowpark over in-memory Pandas DataFrames This course is ideal for individuals who are Data Scientists who want to learn about all AI & ML opportunities in Snowflake or Data Analysis looking how to use the new ML-based and LLM functions or Data and Software Engineers looking to expand into AI & ML on Snowflake or Project Managers looking for a 360 degree view of the new Snowflake Cortex platform or Data Architects willing to understand fast how Snowflake Cortex is built or Anyone else looking for a high-level (but detailed) picture of Snowflake Cortex or Anyone looking to understand the code and query pushdown model of Snowflake or Anyone looking how to save money on Snowflake using new built-in ML and LLM functions or Anyone looking for other Snowflake features yet to come in the AI & ML area It is particularly useful for Data Scientists who want to learn about all AI & ML opportunities in Snowflake or Data Analysis looking how to use the new ML-based and LLM functions or Data and Software Engineers looking to expand into AI & ML on Snowflake or Project Managers looking for a 360 degree view of the new Snowflake Cortex platform or Data Architects willing to understand fast how Snowflake Cortex is built or Anyone else looking for a high-level (but detailed) picture of Snowflake Cortex or Anyone looking to understand the code and query pushdown model of Snowflake or Anyone looking how to save money on Snowflake using new built-in ML and LLM functions or Anyone looking for other Snowflake features yet to come in the AI & ML area.

Enroll now: Snowflake Cortex Masterclass Hands-On

Summary

Title: Snowflake Cortex Masterclass Hands-On

Price: $54.99

Average Rating: 4.68

Number of Lectures: 201

Number of Quizzes: 9

Number of Published Lectures: 201

Number of Published Quizzes: 9

Number of Curriculum Items: 210

Number of Published Curriculum Objects: 210

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Everything about Snowflake Cortex, the new AI & ML platform from Snowflake

How to implement end-to-end ML pipelines using both Snowpark and Snowpark ML

How to develop ML experiments with Snowflake using notebooks and code snippets

How to use the ML-powered classes and functions from Snowflake Cortex

How to call the new LLM functions from Snowflake Cortex

How to use Snowflake Copilot and other super-new LLM UI features in Snowsight

How to integrate Snowflake with ChatGPT using the OpenAI REST API

How to use Snowpark over in-memory Pandas DataFrames

Who Should Attend

Data Scientists who want to learn about all AI & ML opportunities in Snowflake

Data Analysis looking how to use the new ML-based and LLM functions

Data and Software Engineers looking to expand into AI & ML on Snowflake

Project Managers looking for a 360 degree view of the new Snowflake Cortex platform

Data Architects willing to understand fast how Snowflake Cortex is built

Anyone else looking for a high-level (but detailed) picture of Snowflake Cortex

Anyone looking to understand the code and query pushdown model of Snowflake

Anyone looking how to save money on Snowflake using new built-in ML and LLM functions

Anyone looking for other Snowflake features yet to come in the AI & ML area

Target Audiences

Data Scientists who want to learn about all AI & ML opportunities in Snowflake

Data Analysis looking how to use the new ML-based and LLM functions

Data and Software Engineers looking to expand into AI & ML on Snowflake

Project Managers looking for a 360 degree view of the new Snowflake Cortex platform

Data Architects willing to understand fast how Snowflake Cortex is built

Anyone else looking for a high-level (but detailed) picture of Snowflake Cortex

Anyone looking to understand the code and query pushdown model of Snowflake

Anyone looking how to save money on Snowflake using new built-in ML and LLM functions

Anyone looking for other Snowflake features yet to come in the AI & ML area

IMPORTANT:This course requires an INTERMEDIATE level and it may NOT be for you if you just recently started with Snowflake and/or Data Science, relational databases, with SQL and/or Python programming. It occasionally deals with advanced notions that are not explained here. Check the minimum Requirements for this course, and the “What this course is NOT about” section below, before buying this course!

What is Snowflake Cortex

Snowflake got heavily involved into AI and ML only in the past two years. I know, because I’ve been around since the beginning. In Jan 2021 I was selected by them as a “Snowflake Data Superhero”. And in my last two years alone I passed many DS and ML certification exams (see below).

In mid-2023 they came up with Snowpark ML. Then with some built-in ML-powered functions, about regression and classification. The new Model Registry from Snowpark ML – added in Jan 2024 – allows now end-to-end ML on the platform.

You’ll see side-by-side data science experiments I will present to you “the old way”, on Snowflake. Many of them with integrations with ChatGPT (about which I talked in detail in another course of mine). To compare them now with the new LLM functions, as Snowflake decided to host their own Large Language Models.

Most of these (and other features yet to come) are now presented under the Snowflake Cortex umbrella.

What you will learn

High-level picture of the new Snowflake Cortex AI & ML platform.

Detailed views on each of the Snowflake Cortex areas.

How ML experiments were done on Snowflake before Cortex.

How ML experiments can be implemented today with the Snowpark ML APIs, part of Snowflake Cortex.

End-to-end Machine Learning with Snowpark ML and its Model Registry.

How to use the new regression and classification ML-powered classes and functions, in Snowflake Cortex.

How to call the new LLM functions from Snowflake Cortex, and compare them with ChatGPT.

What to expect from Snowflake Copilot and other incoming Snowflake features in Cortex.

What was the roadmap and what are the future plans of Snowflake for Snowflake Cortex or in the AI & ML areas.

What this course is NOT about

I will not teach you data science and machine learning here from ground up. You are expected to have some basic knowledge about ML, DL, LLMs

I will not teach you about many other areas in Snowflake. You are expected to have basic knowledge of Snowflake and data warehouses in general.

I will not teach programming in Python or SQL. It’s a hands-on course and you are expected to have some basic knowledge in this area. However, I may come-up with some small Streamlit apps, but I’ll keep everything simple and easy to understand.

This will not cover EVERYTHING you can do as data science in Snowflake. While you will see experiments “the old way” with scikit-learn, or integrations with ChatGPT, these are not part of Cortex! We have to limit mostly to Snowflake Cortex, as that’s a huge platform.

While Snowpark Container Services are also very new and they also target mostly ML and DL experiments (especially the new containers with GPUs), there will not be enough time to go deeper in detail. And this is also a very specialized and more difficult platform to understand. I may come up later on with a more advanced course on SPCS, but for now this course does not require such an advanced level of preparation.

Who I am

The only world-class expert from Canada selected for the Snowflake Data Superhero program in 2021.

SnowPro Certification SME (Subject Matter Expert) – many SnowPro exam questions have been created by me.

Passed four SnowPro certification exams to date (with no retakes): Core, Architect, Data Engineer, Data Analyst.

Specialized in Snowflake for the past few years: I worked for Snowflake Partner companies. I served dozens of clients in this capacity or as an independent consultant. Today I share my knowledge with highly specialize courses on Snowflake.

A few of my latest Data Science and Machine Learning certifications

AWS Certified in Machine Learning

Microsoft Azure Data Scientist Associate

Microsoft Azure AI Engineer Associate

Microsoft Azure AI Fundamentals

TensorFlow Developer Certificate

Alteryx Machine Learning Fundamentals Certified

Dataiku ML Practitioner Certified

Dataiku MLOps Practitioner Certified

Neo4j Graph Data Science Certified

TigerGraph Graph Algorithms for Machine Learning

This course truly offers a complete coverage of the new Snowflake Cortex, and my intention is to update it frequently. Enroll today, and keep this course forever!

Course Curriculum

Chapter 1: Introduction to Snowflake Cortex

Lecture 1: This course might NOT be right for you if

Lecture 2: Course Structure and Content

Lecture 3: Welcome to This Course

Lecture 4: All About This Course (FAQ Post)

Lecture 5: SnowPro Snowflake Certifications: Added Material!

Lecture 6: Roadmap to Snowflake Cortex

Lecture 7: Quick Tips: SQL Query Without Typing SQL

Lecture 8: Related Features and Technologies

Lecture 9: Overview of Snowflake Cortex

Lecture 10: Quick Tips: TRANSLATE LLM Function

Lecture 11: Quick Checkpoint: About Quick Checkpoints

Chapter 2: ML Pipelines on Datasets (outside Snowflake)

Lecture 1: About this Section

Lecture 2: Quick Tips: Correlation Heatmap

Lecture 3: Introduction: Machine Learning Basics

Lecture 4: Introduction: ML Pipeline Phases

Lecture 5: Introduction: ML Pipeline Architectures

Lecture 6: Quick Checkpoint: What if You Already Know All This?

Lecture 7: Data Collection: Time Series Generation

Lecture 8: Data Collection: Make Regression/Classification

Lecture 9: Data Collection: Realistic Fake Data Generation

Lecture 10: Data Collection: Data Access

Lecture 11: Data Collection: Data Split

Lecture 12: Data Collection: Overview

Lecture 13: Quick Tips: Fake but Realistic Data Generation

Lecture 14: Data Exploration: Overview

Lecture 15: Data Exploration: Correlation Matrix Heatmap

Lecture 16: Data Exploration: Pandas Profiling

Lecture 17: Quick Checkpoint: About Pandas Profiling

Lecture 18: Data Wrangling: Overview

Lecture 19: Data Wrangling: Feature Engineering with Pandas DataFrame

Lecture 20: Data Wrangling: Data Preprocessing with Transformers

Lecture 21: Data Wrangling: Data Preprocessing with Pipeline

Lecture 22: Quick Checkpoint: About Basic ML on Datasets

Lecture 23: Quick Tips: SUMMARIZE LLM Function

Lecture 24: Model Training: Overview

Lecture 25: Model Training: Regression

Lecture 26: Model Training: Classification

Lecture 27: Model Validation: Manual Hyperparameter Optimization

Lecture 28: Model Validation: Manual Cross-Validation

Lecture 29: Model Validation: GridSearchCV for Regression

Lecture 30: Model Validation: RandomizedSearchCV for Classification

Lecture 31: Quick Checkpoint: About Model Validation

Lecture 32: Model Evaluation: Performance Metrics for Regression

Lecture 33: Model Evaluation: Performance Metrics for Classification

Lecture 34: Model Serving: Save/Load the Trained Model File

Lecture 35: Quick Tips: Signup for a Free Snowflake Trial Account

Chapter 3: ML Pipelines using Snowpark (before Cortex)

Lecture 1: About this Section

Lecture 2: Quick Tips: Uploading Files in Snowflake

Lecture 3: Introduction: Snowpark Components

Lecture 4: Introduction: Procedures and Functions from SQL

Lecture 5: Introduction: Snowpark for Python

Lecture 6: Introduction: Procedures and Functions from Python

Lecture 7: Introduction: Vectorized User-Defined Functions

Lecture 8: Introduction: Runtimes and Package Versions

Lecture 9: Introduction: Snowpark for ML Pipelines

Lecture 10: Data Collection: Populating with SQL Statements

Lecture 11: Data Collection: Synthetic Data Generation

Lecture 12: Data Collection: Faker Library in Python Worksheet

Lecture 13: Quick Tips: Easiest Way to Connect to Snowflake

Lecture 14: Data Collection: Uploading with SQL Scripts

Lecture 15: Data Collection: Uploading with Python Code

Lecture 16: Data Collection: Uploading from External Stages

Lecture 17: Data Collection: Uploading Other Datasets

Lecture 18: Data Collection: Sample Data Extraction

Lecture 19: Data Collection: Data Split

Lecture 20: Quick Checkpoint: About Ingesting Data in Snowflake

Lecture 21: Quick Tips: Correlation Heatmap in Snowflake

Lecture 22: Data Exploration: Snowsight Charts and Dashboards

Lecture 23: Data Exploration: Snowflake Partner Notebooks

Lecture 24: Data Exploration: Snowflake Notebooks

Lecture 25: Data Exploration: Overview

Lecture 26: Quick Tips: Data Profiling in Snowflake

Lecture 27: Quick Checkpoint: Pandas vs Snowpark Data Frames

Lecture 28: Feature Engineering: Pandas vs Snowpark DataFrames

Lecture 29: Feature Engineering: Using Pandas DataFrames

Lecture 30: Feature Engineering: Using Snowpark DataFrames

Lecture 31: Feature Engineering: Scalability Check with Python Worksheets

Lecture 32: Feature Engineering: Overview

Lecture 33: Quick Checkpoint: About the Python Worksheets

Lecture 34: Quick Tips: DataFrame Queries

Lecture 35: Data Preprocessing: When You Cannot Avoid Pandas

Lecture 36: Model Training: Sentiment Analysis in Local Mode

Lecture 37: Model Training: Sentiment Analysis with Stored Procedure

Lecture 38: Model Training: Overview

Lecture 39: Model Training: Sentiment Analysis with Imported Modules

Lecture 40: Model Training: House Predictions with Stored Procedure

Lecture 41: Model Serving: Overview

Lecture 42: Model Serving: Sentiment Predictions with UDFs

Lecture 43: Model Serving: Sentiment Predictions with SQL

Lecture 44: Model Serving: House Predictions with Vectorized UDF

Lecture 45: Model Serving: Introduction to Cachetools

Lecture 46: Model Serving: UDFs vs Vectorized UDFs

Instructors

Cristian Scutaru
Snowflake Guru, AWS/Azure/GCP Solutions Architect

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4 stars: 9 votes

5 stars: 16 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!