Snowpark ML APIs and the Model Registry
Snowflake
| Expert
- 10 videos | 1h 20m 15s
- Includes Assessment
- Earns a Badge
Snowflake has several powerful AI/ML features. These are available under two broad categories: Snowflake Cortex for LLM-related activities and Snowflake ML for more traditional ML model-building. In this course, explore how Snowflake integrates AI/ML capabilities across its platform, how Snowpark ML APIs support model training with popular libraries, and how to perform hyperparameter tuning to optimize model performance. Next, learn how to configure Python and Jupyter for Snowflake ML and set up a virtual environment to run a Jupyter Notebook that leverages Snowflake ML APIs. Finally, discover how to connect to Snowflake using the Snowpark API, work with the Snowflake Model Registry, and manage models. Upon course completion, you will be able to outline Snowpark ML APIs and the Snowflake Model Registry.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Identify snowflake ai/ml offerings across both snowflake cortex and snowflake ml and the functionality available across the platform
Outline support in snowpark ml for model training using scikit-learn, xgboost, and lightgbm, as well as for hyperparameter tuning
Configure a python virtual environment to run a jupyter notebook that uses snowflake ml apis
Connect to snowflake from jupyter and use the snowpark api from python -
Utilize the snowflake ml apis to compute correlation matrices, construct pipelines, and fit models
Recognize the process and benefits of registering a model version
Access the snowflake model registry and register a model with it, then consume a model by specifying the model name
Register models and versions, view model artifacts, delete model versions, and invoke model methods dynamically
Summarize the key concepts covered in this course
IN THIS COURSE
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1m 34sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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11mAfter completing this video, you will be able to identify Snowflake AI/ML offerings across both Snowflake Cortex and Snowflake ML and the functionality available across the platform. FREE ACCESS
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3. Snowpark ML APIs for Model Training and Hyperparameter Tuning8m 19sUpon completion of this video, you will be able to outline support in Snowpark ML for model training using scikit-learn, xgboost, and lightgbm, as well as for hyperparameter tuning. FREE ACCESS
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4. Configuring Python and Jupyter for Snowflake ML10m 31sLearn how to configure a Python virtual environment to run a Jupyter Notebook that uses Snowflake ML APIs. FREE ACCESS
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5. Connecting to Snowflake from Jupyter9m 14sDuring this video, discover how to connect to Snowflake from Jupyter and use the Snowpark API from Python. FREE ACCESS
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6. Using Snowflake ML APIs for Correlation, Pipelines, and Models9m 36sFind out how to utilize the Snowflake ML APIs to compute correlation matrices, construct pipelines, and fit models. FREE ACCESS
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7. Use the Snowflake Model Registry4m 41sAfter completing this video, you will be able to recognize the process and benefits of registering a model version. FREE ACCESS
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8. Registering Models with the Snowflake Model Registry10m 11sDiscover how to access the Snowflake Model Registry and register a model with it, then consume a model by specifying the model name. FREE ACCESS
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9. Working with Models, Versions, and Artifacts13mDuring this video, you will learn how to register models and versions, view model artifacts, delete model versions, and invoke model methods dynamically. FREE ACCESS
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10. Course Summary2m 9sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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