Data Science Toolkit Integration

Data Science Toolkit Integration

This Data Science Toolkit provides the layer to accelerate time to value from your data scientists work and actual results in production.

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About this course

One of the biggest challenges data science and machine learning teams face is integrating their machine learning models with production systems. Very often these are two different systems and require integration work and maintenance, creating additional burdens for DevOps teams.  The DSTI helps resolve these pain points!
 
Goal: By the end of this course, users will be able to utilize the data science toolkit integration and recognize the benefits of digital workplace and digital commerce applications.
 
Course Objectives:
  • Identify what the data science toolkit integration is and the components that fall under it.
  • Build and deploy machine learning models at scale in Fusion using Seldon Core.
  • Integrate your model using the index and query pipelines.

Before starting this course

Prerequisites: None!
What you should know: Experience with machine learning principles and building models is necessary in order to be successful in this course. Background knowledge of docker repositories, python, Scikit-learn, TensorFlow, PyTorch, etc. is also helpful. If you are brand new to Fusion or are unfamiliar with its basic components, take our Fusion Architecture eLearning first prior to completing this course.
Duration: ~50 minutes 

 

Curriculum50 Minutes

  • Lexicon
  • Data Science Toolkit Integration
  • Data Science Toolkit Cheat Sheet
  • Course Feedback

About this course

One of the biggest challenges data science and machine learning teams face is integrating their machine learning models with production systems. Very often these are two different systems and require integration work and maintenance, creating additional burdens for DevOps teams.  The DSTI helps resolve these pain points!
 
Goal: By the end of this course, users will be able to utilize the data science toolkit integration and recognize the benefits of digital workplace and digital commerce applications.
 
Course Objectives:
  • Identify what the data science toolkit integration is and the components that fall under it.
  • Build and deploy machine learning models at scale in Fusion using Seldon Core.
  • Integrate your model using the index and query pipelines.

Before starting this course

Prerequisites: None!
What you should know: Experience with machine learning principles and building models is necessary in order to be successful in this course. Background knowledge of docker repositories, python, Scikit-learn, TensorFlow, PyTorch, etc. is also helpful. If you are brand new to Fusion or are unfamiliar with its basic components, take our Fusion Architecture eLearning first prior to completing this course.
Duration: ~50 minutes 

 

Curriculum50 Minutes

  • Lexicon
  • Data Science Toolkit Integration
  • Data Science Toolkit Cheat Sheet
  • Course Feedback