IBM - AI Workflow: Enterprise Model Deployment
- Offered byCoursera
AI Workflow: Enterprise Model Deployment at Coursera Overview
Duration | 9 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
AI Workflow: Enterprise Model Deployment at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 5 of 6 in the IBM AI Enterprise Workflow Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Advanced Level
- Approx. 9 hours to complete
- English Subtitles: English
AI Workflow: Enterprise Model Deployment at Coursera Course details
- This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.
- This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies.
- By the end of this course you will be able to:
- 1. Use Apache Spark's RDDs, dataframes, and a pipeline
- 2. Employ spark-submit scripts to interface with Spark environments
- 3. Explain how collaborative filtering and content-based filtering work
- 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming
- 5. Analyze hyperparameters in machine learning models on Apache Spark
- 6. Deploy machine learning algorithms using the Apache Spark machine learning interface
- 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning
- Who should take this course?
- This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.
- What skills should you have?
- It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
AI Workflow: Enterprise Model Deployment at Coursera Curriculum
Deploying Models
Introduction to Data at Scale
Introduction to Spark
Model Management and Deployment in Watson Studio
Data at scale: Through the Eyes of Our Working Example
Optimizing Performance in Python
High Performance Computing
Apache Spark (Hands-On)
Spark-submit
Docker Containers: Through the Eyes of our Working Example
On Containers and Docker
Docker Installation and Setup
NVIDIA Docker
Getting Started with Docker
Getting Started with Flask
Putting it all Together (Hands-On Tutorial)
More on Containers
Watson Machine Learning: Through the Eyes of Our Working Example
Getting Started (Hands-on)
Tutorial (Hands-on)
Summary/Review
Check for Understanding
Check for Understanding
Check for Understanding
End of Module Quiz
Deploying Models using Spark
Introduction to Spark Machine Learning
Spark Recommendations
Recommenders
Introduction to Model Deployment Case Study
Spark Machine Learning: Through the Eyes of Our Working Example
Spark Pipelines
Spark Supervised Learning
Spark Unsupervised Learning (Hands-On)
Model
Spark Recommenders: Through the Eyes of Our Working Example
Recommendation Systems
Recommendation Systems in Production
Model Deployment: Through the Eyes of Our Working Example
Getting Started (Hands-On)
Summary/Review
Check for Understanding
Check for Understanding
Check for Understanding
End of Module Quiz
AI Workflow: Enterprise Model Deployment at Coursera Admission Process
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