AI Workflow: Enterprise Model Deployment
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
Monthly Subscription Starting at AED 99 + VAT
- Level Expert
- Duration 9 hours
- Course by IBM
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Offered by
About
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.Modules
Data at scale
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2
Videos
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5
Readings
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1
Assignment
1
Assignment
- Check for Understanding
2
Videos
- Introduction to Data at Scale
- Introduction to Spark
5
Readings
- Data at scale: Through the Eyes of Our Working Example
- Optimizing Performance in Python
- High Performance Computing
- Apache Spark (Hands-On)
- Spark-submit
Docker and Containers
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8
Readings
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1
Assignment
1
Assignment
- Check for Understanding
8
Readings
- 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
TUTORIAL: Watson Machine Learning
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1
Videos
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3
Readings
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1
Assignment
1
Assignment
- Check for Understanding
1
Videos
- Model Management and Deployment in Watson Studio
3
Readings
- Watson Machine Learning: Through the Eyes of Our Working Example
- Getting Started (Hands-on)
- Tutorial (Hands-on)
End of module review & evaluation
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1
Readings
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1
Assignment
1
Assignment
- End of Module Quiz
1
Readings
- Summary/Review
Spark Machine Learning
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1
Videos
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5
Readings
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1
Assignment
1
Assignment
- Check for Understanding
1
Videos
- Introduction to Spark Machine Learning
5
Readings
- Spark Machine Learning: Through the Eyes of Our Working Example
- Spark Pipelines
- Spark Supervised Learning
- Spark Unsupervised Learning (Hands-On)
- Model
Spark Recommenders
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2
Videos
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3
Readings
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1
Assignment
1
Assignment
- Check for Understanding
2
Videos
- Spark Recommendations
- Recommenders
3
Readings
- Spark Recommenders: Through the Eyes of Our Working Example
- Recommendation Systems
- Recommendation Systems in Production
Case Study: Model Deployment
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1
Videos
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2
Readings
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1
Assignment
1
Assignment
- Check for Understanding
1
Videos
- Introduction to Model Deployment Case Study
2
Readings
- Model Deployment: Through the Eyes of Our Working Example
- Getting Started (Hands-On)
End of module review & evaluation
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1
Readings
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1
Assignment
1
Assignment
- End of Module Quiz
1
Readings
- Summary/Review
Auto Summary
"AI Workflow: Enterprise Model Deployment" is an advanced course designed for seasoned data science professionals focused on the deployment of machine learning models in large enterprise environments. As the fifth installment in the IBM AI Enterprise Workflow Certification specialization, this course builds on prior knowledge acquired in the preceding modules, emphasizing the continuity of the learning journey. Led by IBM on Coursera, this course delves into the intricacies of deploying models using Apache Spark, a widely-used framework for running machine learning operations. Participants will gain hands-on experience with best practices in data manipulation, model training, tuning, and the deployment of recommender systems. Key competencies include utilizing Apache Spark's RDDs, dataframes, and pipelines, interfacing with Spark environments using spark-submit scripts, and understanding collaborative and content-based filtering mechanisms. Additionally, learners will build and deploy data ingestion pipelines using Apache Spark and Apache Spark streaming, analyze hyperparameters, and deploy machine learning algorithms using the Apache Spark ML interface. The course culminates with deploying a machine learning model from Watson Studio to Watson Machine Learning. Ideal for experienced data scientists seeking to expand their expertise in enterprise AI deployment, this course requires participants to have completed the first four courses in the specialization. Prerequisites include a thorough understanding of linear algebra, probability theory, statistical concepts, and machine learning techniques, as well as proficiency in Python and familiarity with IBM Watson Studio. With a duration of 540 minutes, this expert-level course is available through Coursera's Starter subscription. It's a must for professionals aiming to master the deployment of AI models at scale in enterprise settings.
Mark J Grover
Ray Lopez, Ph.D.