AI Workflow: Enterprise Model Deployment from Coursera

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.

What's inside

Syllabus

Deploying Models

Today data scientists have more tooling than ever before to create model-driven or algorithmic solutions, and it is important to know when to take the time to make code optimizations. This week we spend a lot of time performing hands on activities. We start this week by interacting with Apache Spark then progressing to a tutorial with Docker. We’ll wrap up the week working through a tutorial on Watson Machine Learning.

Deploying Models using Spark

This week is primarily focused on deploying models using Spark. The rationale to move to Spark almost always has to do with scale, either at the level of model training or at the level of prediction. Although the resources available to build Spark applications are fewer than those for scikit-learn, Spark gives us the ability to build in an entirely scaleable environment. We will also look at recommendation systems. Most recommender systems today are able to leverage both explicit (e.g. numerical ratings) and implicit (e.g. likes, purchases, skipped, bookmarked) patterns in a ratings matrix. The majority of modern recommender systems embrace either a collaborative filtering or a content-based approach. A number of other approaches and hybrids exist making some implemented systems difficult to categorize. We wrap the week up with our hands-on case study on Model Deployment.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Focuses on enterprise data science while emphasizing scalable solutions

Targets experienced data science practitioners who want to enhance their deployment skills

Requires a solid understanding of machine learning techniques and best practices

Assumes proficiency in Python and common data science libraries (NumPy, Pandas)

Emphasizes hands-on activities and case studies for practical application

Covers important topics such as model deployment, recommender systems, and Apache Spark

Reviews summary

In-depth ai workflow for enterprise deployment

Learners say this AI Workflow course gives a detailed overview of deploying models within an enterprise setting. According to reviews, this course is well-received and includes real-world examples and case studies. Students do note that the course does expect learners to have some familiarity with Scala, Docker, and Python prior to taking it.

Relevant examples and case studies

"great examples and real-world case"

"Very nice overview of recommendation systems and deployment to spark for scaling."

"very good course, i am find a lot of interesting things"

Complexity could be simplified

"I think I'd probably skip the Watson studios/python part since it just adds to the complexity without adding much value."

Some learners experienced missing data and content issues

"Missing data files, errors in course content, disconnects between stated goal and project files, typos, etc."

Includes one ungraded practice lab

"The practice is one lab ungraded"

Requires knowledge of Scala, Docker, and Python

"Please take note these courses assumes you have the skills like Scala, Dockers, Python etc."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AI Workflow: Enterprise Model Deployment with these activities:

Review basic statistics

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Reviewing basic statistics will help you grasp the fundamental concepts and techniques used throughout this course. This will give you a strong foundation for understanding the more complex concepts that follow.

Browse courses on Descriptive Statistics

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Revisit foundational statistical terms like mean, median, and standard deviation.
Brush up on concepts like probability distributions and sampling techniques.
Perhaps find a statistics refresher course or tutorial and complete it.

Review Linear Algebra

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Get a refresher on the fundamentals of linear algebra to strengthen your understanding of the course materials.

Browse courses on Linear Algebra

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Review basic concepts of vectors, matrices, and linear transformations.
Practice solving systems of linear equations.

Recall Probability Theory

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Brush up on the concepts of probability theory to enhance your ability to understand the statistical techniques used in the course.

Browse courses on Probability Theory

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Review the basics of probability distributions and random variables.
Practice calculating probabilities and expected values.

Seven other activities

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Show all ten activities

Assemble a Toolkit for Large-Scale Data Processing

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Enhance your knowledge and skills by creating a compilation of tools, resources, and best practices for large-scale data processing.

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Research and identify open-source tools and libraries for data processing on large datasets.
Document the tools and libraries in a comprehensive report or wiki.

Spark Hands-On

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Expose students to Apache Spark to improve their understanding of this commonly used framework for running machine learning models

Browse courses on Spark

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Begin Spark Tutorial
Complete Spark MLlib exercises
Test Sample Solutions

Reinforce Data Manipulation Skills

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Sharpen your data manipulation skills through targeted exercises to improve your proficiency in cleaning and organizing data.

Browse courses on Data Manipulation

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Complete coding exercises involving data cleaning and transformations using Python and Pandas.
Participate in online challenges or coding competitions focused on data manipulation.

Explore Best Practices for Model Training and Tuning

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Enhance your understanding of best practices for training and tuning machine learning models by following guided tutorials.

Browse courses on Model Training

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Find and study tutorials on model training strategies and hyperparameter optimization techniques.
Apply the techniques learned in the tutorials to your own projects or datasets.

Practice using Apache Spark

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Hands-on practice with Apache Spark will help you solidify your understanding and gain proficiency in using it for data manipulation and model deployment.

Browse courses on Apache Spark

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Find datasets and work through examples of data manipulation using Spark.
Build simple machine learning models using Spark ML and evaluate their performance.
Deploy models using Spark ML in a scalable environment.

Build a Personal Recommender System

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Solidify your understanding of recommender systems by building and deploying a personalized recommender system from scratch in Python.

Browse courses on Recommender Systems

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Gather and prepare a dataset for training the recommender system.
Implement collaborative filtering algorithms to generate recommendations.
Evaluate and improve the performance of your recommender system.

Develop a Model Deployment Strategy

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Put your understanding of model deployment into practice by creating a detailed strategy for deploying and monitoring a machine learning model.

Browse courses on Model Deployment

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Research and select appropriate tools and technologies for model deployment.
Design a CI/CD pipeline for automated model deployment.
Develop a monitoring and alerting system to track model performance.

Career center

Learners who complete AI Workflow: Enterprise Model Deployment will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists develop machine learning algorithms to solve a variety of business problems. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Data Analyst.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts help businesses identify and solve problems by using data and analysis. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Business Analyst.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Software Engineer.

See salaries and explore the career path for Software Engineer

Financial Analyst

Financial Analysts use financial data to make investment recommendations and help businesses make informed decisions. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Financial Analyst.

See salaries and explore the career path for Financial Analyst

Product Manager

Product Managers are responsible for developing and managing products that meet the needs of customers. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Product Manager.

See salaries and explore the career path for Product Manager

Data Engineer

Data Engineers build and maintain data pipelines. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Data Engineer.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment recommendations. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Business Intelligence Analyst

Business Intelligence Analysts use data to help businesses make informed decisions. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Business Intelligence Analyst.

See salaries and explore the career path for Business Intelligence Analyst

Project Manager

Project Managers are responsible for planning and executing projects. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Project Manager.

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Market Research Analyst

Market Research Analysts collect and analyze data to help businesses understand their customers and make informed decisions. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Market Research Analyst.

See salaries and explore the career path for Market Research Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to help businesses make informed decisions. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians collect, analyze, and interpret data to help businesses make informed decisions. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Statistician.

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Data Architect

Data Architects design and manage data systems. The AI Workflow: Enterprise Model Deployment course from IBM covers the skills needed to deploy models for use in large enterprises, including Apache Spark, data manipulation, model training, and model tuning. This course would be a valuable addition to the skillset of any aspiring Data Architect.

See salaries and explore the career path for Data Architect