AI Workflow: Enterprise Model Deployment

Go to Course: https://www.coursera.org/learn/ibm-ai-workflow-machine-learning-model-deployment

Introduction

### Course Review: AI Workflow: Enterprise Model Deployment If you are traversing the exciting yet complex world of data science and artificial intelligence, understanding the deployment of models within enterprise environments is critical. The course **"AI Workflow: Enterprise Model Deployment,"** which is part of the IBM AI Enterprise Workflow Certification specialization on Coursera, promises to equip you with essential skills for this very purpose. As the fifth course in the series, it builds on the previous lessons, giving you progressive knowledge and practical experience that culminates in effective model deployment at scale. #### Course Overview The foundational aspect of this course is its focus on deploying models for enterprise applications. It targets a niche in the data science field that not many professionals get to experience, making it all the more valuable. The course kicks off with an interactive introduction to **Apache Spark**—a powerful framework widely used for processing large datasets and deploying machine learning models efficiently. The structure of the course is meticulously crafted, emphasizing hands-on learning. Students are encouraged to engage with practical tasks utilizing not just Spark but also tools like **Docker** and **Watson Machine Learning**. #### Syllabus Breakdown 1. **Deploying Models** This initial week sets the stage for understanding model deployment. The emphasis here is on the tooling available to data scientists today and the critical decision-making involved in code optimization. Through a variety of hands-on activities, participants will interact with Apache Spark and Docker—two pivotal tools for modern data science workflows. 2. **Deploying Models using Spark** Diving deeper, the course explores deploying models with Spark. A critical theme is scalability, particularly relevant for enterprise-level operations. You’ll learn how Spark's capabilities extend beyond scikit-learn, making it an optimal choice for both training and prediction. This week focuses on the intricacies of recommendation systems, covering collaborative filtering and content-based approaches, along with hybrid systems. Moreover, students will culminate this week with a hands-on case study that draws on the concepts learned to solidify their understanding of model deployment in real-world scenarios. #### Why You Should Enroll - **Targeted Learning**: The course specifically addresses the less common but highly valuable area of model deployment, making it an excellent choice for data scientists looking to enhance their skill set. - **Hands-On Experience**: A significant amount of the learning occurs through practical engagement, allowing you to apply concepts in realistic contexts, which is essential for cementing knowledge. - **Building on Previous Knowledge**: By being part of a larger specialization, this course builds upon foundational concepts introduced in earlier courses, creating a cohesive learning journey that prepares you for complex challenges. - **Industry-Relevant Skills**: As businesses increasingly rely on data-driven solutions, skills in deploying scalable models will position you favorably in the job market. Companies are constantly seeking professionals who can integrate AI models into their operations effectively. - **Expertise from IBM**: Given that the course is provided by IBM, participants can expect a curriculum that aligns with industry standards and leverages IBM's extensive experience in AI and machine learning. #### Conclusion The **AI Workflow: Enterprise Model Deployment** course is a must for anyone serious about advancing their expertise in deploying AI solutions within enterprises. It offers well-structured content, interactive learning experiences, and opportunities to work with cutting-edge technologies. Enrolling in this course will not only enhance your technical capabilities but also increase your attractiveness to potential employers looking for skilled data scientists who can effectively bridge the gap between model development and application in real-world scenarios. If you've completed the previous courses in the specialization, don't miss the chance to deeply understand enterprise model deployment—your career may depend on it!

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.

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.