Beginning Mlops with Mlflow: Deploy Models in Aws Sagemaker, Google Cloud, and Microsoft Azure

Chapter 1: Getting Started: Data Analysis and Feature Engineering Chapter Goal: Establish the premise of the problem we want to solve with machine learning. Analyze several data sets and process them. No of pages - 30 pages Sub - Topics 1. Premise 4. Data analysis 5. Feature engineering Chapter 2: Building a Machine Learning Model Chapter Goal: Build a machine learning model on a data set / several data sets that we processed the data for in chapter 4.No of pages - 40 pagesSub - Topics: 1. Building the model 2. Training and testing the model 3. Validation and optimizing Chapter 3: What is MLOps? Chapter Goal: Introduce the reader to MLOps, various stages of automation in MLOps setups, automation with pipeline, and to CI/CD and CD Deployment. Pipelines for: source repo to deployment, prediction services, performance monitoring, etc Continuous Integration (source repo updated with new models), and Continuous Delivery (new models deployed). No of pages - 40 pages Sub -Topics 1. What is MLOps? 2. MLOps setups 3. Automation 4. CI/CD - Continuous Integration & Delivery 5. CD - Deployment Chapter 4: Introduction to MlFlowChapter Goal: Introduce the reader to MLFlow and how to incorporate MLFlow into our ML training process (PyTorch, Keras, TensorFlow) No of pages - 30 pages Sub - Topics: 1. What is MLFlow?2. MLFlow in PyTorch3. MLFlow in Keras4. MLFlow in TensorFlow Chapter 5: Deploying in AWS - 40 pages Chapter Goal: Guide the reader through the process of deploying an MLOps setup on AWS SageMaker. -Description: The chapter will walk the reader through AWS SageMaker and help them deploy their MLOps setup (data processing scripts, model train, test, validation scripts) in AWS.
Chapter 6: Deploying in Azure - 40 pages Chapter Goal: Guide the reader through the process of deploying an MLOps setup on Microsoft Azure.-Description: The chapter will walk the reader through Microsoft Azure and help them deploy their MLOps setup (data processing scripts, model train, test, validation scripts) in Azure. Chapter 7: Deploying in Google - 40 pages Chapter Goal: Guide the reader through the process of deploying an MLOps setup on Google Cloud.-Description: The chapter will walk the reader through Google Cloud and help them deploy their MLOps setup (data processing scripts, model train, test, validation scripts) in Google Cloud. Appendix A: a2ml - 20 pages Chapter Goal: This appendix chapter is optional and guides users through the process of deploying an MLOps setup using a2ml. -Description: The chapter will walk the reader through a2ml and help them deploy their MLOps setup (data processing scripts, model train, test, validation scripts) through a2ml.

Author: Sridhar Alla, Suman Kalyan Adari
Publisher: Apress
Published: 12/30/2020
Pages: 330
Binding Type: Paperback
Weight: 1.07lbs
Size: 9.21h x 6.14w x 0.72d
ISBN13: 9781484265482
ISBN10: 1484265483
BISAC Categories:
- Computers | Artificial Intelligence | General

About the Author
Sridhar Alla is the co-founder and CTO of Bluewhale, which helps big and small organizations build AI-driven big data solutions and analytics. He is a published author of books and an avid presenter at numerous Strata, Hadoop World, Spark Summit, and other conferences. He also has several patents filed with the US PTO on large-scale computing and distributed systems. He has extensive hands-on experience in several technologies, including Spark, Flink, Hadoop, AWS, Azure, Tensorflow, Cassandra, and others. He spoke on Anomaly Detection Using Deep Learning at Strata SFO in March of 2019 and at Strata London in October of 2019. He was born in Hyderabad, India and now lives in New Jersey, USA with his wife Rosie and daughter Evelyn. When he is not busy writing code, he loves to spend time with his family and also training, coaching, and organizing meetups.
Suman Kalyan Adari is an undergraduate student pursuing a BS degree in computer science at the University of Florida. He has been conducting deep learning research in the field of cybersecurity since his freshman year, and has presented at the IEEE Dependable Systems and Networks workshop on Dependable and Secure Machine Learning held in Portland, Oregon, USA in June of 2019. He is passionate about deep learning, and specializes in its practical uses in various fields such as image recognition, anomaly detection, natural language processing, targeted adversarial attacks, and more.