Free Download Practical MLOps for Data Scientists & DevOps Engineers - AWS
Published 7/2023
Created by Manifold AI Learning ®
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 128 Lectures ( 23h 57m ) | Size: 10.7 GB
Practical MLOps for Data Scientists , Machine Learning & DevOps Engineers - Implement MLOps - Deploy Models and Operate
What you'll learn
Configuring the CI/CD Pipeline for Machine Learning Projects
Ability to track the source code & training images, configuration files with Git Based Repository - AWS CodeCommit
Ability to Perform the Build using AWS CodeBuild
Ability to Deploy the Application on Server using AWS CodeDeploy
Orchestrate the MLOps steps using AWS CodePipeline
Identify appropriate AWS services to implement ML solutions
Perform the Load testing
Monitoring the End Point Performance
Monitoring the Model Drift
The ability to follow model-training best practices
The ability to follow deployment best practices
The ability to follow operational best practices
Requirements
Basic knowledge of AWS
Account with AWS for practical Hand-On
Basic knowledge of Machine Learning & Deep Learning
Description
This course - Practical MLOps for Data Scientists & DevOps Engineers with AWS is intended for individuals who wants to perform an artificial intelligence/machine learning (AI/ML) development or data science role as close to Production Level working. This course helps you in improving your ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for given business problems by using the AWS Cloud with Practices of DevOps for Machine Learning .Right now, you may be aware of basics of Machine learning, but skills expected by employer is - more than what you can run from local notebook.From Employer perspective, its expected that Candidates to have :· The ability to follow model-training best practices on Large Datasets on cloud· The ability to follow deployment best practices so that it will work always· The ability to follow operational best practices so that there will be Zero downtimeIn short, you are expected to solve the Business problem by implementing on the dataset, not just work on the personal laptop.In this learning journey of this course, we will follow the structured learning journey, which takes you in a logical way to understand the topics in a clear and detailed manner with relevant Practical Exercises/Demo.The course structure is as follows:Section 1 : About AWSMLOPS Course and InstructorSection 2 : Introduction to MLOpsSection 3 : DevOps for Data ScientistsSection 4: Getting Started with AWSSection 5: Linux Basics for MLOpsSection 6: Source code Management using GIT - AWS CodeCommitSection 7: Crash Course on YAMLSection 8: AWS CodeBuildSection 9: AWS CodeDeploySection 10: AWS CodePipelineSection 11 : Docker ContainersSection 12 : Practical MLOps - Amazon SagemakerSection 13 : Feature Engineering - Feature Store in SagemakerSection 14: Training, Tuning & Deploying the ModelSection 15 : Create Custom ModelsSection 16 : MLOps Sagemaker PipelinesAll the source code is shared on github, which ensures that- you get to access from anywhere and always have the latest version.Below are the Tools, Technologies and Concepts covered as part of this Course:· Ingestion/Collection· Processing/ETL· Data analysis/visualization· Model training· Model deployment/inference· Operational Aspectes· AWS ML application services· Notebooks and integrated development environments (IDEs)· AWS CodeCommit· Amazon Athena· AWS Batch· Amazon EC2· Amazon Elastic Container Registry (Amazon ECR)· AWS Glue· Amazon SageMaker· Amazon CloudWatch· AWS Lambda· Amazon S3
Who this course is for
Anyone preparing for Data Science , Machine Learning & Deep Learning Interviews
Anyone interested in learning how Machine Learning is implemented on Large scale data
Anyone interested in AWS cloud-based machine learning and data science
Anyone looking to learn the best practices to deploy the Machine Learning Models on Cloud
Anyone looking to learn the best practices to Operationalize the Machine Learning Models
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