A Machine Learning pipeline is a way to codify and automate the workflow required to produce a machine learning model.
\nMachine learning pipelines consist of multiple sequential steps that do everything from data extraction and preprocessing to model training and deployment.<\/p>\n
An Azure Machine Learning pipeline is an independently executable workflow of a complete machine learning task.<\/p>\n
The Azure Machine Learning pipeline helps standardize best practices for building machine learning models, enabling the team to execute at scale and improving model building efficiency.<\/p>\n
The basis of a machine learning pipeline is to break down a complete machine learning task into a multi-step workflow. Each step is a manageable component that can be individually developed, optimized, configured and automated. Steps are connected through well-defined interfaces. The Azure Machine Learning pipeline service automatically orchestrates all dependencies between pipeline steps.<\/p>\n
It is a program that will allow you to respond to the needs of the future, develop strategies for analyzing data; prepare data for analysis; explore and visualize data; create models with data using Python programming language, distribute them to applications and software and process them.<\/p>\n
This course explores how to use machine learning (ML) pipeline to solve a real business problem in a project-based learning environment.<\/p>\n
Participants will learn each stage of the pipeline from the instructor and then apply this knowledge to complete a project that can solve business problems.<\/p>\n
By the end of the course, all participants will be able to successfully build a machine learning model using Amazon SageMaker to solve their chosen business problem.<\/p>\n
After completing this course, you will learn how to apply machine learning to a real-life business problem, including how to select the appropriate machine learning approach for a specific business problem, how to use a machine learning framework to solve a specific business problem, how to orchestrate, evaluate, deploy, and tune a machine learning model in Amazon SageMaker, and some of the best practices for designing scalable, cost-optimized, and secure machine learning pipelines in AWS.<\/p>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” scene_position=”center” text_color=”dark” text_align=”left” top_padding=”3%” overlay_strength=”0.3″ shape_divider_position=”bottom” bg_image_animation=”none” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_link_target=”_self” column_shadow=”none” column_border_radius=”none” width=”1\/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” overlay_strength=”0.3″ column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][toggles style=”minimal”][toggle color=”Extra-Color-2″ title=”About Training”][vc_column_text]\n
Overview of machine learning, including use cases, types of machine learning and key concepts<\/p>\n
Machine learning pipeline overview<\/p>\n
Introduction and approach to sample projects<\/p>\n
Introduction to Amazon SageMaker<\/p>\n
Amazon SageMaker and Jupyter notebooks<\/p>\n
Overview of problem formulation and deciding whether machine learning is the right solution<\/p>\n
Transforming a business problem into a machine learning problem<\/p>\n
Amazon SageMaker Ground Truth<\/p>\n
Amazon SageMaker Ground Truth<\/p>\n
Problem Formulation Exercise and Review<\/p>\n
Project work for Problem Formulation<\/p>\n
Overview of data collection and integration and data preprocessing and visualization techniques<\/p>\n
Data Preprocessing<\/p>\n
Choosing the right algorithm<\/p>\n
Format and split your data for education<\/p>\n
Loss functions and gradient descent to improve your model<\/p>\n
Create an education job in Amazon SageMaker<\/p>\n
How are classification models evaluated?<\/p>\n
How to evaluate regression models?<\/p>\n
Practice model training and evaluation<\/p>\n
Model Training and Evaluation<\/p>\n
Feature extraction, selection, creation and transformation<\/p>\n
Hyperparameter setting<\/p>\n
SageMaker hyperparameter optimization<\/p>\n
Feature Engineering<\/p>\n
Deploy, infer and monitor models in Amazon SageMaker<\/p>\n
Deploying edge machine learning[\/vc_column_text][\/toggle][toggle color=”Extra-Color-2″ title=”Who Should Receive the Training?”][vc_column_text]\n
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