Managing Models Using MLflow on Databricks from Pluralsight

This course will teach you how to manage the end-to-end lifecycle of your machine learning models using the MLflow managed service on Databricks.

The machine learning workflow involves many intricate steps to ensure that the model that you deploy to production is meaningful and robust. Managing this workflow manually is hard which is why the MLflow service which manages the integrated machine learning workflow end-to-end is game changing. Databricks makes this even easier by offering a managed version of this service that is simple, intuitive, and easy to use.

In this course, Managing Models Using MLflow on Databricks, you will learn to create an MLflow experiment and use it to track the runs of your models.

First, you will see how you can use explicit logging to log model-related metrics and parameters and view, sort, and compare runs in an experiment.

Next, you will then see how you can use autologging to track all relevant parameter, metrics, and artifacts without you having to explicitly write logging code.

Then, you will see how you can use MLflow to productionize and serve your models, and register your models in the model registry and perform batch inference using your model.

After that, you will learn how to transition your model through lifecycle stages such as Staging, Production, and Archived.

Finally, you will see how you can work with custom models in MLflow. You will also learn how to package your model in a reusable format as an MLflow project and run training using that project hosted on Github or on the Databricks file system.

When you are finished with this course, you will have the skills and knowledge to use MLflow on Databricks to manage the entire lifecycle of your machine learning model.

What's inside

Syllabus

Course Overview

Tracking Models Using MLflow

Productionizing and Serving Models

Using Custom Models and MLflow Projects

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces learners to the MLflow managed service on Databricks, teaching learners the necessary skills and knowledge to use MLflow to manage the entire lifecycle of their machine learning model

The course teaches learners how to create an MLflow experiment and use it to track the runs of their models, allowing learners to ensure that the model that they deploy to production is meaningful and robust

Instructs students on how to use MLflow to productionize and serve their models, which is a core skill for ensuring that their models are effectively deployed and operationalized

Teaches learners how to register their models in the model registry and perform batch inference using their model, which is essential for managing and maintaining models in production

Provides learners with the knowledge and skills to transition their model through lifecycle stages such as Staging, Production, and Archived, enabling them to effectively manage the lifecycle of their models

Instructs learners on how to work with custom models in MLflow, giving them the flexibility and control to use their own models and components within the MLflow framework

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 Managing Models Using MLflow on Databricks with these activities:

Review basic Python syntax

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This course requires a basic understanding of Python syntax. Reviewing basic Python syntax will help you be successful.

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Read through a Python tutorial
Complete some Python coding exercises

Review basic machine learning concepts

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This course requires a basic understanding of machine learning concepts. Reviewing basic machine learning concepts will help you be successful.

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Read through a machine learning tutorial
Complete some machine learning coding exercises

Review 'Machine Learning with Python' by Sebastian Raschka and Vahid Mirjalili

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Machine learning workflow involves many intricate steps to deploy your model to production. This book will help you ensure that the model you deploy is meaningful and robust.

View Machine Learning with PyTorch and Scikit-Learn:... on Amazon

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Read Chapter 1: Introduction to Machine Learning
Read Chapter 2: Supervised Learning
Read Chapter 3: Unsupervised Learning

Six other activities

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Show all nine activities

Create a study guide for the course

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This will help you stay organized and on track with the course material.

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Gather your course materials
Identify the key concepts in each lesson
Create a summary of each lesson

Practice logging metrics using autologging

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Autologging will help you track all relevant parameters, metrics, and artifacts without writing explicit logging code.

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Create a new MLflow experiment
Log a metric using the autologging API
View the logged metric in the MLflow UI

Follow the Databricks tutorial on using MLflow to transition models through lifecycle stages

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This tutorial will help you understand how to use MLflow to transition models through lifecycle stages.

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Read the Databricks tutorial on using MLflow to transition models through lifecycle stages
Follow the steps in the tutorial to transition a model through lifecycle stages

Practice using MLflow to run training using a project hosted on Github

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This will help you understand how to use MLflow to run training using a project hosted on Github.

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Create a new MLflow project
Host the project on Github
Run training using the project hosted on Github

Create a blog post on using MLflow to serve models

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This will help you solidify your understanding of how to use MLflow to serve models.

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Write an introduction to MLflow and model serving
Explain how to use MLflow to serve a model
Provide an example of how to use MLflow to serve a model

Create a presentation on using MLflow to manage the end-to-end lifecycle of machine learning models

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This will help you solidify your understanding of how to use MLflow to manage the end-to-end lifecycle of machine learning models.

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Create an introduction to MLflow and model lifecycle management
Explain how to use MLflow to manage the end-to-end lifecycle of machine learning models
Provide an example of how to use MLflow to manage the end-to-end lifecycle of machine learning models

Career center

Learners who complete Managing Models Using MLflow on Databricks will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers build and deploy machine learning models. They work with data scientists to understand the business problem, and they develop and implement the technical solutions. This course may be useful for Machine Learning Engineers who want to learn how to manage and track machine learning models using MLflow on Databricks.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They develop machine learning models to solve problems such as fraud detection, customer churn, and product recommendation. This course may be useful for Data Scientists who want to learn how to manage and track machine learning models using MLflow on Databricks.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, and maintain software systems. They may work on a variety of projects, including machine learning applications. This course may be useful for Software Engineers who want to learn how to manage and track machine learning models using MLflow on Databricks.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations to businesses on how to improve their operations. This course may be useful for Data Analysts who want to learn how to manage and track machine learning models using MLflow on Databricks.

See salaries and explore the career path for Data Analyst

Data Engineer

Data Engineers design and build the infrastructure that processes and stores data. They work with data scientists to ensure that data is clean and usable for analysis, and they may also develop machine learning models. This course may be useful for Data Engineers who want to learn how to manage and track machine learning models using MLflow on Databricks.

See salaries and explore the career path for Data Engineer

Business Analyst

Business Analysts use data to understand the needs of businesses and develop solutions to their problems. They may work with data scientists and machine learning engineers to develop and deploy machine learning models. This course may be useful for Business Analysts who want to learn how to manage and track machine learning models using MLflow on Databricks.

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Product Manager

Product Managers are responsible for the development and delivery of products. They work with engineers, designers, and marketers to bring products to market. This course may be useful for Product Managers who want to learn how to manage and track machine learning models used in their products.

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Sales Manager

Sales Managers lead and motivate sales teams to achieve their goals. They may use data to track the performance of their teams and identify opportunities for improvement. This course may be useful for Sales Managers who want to learn how to manage and track machine learning models used in their sales processes.

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Marketing Manager

Marketing Managers develop and execute marketing campaigns to promote products and services. They may use data to track the effectiveness of their campaigns and identify opportunities for improvement. This course may be useful for Marketing Managers who want to learn how to manage and track machine learning models used in their marketing campaigns.

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Operations Manager

Operations Managers oversee the day-to-day operations of a business. They may use data to track the performance of their operations and identify opportunities for improvement. This course may be useful for Operations Managers who want to learn how to manage and track machine learning models used in their operations.

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Compliance Analyst

Compliance Analysts ensure that businesses comply with laws and regulations. They may use data to identify and assess compliance risks. This course may be useful for Compliance Analysts who want to learn how to manage and track machine learning models used in their compliance assessments.

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Financial Analyst

Financial Analysts use data to evaluate the financial performance of companies and make investment recommendations. This course may be useful for Financial Analysts who want to learn how to manage and track machine learning models used in their analysis.

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Risk Analyst

Risk Analysts identify and assess risks to businesses. They may use data to develop risk models and make recommendations on how to mitigate risks. This course may be useful for Risk Analysts who want to learn how to manage and track machine learning models used in their risk assessments.

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Consultant

Consultants provide advice and guidance to businesses on a variety of topics. They may use data to support their recommendations. This course may be useful for Consultants who want to learn how to manage and track machine learning models used in their consulting work.

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Auditor

Auditors examine financial statements and records to ensure accuracy and compliance with laws and regulations. They may use data to identify and assess risks. This course may be useful for Auditors who want to learn how to manage and track machine learning models used in their audits.

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