MLOps Fundamentals - Learn MLOps Concepts with Azure demo
The course begins by defining MLOps and explaining the traditional machine learning lifecycle.
You’ll understand the roles and responsibilities of different team members involved in ML projects.
It then dives into the challenges faced in existing ML projects, such as the lack of automation and standardization, which often leads to models not being productionized.
To address these issues, the course introduces MLOps as a solution.
You’ll learn about the standards and principles of MLOps, its implementation process, and the benefits it offers to various stakeholders.
Importantly, it covers the different maturity levels of MLOps adoption - Level 0, Level 1, and Level 2 - helping you understand the importance of progressing through these levels.
The course also provides an overview of the tools and platforms required for building an MLOps platform.
It compares different MLOps platforms, guiding you on how to choose the right one for your needs.
The highlight of the course is a hands-on demo where you’ll build and run a CI/CD MLOps pipeline using Azure DevOps and Azure Machine Learning.
You’ll start by understanding the project requirements and getting a crash course on Azure Machine Learning Studio.
Next, you’ll work on a data scientist’s experiment, converting it into an MLOps pipeline.
This involves orchestrating the ML codes in Azure, including model training, evaluation, registration, and scoring.
Finally, you’ll build the CI/CD MLOps pipeline itself, covering continuous integration and deployment scripts.
Throughout the course, you’ll encounter key MLOps concepts like DevOps, Continuous Integration, Azure Machine Learning, automation, and more.
The course is accessible, using simple language and avoiding unnecessary jargon.
Azure Machine Learning & MLOps : Beginner to Advance
You’ll kickstart your journey with an introduction to the Azure Machine Learning Service and Azure DevOps, setting up the necessary configurations for seamless MLOps workflows.
Hands-on lectures guide you through creating and deploying infrastructure as code pipelines, enabling continuous integration (CI) and continuous deployment (CD) for machine learning models.
The course dives deep into the Azure ML SDK V2, an accelerator designed to streamline the machine learning lifecycle across multiple workspaces.
You’ll also explore highly demanded capabilities like Responsible AI, which empowers you to assess model fairness, explainability, and potential biases.
Imagine leveraging Azure Machine Learning Pipelines to orchestrate and schedule every step of your ML process with ease.
Or harnessing the power of Feature Store (Feast) to build, manage, and share features seamlessly.
The syllabus covers advanced topics like distributed processing with Ray and Dask, AutoML for Computer Vision and Natural Language Processing, and even training thousands of models in parallel.
From monitoring data drift to integrating Azure Synapse and Databricks, you’ll gain a comprehensive understanding of the Azure ML ecosystem.
Deploy multi-model endpoints, implement blue-green deployments, and even consume your models in Power BI or low-code Power Apps.
The course keeps you future-ready by exploring cutting-edge technologies like ONNX for platform-agnostic model deployment and the integration of Azure with OpenAI’s generative AI capabilities.
Machine Learning Deep Learning model deployment
You’ll start by understanding what models are and how to build them using Python libraries like NumPy, Pandas, and Matplotlib.
The course then dives into creating classification models, saving them, and deploying locally using deserialized Pickle objects.
You’ll also learn to use models in Google Colab and create REST APIs with Flask to serve the models over the internet.
But that’s not just for traditional machine learning models.
The syllabus covers deploying deep learning models built with PyTorch and TensorFlow too!
You’ll learn to host the APIs on Google Cloud, use serverless functions, and even convert between PyTorch and TensorFlow formats using ONNX.
Natural Language Processing is a key focus area.
You’ll build text classifiers and TF-IDF models for sentiment analysis on Twitter data.
The course covers creating Twitter developer accounts and deploying the NLP models as REST APIs and serverless functions.
Going beyond just APIs, you’ll deploy models to web browsers using TensorFlow.js and JavaScript!
The course even teaches you to derive model formulas mathematically and store models in databases like PostgreSQL.
The syllabus covers cutting-edge topics like MLOps and using MLflow for experiment tracking, model management and deployment.
You’ll get hands-on experience with MLflow on Windows, Mac, Colab and Databricks.
Finally, you’ll explore the world of generative AI - using OpenAI’s GPT models for text generation, image creation, text-to-speech and even building a chatbot!
Complete MLOps Bootcamp | From Zero to Hero in Python 2022
You’ll begin by understanding the challenges and evolution of machine learning, exploring the fundamentals of MLOps, DevOps, and DataOps.
This lays a solid foundation for the rest of the course.
Next, you’ll dive into the core components of MLOps, including its toolbox, stages, and the problems it solves.
The course then guides you through setting up the necessary tools and libraries, such as Jupyter Notebook, Docker, and Ubuntu.
This hands-on approach ensures you’re well-equipped for the practical aspects of MLOps.
One of the course’s strengths is its focus on productivization and structuring of machine learning projects.
You’ll learn how to use tools like Cookiecutter, Poetry, Makefile, Hydra, and Git to manage project structure, dependencies, automated tasks, configuration files, and code quality.
The course covers the three main phases of MLOps: solution design, automating the model cycle, and model serving.
In the first phase, you’ll explore Volere design and implementation.
The second phase delves into AutoML with Pycaret, model versioning with MLFlow, dataset versioning with DVC, and integrating these tools with DagsHub for a centralized code repository.
Model interpretability is also covered, with a focus on SHAP for interpreting Scikit-learn and Pycaret models.
You’ll then learn how to put models into production and serve them through APIs using FastAPI and web applications with Gradio, Streamlit, and Flask.
The course introduces you to Docker and containers for isolating applications, as well as BentoML for automated development of machine learning services.
You’ll also explore deploying models to cloud platforms like Azure using Azure Container and SDKs.
Continuous integration and delivery (CI/CD) are essential aspects of MLOps, and the course covers GitHub Actions and Continuous Machine Learning (CML) for implementing CI/CD pipelines.
Additionally, you’ll learn about model and service monitoring with Evidently AI, focusing on data drift, concept drift, and model performance.
The course culminates with an end-to-end MLOps project, where you’ll apply the knowledge gained throughout the course to develop, validate, version, deploy, and monitor a machine learning model and its associated services.
Practical MLOps: AWS Mastery for Data Scientists & DevOps
This comprehensive syllabus covers a wide range of topics, from the fundamentals of MLOps and DevOps to hands-on experience with AWS services like CodeCommit, CodeBuild, CodeDeploy, and CodePipeline.
The course starts with an introduction to MLOps, explaining its importance and how it differs from traditional DevOps practices.
You’ll learn about the MLOps fundamentals, including the challenges of integrating Machine Learning (ML) into DevOps workflows.
Additionally, you’ll gain insights into why DevOps alone is not suitable for ML projects.
One of the key aspects of the course is its focus on AWS services.
You’ll learn about the benefits of AWS and its technical stack for MLOps and ML.
The syllabus includes sections on setting up an AWS account, configuring IAM policies, and working with S3 buckets and EC2 instances.
The course provides a solid foundation in Linux, which is essential for DevOps and data scientists.
You’ll learn about Linux features, bash scripting, and various Linux commands.
Source code management is covered through a comprehensive section on Git and AWS CodeCommit, where you’ll learn about version control, branching, merging, and resolving conflicts.
The syllabus also includes a crash course on YAML, which is widely used in DevOps tools and configurations.
You’ll dive into AWS CodeBuild, learning how to create and configure CodeBuild projects, work with buildspec.yml files, and handle environment variables and artifacts.
AWS CodeDeploy and CodePipeline are covered in detail, allowing you to understand and implement continuous integration and continuous deployment (CI/CD) pipelines.
The course also introduces you to Docker containers and Amazon Elastic Container Registry (ECR), which are essential for packaging and deploying ML models.
Practical MLOps with Amazon SageMaker is a significant part of the course.
You’ll learn about feature engineering, data wrangling, and creating a feature store.
Additionally, you’ll gain hands-on experience in training, tuning, and deploying ML models using SageMaker.
The syllabus also covers creating custom models with popular frameworks like TensorFlow, PyTorch, and scikit-learn.
Infrastructure as Code (IaC) is introduced through AWS CloudFormation, where you’ll learn to define and manage infrastructure resources using CloudFormation templates.
The course delves into advanced topics like AWS Step Functions for serverless workflows and SageMaker Pipelines for end-to-end MLOps pipelines.
You’ll also find expert guidance on packaging ML models, using MLflow for MLOps, and an introduction to Kubernetes.
MLflow in Action - Master the art of MLOps using MLflow tool
You’ll start by understanding the fundamentals of MLOps and the challenges it addresses in traditional machine learning projects.
The course then dives deep into MLflow, an open-source MLOps tool.
You’ll learn about its four key components: Tracking, Models, Projects, and Registry.
The Tracking component allows you to log parameters, metrics, and artifacts during model training.
With the Models component, you’ll package machine learning models in different formats for deployment.
The Projects component helps you package your code as a reusable project, while the Registry component lets you manage and version your models centrally.
Throughout the course, you’ll implement these components using hands-on examples with popular libraries like scikit-learn.
The course also covers advanced topics like handling custom models, model evaluation techniques, and integrating MLflow with AWS services like SageMaker, CodeCommit, and S3.
You’ll even build an end-to-end project, training models on AWS SageMaker and deploying them for inference.
Katonic MLOps Certification Course
The course starts with an introduction to MLOps, explaining why it’s crucial and covering the lifecycle of an ML system.
You’ll learn about the activities involved in productionizing a model and gain an understanding of MLOps maturity levels.
Next, you’ll dive into Kubernetes and Docker, two essential technologies for MLOps.
The course covers the basics of containers, virtual machines, and why Docker is so important.
You’ll also learn about Kubernetes, including pods, deployments, and working with namespaces.
Moving on, the course introduces the Katonic MLOps platform, discussing the requirements for an MLOps stack and the landscape of available solutions.
You’ll get an overview of the platform itself and learn about feature engineering and the AI model lifecycle.
The real highlight is the end-to-end use case demo, where you’ll get hands-on experience with the entire MLOps process.
You’ll start by creating a workspace and fetching data, then work with notebooks and experiments.
The demo covers registering a model, building an ML pipeline, deploying a model, and even creating an app using Streamlit.
You’ll also learn how to build an inference pipeline, schedule pipeline runs, monitor your model, and retrain it when necessary.
This comprehensive demo will give you a solid understanding of the entire MLOps workflow.
End-to-End Machine Learning: From Idea to Implementation
The course begins with introductions to essential tools like Git, GitHub, Docker, and Google Cloud Platform.
You’ll learn how to version control your code, containerize applications, and leverage cloud resources effectively.
These foundational skills are crucial for collaborative development and scalable deployments.
Next, you’ll dive into data versioning with DVC, a powerful tool for tracking and managing datasets.
This section teaches you how to version your data, create pipelines, and experiment with different configurations seamlessly.
Hydra, a configuration management library, is also covered in-depth, enabling you to create structured and hierarchical configurations for your projects.
The course then explores MLflow, a popular open-source platform for managing the end-to-end machine learning lifecycle.
You’ll learn how to track experiments, log metrics, version models, and deploy MLflow on Google Cloud Platform for production use cases.
Distributed computing is a key focus, with sections dedicated to Dask for parallel processing and launching distributed training jobs on Google Cloud.
You’ll gain hands-on experience in scaling up computations and leveraging cloud resources for efficient model training.
Moving on, you’ll learn about FastAPI and Streamlit, two powerful frameworks for building web applications and user interfaces.
These skills will enable you to create interactive dashboards and deploy your machine learning models as web services.
Throughout the course, you’ll work on a real-world project, covering every stage from data processing and tokenizer training to distributed model training, evaluation, and deployment as a web application.
This project-based approach ensures you gain practical experience and a deep understanding of the entire machine learning pipeline.
The course also emphasizes best practices for project setup, code formatting, linting, and type checking, ensuring you develop high-quality, maintainable code.
Mastering MLOps: Complete course for ML Operations
You’ll start by understanding the challenges and evolution of machine learning, as well as the fundamentals of MLOps, DevOps, and DataOps.
This lays the groundwork for diving into the core MLOps components like model versioning, data versioning, and automating the ML model cycle.
The course takes a hands-on approach, guiding you through installing tools like Jupyter Notebook, Docker, and Ubuntu.
You’ll then learn to design ML solutions, build models from scratch using libraries like Pycaret, and work with advanced models like XGBoost, CatBoost, and LightGBM.
A major focus is on model versioning and registration using MLflow.
You’ll version datasets with DVC and create code repositories integrating DagsHub, DVC, Git, and MLflow.
Automated registration of models with Pycaret and DagsHub is also covered.
For model interpretability, you’ll use SHAP to interpret Scikit-learn and Pycaret models.
The course dives deep into deploying models via APIs using FastAPI, web applications with Gradio and Streamlit, and containerization with Docker.
You’ll learn advanced deployment techniques like BentoML for automated ML service development, deploying to Azure Cloud, and using Heroku.
Continuous integration and delivery (CI/CD) with GitHub Actions and CML is another key topic.
Monitoring ML models and services is crucial, so you’ll work with Evidently AI to detect data drift, concept drift and track model performance.
The course culminates in an end-to-end MLOps project tying everything together - from model development to CI/CD deployment.
MLOps. Machine Learning deployment: AWS, GCP & Apple in<7hrs
This course goes from the fundamentals of MLOps and DevOps to hands-on practice with AWS, GCP, and Apple platforms.
It starts with an introduction to MLOps, explaining its importance and applicable career tracks.
You’ll gain insights into the MLOps market, including salaries, job requirements, and industry trends.
This section provides a solid foundation for understanding the field.
The theory stack chapter dives into the core concepts of DevOps, Continuous Integration (CI), Continuous Delivery (CD), Infrastructure as Code (IaC), and Microservices.
You’ll learn how to set up CI & CD pipelines yourself, a crucial skill for MLOps professionals.
One of the standout features of this course is the practical application of MLOps best practices.
You’ll learn how to package ML models into Docker containers, run AutoML locally, train ML models for Apple devices, and monitor and log ML experiments using the MLFlow framework.
These hands-on exercises will give you valuable experience in deploying and managing ML solutions.
The course dedicates separate chapters to MLOps in AWS and GCP, where you’ll set up and manage MLOps pipelines in AWS SageMaker and operate Model Registry & Endpoints in GCP VertexAI.
This practical experience with industry-leading cloud platforms is invaluable for aspiring MLOps professionals.
Additionally, the course covers important topics like data drifts, a common issue in MLOps, and how to streamline ML model development using MLFlow.
You’ll learn about experiment tracking, using DagShub, and deploying an ML-powered web app to AWS Cloud.
The course concludes with industry hacks to boost your career and studying efficiency, including tips on staying up-to-date, exploring cool projects, and showcasing your portfolio effectively.