10 Best MLOps Tools You Need to Know
MLOps (Machine Learning Operations), as we've mentioned in a previous article, is an approach to managing machine learning projects at a large scale.
It's a set of methods and tools for greater automation that enhance collaboration within a business's data team and facilitate the management of the entire lifecycle of machine learning models.
In today's article, we'll take a look at the following top 10 MLOps tools that are worth knowing:
- Comet ML
- Weights & Biases
- Data Version Control
- Fiddler AI
- AWS SageMaker
Let’s start with the first tool on our list.
Tool #1: MLFlow
MLflow is an open-source tool that helps manage various aspects of the machine learning project lifecycle and It's generally used for experiment tracking.
Through its use a data scientist or a machine learning engineer can manage machine learning experiments and model metadata using Python, R, Java and REST APIs.
MLflow has four primary components:
- MLflow Tracking for storing and accessing code and data
- MLflow Projects
- MLflow Models for developing and managing machine learning models in different environments
- MLflow Model Registry, a central model repository that provides versioning, comments etc.
Tool #2: Comet ML
Comet ML is a platform for monitoring, comparing, and optimizing machine learning models and experiments.
It can be easily used with a wide range of machine learning libraries such as Scikit-learn, PyTorch, and TensorFlow.
Comet ML also offers visualization capabilities for samples from images, audio, and tabular data.
Tool #3: Weights & Biases
Weights & Biases is a machine learning platform for experiment tracking, data and model versioning, dataset iteration, performance evaluation, and managing machine learning workflows.
It features a user-friendly central control panel for machine learning experiments and can be easily integrated with other machine learning libraries like Keras and PyTorch.
Tool #4: Metaflow
Metaflow is a workflow management tool for data science and machine learning projects.
With Metaflow, workflow design is optimized, and automated experiments and machine learning data output is achieved.
It works with multiple clouds and Python machine learning packages such as Scikit-learn and Tensorflow.
Metaflow was originally developed at Netflix to meet the needs of data scientists working on demanding and big data.
Today it is used by hundreds of companies in various industries, powering projects in natural language processing (NLP), data science and statistics.
Tool #5: Kedro
Kedro is a popular workflow orchestration tool based on the Python programming language.
Using Kedro, various parameters and dependencies can be easily configured, experiments can be logged and monitored, and reusable code can be created.
Tool #6: Data Version Control
Data Version Control is an open-source tool designed for machine learning projects.
It integrates seamlessly with Git to provide version control for code, data, models, metadata, and version management.
It also supports experiment tracking, reproducibility, and continuous integration and deployment (CI/CD) for machine learning.
Tool #7: Fiddler AI
Fiddler AI is a machine learning model monitoring tool with a user-friendly interface.
It allows the user to explain and detect errors in predictions, analyze how the entire dataset works, as well as develop machine learning models at scale and monitor their performance.
Among the key features of Fiddler AI are performance monitoring, outlier detection, and data integrity.
Tool #8: Amazon Web Services SageMaker
Amazon Web Services SageMaker is an integrated MLOps solution for the AWS cloud platform.
It offers a collaborative data science environment, model development, versioning, and experiment tracking.
The user can train and accelerate model development and perform various monitoring and versioning experiments, among others.
Some key features of AWS Sagemaker are the following:
- A collaborative environment for data science teams
- Development and management of models in production
- Model version tracking and maintenance
- CI/CD for automatic integration and deployment
Tool #9: Kubeflow
Kubeflow is a fundamental MLOps tool, as it simplifies the development of machine learning models on Kubernetes, making it easy and scalable.
It can be used by data scientists for data preparation, model training and optimization, local or cloud-based machine learning workflow development, and internal installation.
Among its key features are:
- A central control panel with an interactive user workspace
- Native support for JupyterLab, RStudio, and Visual Studio Code
- Hyperparameter tuning
Tool #10: DagsHub
DagsHub is a platform created for the machine learning community to track and release data, models, experiments, and code.
It enables data teams to create, modify, and share machine learning projects.
Its key features include:
- Git and DVC repositories for machine learning projects
- CI/CD pipeline for training and deploying models
- Commenting on files, code lines, or datasets
- Data merging
In a Nutshell
In a nutshell, each MLOps tool has its own core features and functionalities, tailored to the specific needs of professionals and businesses.
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