Top 15 MLOps Tools You Need to Know in 2024

MLOps tools are essential for engineers to manage the lifecycle of machine learning models. They play a vital role in development and beyond.

The lesser-known MLOps job role is gaining traction among tech professionals because the rapid growth of machine learning applications requires specialized skills to streamline model deployment. And, hence this necessitates the specialized MLOps tools that can ensure ML models perform reliably in a production environment.

This article looks into the features of the top 15 MLOps tools. The listed tools cater to deployment frameworks, enterprise and open-source cloud platforms, and machine learning lifecycle management solutions.

Professionals planning to learn MLOps in general or get an in-depth understanding of various MLOps tools can pursue the MLOps engineering course. The course provides an in-depth understanding of deploying large-scale ML models with the use of a range of MLOps tools.

What are MLOps Tools?

MLOps engineers use specialized tools to streamline the deployment of the machine learning models. These tools are also responsible for training and monitoring the ML models to create more efficient and effective models.

With the help of these tools, engineers manage the core parts of machine learning lifecycle. These tools are also compatible with various machine learning libraries as well, thus bringing a seamless workflow across platforms.

MLOps tools can be categorized based on their functionality and the support they bring during the ML models’ lifecycle process.

1. Data Management: MLOps tools that facilitate the collection and integration of data from various sources. Also, the tools that assist in cleaning and preparing data for analysis.
2. Model training and evaluation: Tools that help in model training with features like hyperparameter tuning.
3. Data Versioning: Just like code versioning in software development, data versioning is also important in MLOps. It allows teams to track changes to code, data, and models.
4. Model Deployment: Tools that handle deployment of models to production environments and manage real-time interface. Example, TensorFlow Serving, TorchServe.
5. Monitoring and Maintenance: Tools that continuously monitor model performance in production to detect any issues.
6. Orchestration: Here the tools help in automating the workflows, optimizing resource management, and manage dependencies.

 Categorizing MLOps tools in this manner helps organizations identify the right tools for each stage of their machine learning workflows.

15 Top MLOps Tools of 2024

Engineers use MLOps tools to manage data preparation, testing, and deployment of trained models for accuracy and stability in a market-ready product.

So, let’s review these top specialized MLOps tools, including, frameworks, enterprise and open-source cloud platforms, and machine learning lifecycle management solutions.

1. MLFlow‍

Metadata management with MLOps tools is possible with MLflow. You can manage core parts of the machine learning lifecycle using this open-source tool.

Features: 

• Manages machine learning experiments and model metadata via CLI, Python, R, Java, and REST API.
• Provides ML Tracking for code, data, configuration, and results storage.
• Packages data science source for reproducibility.
• Manages ML Models for deployment and management.
• Provides versioning, stage transitions, annotations, and model management at one central model store.

Pricing:

Depends on the platform on which MLFLow is deployed, access duration and load patterns. For instance, deploying MLFlow on AWS ECS for 5 days a week, 8 hours a day can cost you $18.74 per month (as of May 15, 2024)

2. Google Cloud ML Engine‍

Google Cloud ML Engine is a managed service that enables easy creation, training, and implementation of machine learning models.

Features:

• Uniform interface for training, serving, and monitoring ML models
• Hyperparameter tuning for prediction accuracy
• Bigquery and cloud storage for dataset preparation and storage
• Built-in feature for data labeling
• Auto ML feature for task completion without code
• Free notebook running via Google Colab

Pricing:

The Google Cloud ML Engine is a pay as you go service with its pricing depending on various factors like number of virtual machines deployed for training, their configuration and storage costs involved with it.

3. LangChain‍

LangChain is an open-source framework that offers an adaptable and strong framework for creating language model-powered applications. ‍

Features:

• Uses Python and JavaScript libraries for context-aware reasoning applications.
• Templates are pre-built reference architectures for various tasks.
• LangServe is the REST API for deploying LangChain chains.
• LangSmith is the platform where engineers can monitor, debug, test, and evaluate LLM framework-built chains

Pricing:
Developer: Single user license; 5,000 free traces per month, 0.005 per trace thereafter
Plus: $39/user per month; 10 seats per organization; 10,000 free traces per month, 0.005 per trace thereafter
Startup: Available on request

4. TensorFlow Extended (TFX)‍

Google’s TensorFlow Extended is a production-scale machine learning platform that offers shared libraries and frameworks for incorporating machine learning into workflows.

Features: 

• Coordinates with Apache, Beam, and KubeFlow machine learning workflows
• Supports data analysis and assessment for machine learning
• Provides measurements for the spread of big data
• Aids in the evaluation of TensorFlow models
• Offers information so that TF can be used to train machine learning models
• Elegant design to enhance TFX operations

5. Comet ML‍

Comet ML is a platform that allows machine learning models and experiments to be tracked, compared, explained, and optimized.

Features:

• Compatible with various machine learning libraries. (like Pytorch, TensorFlow, etc.)
• Ideal for individuals, teams, and enterprises, and academics
• Facilitates easy experiment visualization and comparison
• Enables sample visualization from various data types|

Pricing:
Personal Use: Free
Starter: $50per user/month
Enterprise: Available on request on the Comet’s website

6. Metaflow

Metaflow is a useful workflow management tool for data science and machine learning projects. It enables data scientists to focus on building models instead of MLOps engineering.

Features: 

• Allows workflow design, scale deployment, and model production
• Automatically tracks and versions machine learning experiments and data
• Allows visualization of results in the notebook
• Is compatible with multiple clouds and Python packages
• API’s are available for R language

Pricing:
Community: Free
Starter: $1199/month
Enterprise: Available on request

7. Amazon SageMaker

Amazon SageMaker offers machine learning operations (MLOps) solutions for consumers looking to automate and standardize activities across the machine learning lifecycle.

Features:

• ML model testing, deployment, governance, and training make data scientists and ML engineers more productive.
• Training takes only minutes instead of hours because of optimized infrastructure.
• Shortens production time by integrating CI/CD pipelines with machine learning procedures.
• Compatible with popular programming languages, toolkits, and ML frameworks.
• Has security features for monitoring, authorization, authentication, data protection, infrastructure security, and policy administration.

Pricing:
Varies on the basis of instances used (Amazon SageMaker P5, Amazon SageMaker P4d, Amazon SageMaker Code Editor, etc.) as well as location of use in the USA

8. Databricks MLflow

The open-source MLflow platform from Databricks is the foundation around which Managed MLflow is constructed. It supports customers in managing the full ML lifecycle with enterprise-level security, scalability, and dependability.

Features: 

• Tracks using Python, REST, R API, and Java API for automatic parameter logs, metrics, code versions, and artifacts.
• Allows recording of stage transitions and approval of changes in CI/CD pipelines.
• Provides access control and search queries for experiment creation, security, organization, and visualization.
• Enables quick deployment via Apache Spark UDF and other production environments.

Pricing:
Pay as you go pricing with 14-day free trial.
Standard, Premium, or Enterprise are three pricing categories
Contact Databricks for more information

9. Azure Machine Learning

Azure Machine Learning Services is a cloud-based platform for machine learning and data science, offering features that allow users to run workloads remotely.

Features:

• Remote machine learning systems with integrated mechanisms to manage, secure and ensure regulatory requirements
• Rapid creation of models for tasks such as classification, regression, forecasting, natural language processing, and computer vision
• PySpark for interactive data preparation with Azure Synapse Analytics.
• Microsoft Power BI and different Azure services boost enterprises’ productivity

Pricing:
Standard Version: $9.99 per ML studio workspace per month; $1 per studio experimentation hour.
Free Version: No charges.

10. Nuclio

Nuclio is a serverless framework that does not require server maintenance and is ideal for workloads that are heavy on data, input/output, and computation.

Features: 

• Integration with popular data science tools like Jupyter and Kubeflow
• Supports diverse data and streaming sources
• Made for enterprise use
• Executable over CPUs and GPUs
• Requires minimal CPU/GPU and I/O resources for real-time processing
• Portability across various devices and cloud platforms
• Provides stateful functions with data-path acceleration

Pricing:
Free version: No cost
Standard version: $5/per user
Premium version: $10 per user

11. Data Version Control (DVC)

DVC is a Python-based open-source tool for data science and machine learning. It aims to make machine learning models shareable and reproducible and handle large files, models, metrics, data sets, and code.

Features: 

• Controls machine learning models, data sets, and intermediate files
• Stores file contents on various storage platforms
• Outlines collaboration rules and processes
• Connects ML steps into a DAG for end-to-end pipeline execution‍

Pricing: Free (Open source)

12. Kubeflow

As a component of the Cloud Native Computing Foundation (CNCF), which also includes Prometheus and Kubernetes, Kubeflow is a cloud-native platform for machine learning operations.

Features:

• Leverages cloud providers like Google Cloud or AWS for MLOps stack creation
• Provides comprehensive solutions for deploying and managing end-to-end ML workflows
• Supports PyTorch, Apache MXNet, Chainer, MPI, XGBoost
• Integrates with Istio, Ambassador, and Nuclio for managing data science pipelines

Pricing:
$2.06/hour per running Kubeflow deployment
$0.20/hour per stopped Kubeflow deployment.
7 day free trail

13. H2O Driverless AI

H2O Driverless AI is a cloud-based machine learning platform that enpowers data scientists to create, train, and apply machine learning models with a few clicks.

Features: 

• Extracts information from digital photos for predictive characteristics
• Accesses data from Hadoop HDFS, Amazon S3
• Automatically selects data plots based on relevant statistics
• Supports programming languages like Python, R, and Scala
• Develops visualizations and provides statistically significant data plots

Pricing:
Opensource

14. TruEra

With automated testing, explainability, and root cause analysis, the intelligent platform TruEra improves model quality and performance while guaranteeing smooth integration into machine learning technology stacks.

Features: 

• Model testing and debugging for improved model quality
• Automated and systematic testing for performance, fairness, and stability
• Understanding model version evolution for faster development
• Identifying specific features contributing to model bias
• Easy integration into existing infrastructure and workflow

Pricing:

15. Hugging Face Inference Endpoints

Hugging Face, an all-in-one machine learning platform, offers Hugging Face Inference Endpoints, a cloud-based service that lets customers host, train, and share models, datasets, and demos. These endpoints are intended to assist customers in deploying their machine learning models that have been trained for inference, eliminating the need to configure and maintain the necessary infrastructure.

Features: 

• Provides quick deployment
• Completely managed and autoscaled
• Belongs to the Hugging Face ecosystem
• Provides enterprise-level security

Pricing:
$0.06 per CPU core/hr,
$0.6 per GPU/hr

To Conclude:
According to the Allied Market Research report, the global MLOps market is expected to increase at a CAGR of 39.3% from 2023 to 2032. As newer versions of these tools continue to emerge, they will further fuel the growth of the MLOps market in years to come.

FAQs: Top 15 MLOps Tools

What is the difference between MLOps and DevOps?
Software development, deployment, and monitoring procedures are improved by MLOps and DevOps methods. DevOps facilitates the development and operations teams, which encourages cooperation and quicker release cycles. MLOps is developed for machine learning projects to automate the lifecycle and ensure that models are deployed, tracked, and retrained methodically.

How does MLOps solve the problem of inefficient workflows?
MLOps offers a platform for effectively and efficiently managing the machine learning lifecycle for inefficient workflows. MLOps matches technological skills with business knowledge to produce an iterative, more structured workflow.

What programming languages are used in MLOps?
The development and deployment of machine learning models call for the knowledge of programming languages, including Python, R, and Java.

What qualifications are required for MLOps?
For a position of MLOps engineer, employers prefer candidates with a degree in computer science, software engineering, data science, computational statistics or mathematics. He should have experience with a few programming languages like Python, SQL and R as well.

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