The AI/ML path is fascinating and complicated from exploration to implementation. Strong, scalable, and practical deployment pipelines are more critical than ever as businesses want to capitalize on data-driven insights. MLOps tools enable collaboration across data scientists, machine learning engineers, and DevOps teams to bridge the gap between research and operationalization.
From collecting data to model deployment and monitoring, MLOps encompasses the procedures and resources that aid in managing and optimizing the whole machine learning lifecycle. Organizations need specific tools to manage the operational elements of machine learning (ML) models as they get more complicated and are deployed more often. This ensures that the models function as intended and provide value over time.
Keep exploring and reading to learn about the best tools for MLOps available in 2024.
Table of Contents
What is MLOps?
Machine learning operations, or MLOps for short, is a collection of procedures that integrates the ideas of data engineering, machine learning, and DevOps. However, from data collection and developing models to deployment, monitoring, and governance, MLOps aims to automate and optimize the complete machine learning lifecycle.
Additionally, The fundamental goal of MLOps is to set up and maintain machine learning algorithms in production environments reliably and effectively. MLOps promotes improved cooperation and guarantees that all teams are operating within the same framework by dismantling silos between data analysts/scientists, ML engineers, and IT operations teams.
Also Read: Data Science vs Data Analytics: Which Path Should You Choose?
What Makes MLOps Necessary?
In general, your company must start with data preparation before starting the machine learning lifecycle. You retrieve diverse data from several sources and carry out tasks, including feature engineering, aggregation, and duplication cleansing.
The ML model is then trained and validated using the data. The trained and verified model may then be made available to other apps via APIs as a prediction service using MLOps tools.
Experimenting with many models until the optimal model version is prepared for deployment is frequently necessary while conducting exploratory data analysis. It results in regular data version control and model version deployments.
Before your apps may include or use the model in their code, you must manage the ML training process and log your experiments.
MLOps is essential for managing the simultaneous and methodical delivery of new ML models together with modifications to data and application code. The ML assets are handled similarly to other software resources in a continuous integration and delivery (CI/CD) environment by an ideal MLOps deployment. As part of an integrated release process, you deploy ML models within the services and apps that utilize them as well as those that use them.
What are MLOps Tools?
Tools for MLOps are software applications that facilitate collaboration, workflow optimization, and integration between data scientists, machine learning engineers, and IT management teams. In the end, they help achieve MLOps’ main objective, which is to automate the creation, deployment, and monitoring of models by combining data engineering, DevOps, and machine learning.
10 Best MLOps Tools You Need To Know in 2024
In the world of machine learning, selecting the appropriate MLOps solution is essential as it has a significant influence on the success and productivity of your team. Here are some of the best tools for MLOps on the market right now:
AWS SageMaker
Web Services by Amazon SageMaker is a complete MLOps solution. Model creation can be accelerated and trained, experiments can be tracked and versioned. Furthermore, ML artifacts can be cataloged, Write-Audit-Publish ML processes can be integrated, and models can be easily deployed, served, and monitored in production.
Data scientists can work together and exchange code with ease thanks to Amazon SageMaker’s single interface for data pretreatment, model training, and experimentation. SageMaker Studio is an effective platform for creating and implementing machine learning solutions at scale since it has integrated algorithms, automatic model tweaking, and a smooth interaction with AWS services.
Key Features:
- A platform enabling data science teams to collaborate
- Process automation for machine learning training
- Model deployment and management in production
- Monitoring and controlling model versions
- Write-Audit-Publish streamlines deployment and integration.
- Possibilities for cost and performance optimization
LakeFS
An open-source, data-scalable version control system called lakeFS offers an object storage version control interface akin to Git. In essence, it enables users to handle their data lakes similarly to how they handle their code. A highly scalable solution for big data lake management, lakeFS is crucial for enhancing data quality.
It enables you to version and manage data lake changes at scale by occupying the space between the data lake and cloud entity storage. Within the data lake context, LakeFS makes data governance, interaction, and reproducibility easier. It is, therefore, one of the best tools in our MLOps tools list.
Although lakeFS is an open-source solution that is free to use, bigger teams could find the lakeFS Cloud version, which has additional features and SLAs, helpful.
Key Features:
- Git operations using any storage provider, such as branching, performing, and merging
- Zero-copy branching speeds up development and facilitates collaboration and experimentation.
- For Write-Audit-Publish processes to be clean, LakeFS leverages merge hooks and pre-commit hooks.
- Revert capabilities in a resilient platform allow for quicker recovery from data issues.
Microsoft Azure ML Platform
It has automated machine learning components that determine the best machine learning pipelines and hyperparameters, together with an easy-to-use drag-and-drop interface for model construction called Designer.
All facets of machine learning projects, including data intake, model training, and deployment, can be managed centrally using the Azure ML Studio.
Key Features:
- Drag-and-drop user interface
- Automated machine learning
- Connectivity to Azure DevOps
Also Read: Quantum Machine Learning: Redefining AI’s Frontier
Metaflow
For data science and machine learning applications, Metaflow is a robust, well-tested workflow management solution. Metaflow is for data scientists so they wouldn’t have to bother about MLOps engineering and could concentrate on creating models.
Workflow creation, scaling, and production deployment are all possible with Metaflow. It automatically monitors and versions data and experiments related to machine learning. Additionally, the notebook allows you to see the outcomes. However, these MLOps tools help businesses grow in the machine learning field.
Metaflow is compatible with a number of cloud computing platforms, such as AWS, GCP, and Azure, as well as machine learning Python packages, such as Scikit-learn and Tensorflow. The R language API is also accessible.
Moreover, you can develop workflows, run them at scale, and put the models into production with Metaflow. It automatically logs and refreshes data and experiments related to machine learning.
Numerous cloud service providers (such as AWS, Temok, and Azure) and Python machine-learning packages (like Scikit-learn and Tensorflow) are compatible with it, and the R language may also use the API.
Key Features:
- Performance Monitoring
- Easy-to-Use
- Automatically records and updates machine learning experiments.
Google Cloud Vertex AI
A tool called Google Cloud Vertex AI makes it easier to install and scale AI models by combining all of Google’s AI products into a single API. From data management to model deployment, it offers a unified user interface and API for handling every stage of the machine learning lifecycle.
But Vertex AI has a feature called AutoML that hunts for the best learning algorithms and hyperparameters. It also enables the development, deployment, and management of machine learning pipelines using AI Platform Pipelines.
Additionally, if you need ready-made solutions, Google comes with extensive pre-trained models for image recognition, NLP, and conversational AI. Still, Vertex AI makes an effort towards model accountability and transparency, offering Explainable AI tools to help users better understand and interpret the decisions made by their models.
Key Features:
- Pre-Trained Models
- Explainable AI Tools
- Model Accountability
TFX, or TensorFlow Extended
An end-to-end platform created especially for TensorFlow users is called TensorFlow Extended (TFX). From data intake and inspection to model training, serving, and monitoring, it offers a set of components that span the whole machine learning lifecycle. Therefore, it is another one of the best MLOps tools in 2024. Because of its adaptability, TFX can be easily integrated into current processes. Supporting containerization with Docker or Kubernetes makes repeatability possible in many contexts.
Key Features:
- Easy Integrated
- Support Containerization
Also Read: What Is Machine Learning: Its Importance in Today’s World
TruEra
The TruEra platform supports model development and effectiveness with directness, automated validation, and root-cause study. In addition, it comes with numerous features to make sure you deliver superior explainability, enhance and debug models, and interrelate naturally with your ML tech stack.
Key Features:
- You may boost model quality throughout expansion and production by utilizing the model testing and fixing capability.
- It can conduct systematic and automated testing to guarantee stability, performance, and equity.
- It is aware of how model versions have changed throughout time. However, this enables you to get insights that direct the creation of models more quickly and efficiently.
- Determine which specific characteristics are causing model bias.
- Integrating TruEra into your existing process and infrastructure is simple and hassle-free.
Dagster
Dagster offers data teams an orchestration platform that facilitates effective data pipeline management through a creative, cloud-native methodology. Complex data processes may be defined, carried out, and observed with Dagster.
Additionally, task-based workflows, and asserting programming models and networks with famous MLOps tools are important features that improve testability and observability. Its important features include project administration, automated networks, and model serving.
KubeFlow
An open-source project called Kubeflow was created on top of Kubernetes to make machine learning workflow deployments easier, whether on-site or in the cloud. Kubeflow provides an integrated platform that can effectively manage complicated machine learning workloads by utilizing Kubernetes-native features like scalability and fault tolerance. It interfaces with other MLOps tools like MLflow or Seldon Core and supports well-known ML frameworks like TensorFlow and PyTorch.
Teams Can Perform The Following With Kedro:
- Configure the settings and dependencies.
- Construct, illustrate, and execute pipelines.
- Record and monitor experiments.
- Install on one or more computers.
- Ensure the maintainability of your data science
- Write reusable, modular code.
- Work together on projects with coworkers.
Hugging Face Inference Endpoints
The last tool on our list of top 10 MLOps tools in 2024 is Hugging face inference endpoints. The cloud-based service Hugging Face Inference Endpoints allows users to train, support, and share models, datasets, and demos. Moreover, Hugging Face is an all-in-one machine learning platform. Without having to set up and maintain the necessary infrastructure, these endpoints are made to assist users in deploying their generated machine learning models for inference.
Key Features:
- Depending on your demands, keep the price as low as $0.06 per CPU core/hour and $0.6 per GPU/hour.
- Deployment is simple and takes only a few seconds.
- Autoscale and entirely handled.
- A component of the ecology of Hugging Face
- Security at the corporate level.
Conclusion
The fundamental problem of turning notebooks into production-ready programs is being addressed by new developments, companies, and methods in MLOps every week. Moreover, to become MLOps solutions, even legacy tools are expanding their use and adding new features.
Additionally, the collection of MLOps tools landscape for experimentation, creation, execution, and monitoring is intended to assist you in developing a strong MLOps practice.
Keep in mind that your machine learning efforts may be significantly accelerated and made simpler with the correct list of tools for MLOps. A clear strategy covering the entire project lifespan is still the most essential component, though. You may maximize the potential of your machine learning endeavors by combining the appropriate technologies with a planned strategy.
FAQs (Frequently Asked Questions)
What Is MLOps Software?
Data scientists and engineers may better manage the machine learning (ML) life cycle by using a set of methods called MLOps (machine learning). Moreover, it seeks to close the gap between machine learning development and operations.
What Is The Difference Between ML And MLOps?
Machine learning operations (MLOps) and machine learning (ML) vary primarily in that MLOps focuses on the full lifetime of an ML model, whereas ML concentrates on creating and testing models.
Is MLflow An MLOps Tool?
Yes, MLflow is the best open-source MLOps solution available, and we highly advise incorporating it into your machine learning process. MLflow’s many components significantly increase productivity at different phases of your machine learning process.
What Is The Best Programming Language For MLOps?
Python is now the machine learning champion. This is due to Python’s extensive collection of machine-learning technologies. Popular libraries include Pytorch, Tensorflow, NumPy, and Keras.