Top Software Picks & Reviews

Software Development

Top MLOps Tools Of 2024

By TechDogs Editorial Team

Overall Rating

Overview

If you’ve binge-watched the TV series Black Mirror or horror flicks like M3GAN or any tech-gone-bad movie, you might wonder how close we are to such a future. Scientists and research communities share this curiosity and are actively exploring the potential of AI.

Take M3GAN, for example. What can a child, a toy inventor/scientist and a murderous cyborg doll teach us about technology? No spoilers (but yes, M3GAN does turn into a killer robot). Instead, let's marvel at the wonders of its backbone: Machine Learning!

In the movie, M3GAN’s capabilities reflect real innovation in Machine Learning. On another level, Gemma, her inventor, is also continuously learning from M3GAN’s actions, behavior and responses. She realizes that M3GAN's (machine) development is an ongoing lifecycle with various stages (learning).

With everyone buzzing about Machine Learning and MLOps, how can Gemma find the right platform to train M3GAN to not turn into a killer robot? Well, don’t worry, Gemma - we’ve got you covered.

Here are the top 5 Machine Learning Operations (MLOps) Tools of 2024!

Are you familiar with Machine Learning and how it streamlines your data processes? If so, you're in the right place!

Today, we’ll delve into MLOps Tools, which stands for Machine Learning Operations. These tools encompass a set of practices designed to help businesses automate their Machine Learning workflows and deployments.

MLOps is primarily used to structure the development of machine learning models and align teams for efficient development. As you're aware, AI has been revolutionizing the tech landscape over the past few years. Well, MLOps is no exception!

MLOps Tools have hence become popular due to their flexibility, community support and adaptability to various workflows. On the other hand, closed-source platforms often offer enterprise-grade features, enhanced security and dedicated user support.

So, what’s the best tool for you? Let’s end the suspense by diving into the top 5 MLOps Tools of 2024!

Top 5 MLOps Tools Of 2024

For a quick recap, MLOps Tools helps streamline the machine learning lifecycle. They integrate right from the development stage, throughout operations and the entire lifecycle process by facilitating tasks such as data preprocessing, model training, deployment, monitoring and scaling. These tools help development teams seamlessly collaborate and effectively manage ML models in production!

So, here are the top 5 MLOps Tools of 2024 – in a spooky, creepy, random order!

Amazon Sagemaker	Microsoft Azure	LakeFS
MLFlow	PyTorch

Tool 1: Amazon SageMaker

Once upon a time, in 1994, the tech landscape saw the birth of Amazon. Originally called Cadabra for a year, Amazon is headquartered in Seattle, Washington, USA and employs over 1.5 million people worldwide. In 2023, Amazon won in two categories at the Retail Week Awards and JMCP Award of Excellence, along with many more recognitions for its innovative solutions.

Source

Amazon SageMaker is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning for any use case. It's a fully managed machine learning service that allows developers and data scientists to build, train and deploy machine learning models at scale. It helps build, train and deploy ML models at a scale using tools like notebooks, debuggers, profilers, pipelines, MLOps and more – all in one integrated development environment (IDE).

Based on a pay-as-you-go model, SageMaker allows users to train their workflows by using their own algorithms and training scripts, with support for distributed training across multiple instances. It also makes it easy to deploy trained models to production environments using managed hosting services, with support for real-time inference and batch prediction. How cool is that!

Feature Ratings:

Ease Of Use: ⭐⭐⭐⭐
Value For Money: ⭐⭐⭐⭐
Startup Friendly: ⭐⭐⭐⭐
Enterprise Friendly: ⭐⭐⭐⭐
Customer Support: ⭐⭐⭐

Tool 2: Microsoft Azure ML

Ok, everyone is a little biased and so are we about the legendary Microsoft. Founded in 1975 by Bill Gates and Paul Allen, its current headquarters is in Washington, United States. They are ranked fifth in ‘America’s 100 Most Just Companies’ and received a 100% rating on the HRC Corporate Equality Index™ for 2023. Leaders for a reason!

Source

The Azure Machine Learning by Microsoft is a cloud-based machine learning platform. It offers data scientists and developers a unified platform to build, train and deploy machine learning models at scale. It also boasts a wide range of tools and services for the complete ML lifecycle, from data preparation and model development to deployment and monitoring.

What helps it stand out in the market is its ability to quickly build and deploy machine learning models without extensive manual tuning or coding. With its model interpretability and explainability, it allows users to understand how models make predictions and identify factors contributing to model performance. From beginners to experts, this MLOps Tool is for everyone!

Feature Ratings:

Ease Of Use: ⭐⭐⭐⭐
Value For Money: ⭐⭐⭐⭐
Startup Friendly: ⭐⭐⭐
Enterprise Friendly: ⭐⭐⭐⭐
Customer Support: ⭐⭐⭐⭐

Tool 3: lakeFS

lakeFS was developed by Oz Katz and Einat Orr and was first released in August 2020. With official partners on board such as AWS, Azure and NetApp, it serves clients such as Netflix, Enigma and Karius. LakeFS is on a mission to simplify the lives of data engineers, data scientists and analysts providing a data version control platform at scale. Pretty cool, if you ask us!

Source

lakeFS is an open-source, scalable data version control solution that provides a Git-like version control interface for object storage. lakeFS helps accelerate your team and simplify the version control process for the ML use cases. It has a unique zero-copy import of datasets, making different versions that are available to all. It supports both structured and unstructured datasets and trains the ML model locally at scale in a distributed computing environment.

Users often commend lakeFS’ open-source features as well as its resilient platform that enables faster recovery from data loss through its robust revert capabilities. Moreover, it provides faster development with zero-copy branching, allowing for seamless experimentation and cooperation for ML development teams.

Feature Ratings:

Ease Of Use: ⭐⭐⭐⭐
Value For Money: ⭐⭐⭐⭐
Startup Friendly: ⭐⭐⭐⭐
Enterprise Friendly: ⭐⭐⭐
Customer Support: ⭐⭐⭐⭐

Tool 4: MLFlow

MLFlow is headquartered in California, United States and was founded in 2018. MLFlow was founded by Matei Zaharia & Aaron Davidson and in 2023, they crossed 16 million users. Today, they have esteemed clients such as Meta, Booking.com, Zillow and many more. MLflow was successfully incorporated with popular AI services and frameworks such as Hugging Face, LangChain and OpenAI in 2023.

Source

MLFlow is an open-source tool that helps businesses manage core parts of their machine learning lifecycle. It is used for experiment tracking but for reproducibility, deployment and model registry. Organizations can manage machine learning experiments and model metadata by using CLI, Python, R, Java and REST API.

What’s more, MLFlow’s core functions range from tracking, storing, project management to deployment. It helps create a central model store that provides versioning, stage transitions, annotations and managing machine learning models.

Feature Ratings:

Ease Of Use: ⭐⭐⭐⭐
Value For Money: ⭐⭐⭐⭐
Startup Friendly: ⭐⭐⭐⭐
Enterprise Friendly: ⭐⭐⭐
Customer Support: ⭐⭐⭐⭐

Tool 5: PyTorch

Nestled in Warsaw, Poland, PyTorch was founded in 2016. They have a robust client portfolio that includes Amazon Advertising, Salesforce and even Stanford University. It was originally developed by Meta (formerly Facebook) and in 2022 is part of the PyTorch Foundation under the Linux Foundation umbrella.

Source

PyTorch was developed by Facebook’s AI Research Lab (FAIR) and is a popular machine learning framework that offers a flexible and dynamic approach towards neural networks. PyTorch provides more control over the training process as things are not abstracted to the virtual layer. Moreover, PyTorch can be deployed for different ML use cases thanks to its adaptability.

What’s great about PyTorch is that as an open-source tool, it allows users to develop deep learning models too. They use dynamic computational graphs that allow flexibility and intuitive debugging compared to static graph-based frameworks. It also seamlessly integrates with CUDA for GPU acceleration, enabling faster model training and inference on NVIDIA GPUs. This makes it an ideal solution for enterprises looking to boost their MLOps!

Feature Ratings:

Ease Of Use: ⭐⭐⭐
Value For Money: ⭐⭐⭐
Startup Friendly: ⭐⭐⭐⭐
Enterprise Friendly: ⭐⭐⭐
Customer Support: ⭐⭐⭐

Conclusion

The machine learning landscape is continually evolving. Naturally, it is driven by the need for robust, scalable and efficient tools. That’s where MLOps Tools shine!

Our top 5 MLOps picks offer unique capabilities that streamline the end-to-end machine learning lifecycle. Right from data preprocessing and model training to deployment and monitoring. MLOps Tools empower teams to automate workflows, ensure reproducibility and maintain model performance in production environments.

Remember: since each business has unique MLOps needs, make sure you choose a tool that meets all your requirements!

Frequently Asked Questions

What Are The Top 5 Mlops Tools Of 2024?

The top 5 MLOps tools of 2024 are Amazon SageMaker, Microsoft Azure ML, lakeFS, MLFlow and PyTorch. These tools offer various features and capabilities to streamline the machine learning lifecycle, from data preprocessing and model training to deployment and monitoring.

How Do Mlops Tools Help In Managing Machine Learning Workflows?

MLOps tools help manage machine learning workflows by automating and streamlining various stages of the ML lifecycle. They integrate processes such as data preprocessing, model training, deployment, monitoring and scaling. This ensures seamless collaboration among development teams and efficient management of ML models in production environments, ultimately improving productivity and model performance.

What Features Should I Look For In An Mlops Tool?

When selecting an MLOps tool, look for features such as ease of use, scalability, support for various machine learning frameworks, integration with other tools and strong community or enterprise support. Additionally, consider the tool's capabilities in terms of experiment tracking, model deployment, version control and monitoring to ensure it meets your specific requirements and workflow needs.

Enjoyed what you read? Great news – there’s a lot more to explore!

Dive into our content repository of the latest tech news, a diverse range of articles spanning introductory guides, product reviews, trends and more, along with engaging interviews, up-to-date AI blogs and hilarious tech memes!

Also explore our collection of branded insights via informative white papers, enlightening case studies, in-depth reports, educational videos and exciting events and webinars from leading global brands.

Head to the TechDogs homepage to Know Your World of technology today!

Disclaimer - Reference to any specific product, software or entity does not constitute an endorsement or recommendation by TechDogs nor should any data or content published be relied upon. The views expressed by TechDogs' members and guests are their own and their appearance on our site does not imply an endorsement of them or any entity they represent. Views and opinions expressed by TechDogs' Authors are those of the Authors and do not necessarily reflect the view of TechDogs or any of its officials. All information / content found on TechDogs' site may not necessarily be reviewed by individuals with the expertise to validate its completeness, accuracy and reliability.

Tags:

Top MLOps Tools Of 2024 MLOps Tools Machine Learning Operations Amazon Sagemaker Microsoft Azure LakeFS MLFlow PyTorch

Join The Discussion

Communication And Collaboration Software