NEWSLETTER
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Have you ever felt that there are too many tools for MLOps? There is a tool for experiment tracking, data and model versioning, workflow orchestration, feature repository, model testing, deployment and serving, monitoring, runtime engines , LLM frameworks and more. Each tool category has multiple options, which is confusing for managers and engineers who want a simple solution, a unified tool that can easily perform almost all MLOps tasks. This is where end-to-end MLOps platforms come into play.
In this blog post, we will review the best end-to-end MLOps platforms for personal and business projects. These platforms will allow you to create an automated machine learning workflow that can train, track, deploy, and monitor models in production. Additionally, they offer integrations with several tools and services that you may already be using, making it easy to transition to these platforms.
1.AWS SageMaker
amazon.com/sagemaker/” rel=”noopener” target=”_blank”>amazon SageMaker is quite a popular cloud solution for end-to-end machine learning lifecycle. You can track, train, test, and then deploy the model to production. Additionally, you can monitor and retain models to maintain quality, optimize compute to save costs, and use CI/CD pipelines to fully automate your MLOps workflow.
If you are already on the AWS (amazon Web Services) cloud, you will have no problem using it for your machine learning project. You can also integrate the machine learning process with other services and tools that come with amazon Cloud.
Similar to AWS Sagemaker, you can try Vertex ai and Azure ML. They all provide similar features and tools to create an end-to-end MLOP pipeline with integration with cloud services.
2. Hug your face
I am a big fan of hugging face platform and team, creating open source tools for machine learning and large language models. The platform is now end-to-end as it now provides the enterprise solution for multiple GPU power model inference. I highly recommend it for people who are new to cloud computing.
Hugging Face includes tools and services that can help you build, train, tune, test, and deploy machine learning models using a unified system. It also allows you to save and version models and data sets for free. You can keep it private or share it with the public and contribute to open source development.
Hugging Face also provides solutions for building and deploying web applications and machine learning demos. This is the best way to show others how fantastic your models are.
3. Iguazio MLOps Platform
Iguazio MLOps Platform is the all-in-one solution for your MLOps lifecycle. You can create a fully automated machine learning pipeline for data collection, training, tracking, deployment, and monitoring. It's inherently simple, so you can focus on building and training amazing models instead of worrying about deployments and operations.
Iguazio allows you to ingest data from all types of data sources, comes with a built-in feature warehouse, and has a dashboard to manage and monitor models and production in real time. Additionally, it supports automated tracking, data versioning, CI/CD, continuous model performance monitoring, and model drift mitigation.
4. DagsHub
DagsHub It is my favorite platform. I use it to build and display my portfolio projects. It is similar to GitHub but for data scientists and machine learning engineers.
DagsHub provides tools for code and data version control, experiment tracking, mode registration, continuous integration and deployment (CI/CD) for model training and deployment, model serving, and more. It is an open platform, meaning anyone can build, contribute and learn from the projects.
The best features of DagsHub are:
- Automatic data annotation.
- Model service.
- Visualizing ML pipelines.
- Differentiate and comment on Jupyter notebooks, code, datasets, and images.
The only thing it lacks is a computing instance dedicated to model inference.
5. Weights and biases
ai/site” rel=”noopener” target=”_blank”>Weights and biases It started as an experimental monitoring platform but evolved into an end-to-end machine learning platform. It now provides experiment visualization, hyperparameter optimization, model registration, workflow automation, workflow management, monitoring, and code-free machine learning application development. Additionally, it also comes with LLMOps solutions such as LLM application exploration and debugging and GenAI application evaluations.
Weights & Biases comes with private and cloud hosting. You can host your server locally or use it for survival. It's free for personal use, but you have to pay for business and team solutions. You can also use the core open source library to run on your local machine for privacy and control.
6. Model bit
Modelbit It is a new but full-featured MLOps platform. Provides an easy way to train, deploy, monitor, and manage models. You can deploy the trained model using Python code or the `git push` command.
Modelbit is designed for both Jupyter Notebook lovers and software engineers. In addition to training and deployment, Modelbit allows us to run models on autoscale computing using your preferred cloud service or dedicated infrastructure. It is a true MLOps platform that allows you to record, monitor and alert on the model in production. Additionally, it comes with model registration, automatic retraining, model testing, CI/CD, and workflow versioning.
7. True casting
True casting It is the fastest and most cost-effective way to build and deploy machine learning applications. It can be installed on any cloud and used locally. TrueFoundry also comes with multi-cloud management, auto-scaling, model monitoring, version control, and CI/CD.
Train the model in the Jupyter Notebook environment, track experiments, save the model and metadata using the model registry, and deploy with a single click.
TrueFoundry also provides LLM support, where you can easily fine-tune open source LLMs and deploy them using the optimized infrastructure. Additionally, it comes with integration with open source model training tools, model storage and serving platforms, version control, Docker registry, and more.
Final thoughts
All the platforms I mentioned above are enterprise solutions. Some offer a limited free option and others have an open source component attached. However, you will eventually have to move to a managed service to enjoy a full-featured platform.
If this blog post becomes popular, I will introduce you to free and open source MLOps tools that provide greater control over your data and resources.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a master's degree in technology management and a bachelor's degree in telecommunications engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
