Choosing the right machine learning operations (MLOps) tools is essential for ensuring that your business can effectively take advantage of AutoML (Automated machine learning) to build machine learning models at scale. However, it can be challenging to determine which toolset is best for your needs with so many options available.
This blog post will outline 15 features to look for when choosing MLOps tools.
- Platform vs. Specific Tools
- Coverage of MLOps Tasks
- Coverage of Needed Libraries
- Product Support
- GUI or CLI
- Cloud-agnostic or Cloud-specific
- Open-source vs. Proprietary
- Model Templating and Cataloging
- Model and Production Environment Monitoring
- Pipeline Management
- Model Training, Tuning, and Drift Management
- Collaboration and Communication Capabilities
- Multi-Stage Deployment
- Scalability
- Pricing
1. Platform vs. Specific tools
Some companies prefer a Machine Learning platform that provides an end-to-end solution with many integrations. At the same time, some other organizations might want more specific tools that they can piece together to form a complete solution. There is no wrong answer here; it just depends on what your company’s preferences are.
If you decide that you want a platform, then you should evaluate features such as:
- Does the platform provide an easy way to monitor your machine learning models in production?
- Is it easy to deploy new versions of models?
- And, how many steps are needed to get from data to predictions?
On the other hand, if you’d rather have specific tools, you should ask yourself:
- Can these tools be easily integrated with each other and my current infrastructure?
- Do these tools cover all the machine learning operations tasks that I need?
- What kind of support do these tools have?
It’s important to note that many options are available on both sides. Do your research and decide what is best for your company.
2. Coverage of MLOps Tasks
When choosing machine learning operations tools, you should ensure that the toolset covers all of the tasks involved in MLOps.
Some of these tasks include:
- Data collection
- Data pre-processing
- Model training
- Model deployment
- Model monitoring and management
If a tool only covers some of these tasks, it might not be the best fit for your company. You want to make sure that you have a complete solution to manage your machine learning models throughout their entire lifecycle effectively.
It’s also important to consider how easy it is to use the tools. If they are too complicated to use, it will take longer to get up and running and lead to errors.
Make sure to read reviews and compare different options before making a final decision.
3. Coverage of Needed Libraries
Another important consideration is whether or not the toolset you are considering covers all of the libraries you need.
Some of the most popular machine learning libraries include:
- TensorFlow
- Keras
- PyTorch
- Scikit-learn
If a toolset only supports a limited number of these libraries, it might not be able to meet all of your needs. Therefore, it’s essential to ensure that the tools you choose can work with all of the libraries you need to take full advantage of their capabilities.
Don’t also forget to consider the ease of installing and updating these libraries. If a tool requires manual installation or updates, it will take more time and effort to keep everything up-to-date.
Be sure to check this before making a final decision so that you can be confident that the toolset you choose will meet all of your needs.
4. Product Support
When using machine learning tools, it’s crucial to have access to product support if you run into any issues.
Some companies offer free support while others charge for it. Some only offer support during business hours, while others provide 24/hour support.
It’s important to consider what kind of support you need and whether or not the company offers it before making a final decision.
You don’t want to be stuck with a tool that doesn’t work correctly and have no way to get help. So be sure to check this before you make your purchase to be confident that you will have the support you need if you run into any problems.
5. GUI or CLI
When you’re choosing machine learning operations tools, you should decide whether you want a graphical user interface (GUI) or a command-line interface (CLI).
Some people prefer GUIs because they are easier to use and don’t require coding knowledge. Others prefer CLIs because they are more flexible and customized to meet their specific needs.
Again, there is no right or wrong answer here; it just depends on your personal preferences.
6. Cloud-agnostic or Cloud-specific
Another important consideration is whether you want a toolset that is cloud-agnostic or specific to a particular cloud provider.
Cloud-agnostic tools can be used with any cloud provider, while cloud-specific tools are designed to work only with one provider.
Both options have pros and cons, so it’s essential to decide what is best for your company.
If you’re not sure which way to go, consider talking to an expert who has experience with both options so that you can make an informed decision.
7. Open-source vs Proprietary
When choosing machine learning operations tools, you should also decide whether you want open-source or proprietary options.
Open-source options are usually free and adjustable to meet your specific needs. Proprietary options are generally more expensive but can offer more features and support.
8. Model Templating and Cataloging
When looking for machine learning operations tools, finding ones that offer model templating and cataloging is important.
Model templating allows you to create templates for your models to deploy them easily. Cataloging helps you keep track of all the different models you have to quickly find the one you need.
Both of these features can save you a lot of time and effort when you’re deploying models.
9. Model and Production Environment Monitoring
Another critical feature in machine learning operations tools is model and production environment monitoring.
This feature allows you to keep track of your models and production environments so that you can quickly identify and fix any issues that arise.
It’s essential to have this kind of visibility into your system so that you can keep everything running smoothly.
Without it, you might not be able to identify problems until they cause significant disruptions.
Model and production environment monitoring is critical for keeping your system running smoothly.
10. Pipeline Management
Pipeline management is another critical feature to look for in machine learning operations tools.
Pipeline management allows you to automate the process of training, testing, and deploying models.
It can save you time and effort by automating repetitive tasks.
11. Model Training, Tuning, and Drift Management
When looking for machine learning operations tools, it’s important to find ones that offer model training, tuning, and drift management.
Model training helps you improve the performance of your models by adjusting the parameters. In addition, model tuning allows you to optimize your models for specific environments.
Drift management helps you detect, and correct data drift so that your models remain accurate.
12. Collaboration and Communication Capabilities
You should look for machine learning operations tools that offer collaboration and communication capabilities.
This feature allows you to work with others on your team to develop and deploy models.
It’s important to have this capability to share knowledge and ideas.
Without it, you might not be able to take advantage of the expertise of everyone on your team.
13. Multi-Stage Deployment
Another vital feature to look for in machine learning operations tools is multi-stage deployment.
Multi-stage deployment allows you to deploy your models in multiple stages, such as development, staging, and production.
This can be helpful if you want to test your models in different environments before
14. Scalability
When choosing machine learning operations tools, it’s important to consider scalability.
Scalability refers to the toolset’s ability to handle increased workloads as your company grows.
If you anticipate that your company will be growing soon, you need to make sure that the toolset you choose can scale with you.
15. Pricing
Last but not least, you need to consider pricing when you’re choosing machine learning operations tools.
Pricing can vary depending on the features and support that you need. So ensure you are comparing apples with apples.
You should consider your budget and needs when you’re making your decision.
These are just a few of the many things to consider when choosing MLOps tools. Be sure to do your research and compare different options before making a final decision. The right toolset will save you time and effort in the long run and help you effectively manage your machine learning models.
What other features should be considered when choosing MLOps tools? Let us know in the comments below!
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