Machine-learning-as-a-service (MLaaS) tools for data analytics could increase the accuracy and efficiency of your research in the data science realm without requiring substantial upfront costs from on-site equipment. That’s because MLaaS options exist in the cloud. Here are six you should keep in mind if you’re planning to invest in machine learning tools or would like to learn more about them.
1. Azure Machine Learning Studio
This option from Microsoft features a drag-and-drop interface that doesn’t require coding expertise. It takes an applied approach to machine learning, allowing you to integrate the technology into your work swiftly. The visual-based interface also allows you to export data related to predictive analytics, making it simple to share your findings with executive board members or other superiors.
There’s a free version available that lets you experiment with the program and how it works, too. Premium pricing starts at $9.99 per month.
2. Amazon Machine Learning
This machine learning service from Amazon features the same technology used internally by its data scientists, now available for customers who sign up for the service. Amazon is one of the leading providers of MLaaS, and because it is highly automated, it’s an ideal option for data scientists who need to rely on machine learning to meet tight deadlines.
For example, the technology offers three machine learning prediction capacities, and it’s not necessary to know any machine learning methods before importing data. The tool analyzes the information and chooses the best one for you.
Pricing for Amazon’s technology is not as straightforward as what Microsoft provides, though. For example, details about the cost mention data analysis and model building fees, plus prices charged for predictions.
3. Watson Machine Learning
In March 2018, IBM hosted Think 2018, one of the biggest technology events ever. The conference schedule split events across several campuses, including one called Business and AI that featured cloud-based analytics solutions. Attendees undoubtedly learned about IBM’s Watson Machine Learning while there.
It allows creating machine learning models with visual-based tools that aid users in spotting patterns and making more-intelligent decisions than they could without those offerings. Also, Watson Machine Learning has a deep-learning component incorporating neural networks.
There are several options under the Watson Machine Learning umbrella. Watson Studio allows using open-source data science tools and interacting with drag-and-drop information on dashboards. Besides a free version, there are premium options to consider. There’s also the Watson Knowledge Catalog, offering datasets and more to inform data scientists’ work.
4. Google Cloud Machine Learning Engine
By using this product, you can train machine learning models, then use either online prediction or batch prediction to apply what the model learned through training to make it more intelligent. Plus, you aren’t restricted to only training machine learning models within Google’s product. It accepts models trained anywhere.
If you’re new to machine learning, think about learning the principles through an online course and enrolling in it at the same time you use this MLaaS option from Google.
Getting pricing details for your project is only possible by contacting Google, but its rates are reportedly substantially less than other providers for some projects.
The desire that motivated the team behind Big ML was to make machine learning accessible to everyone. So, if you’ve long been interested in applying machine learning to your data science career but weren’t sure where to start, BigML might help.
It’s like some of the other services on this list in that automation plays a significant part in how the product works. You can import data from multiple sources and quickly build models that cater to your workflow. It’s even possible to embed models into mobile applications and use them to make predictions.
There’s a free plan, plus premium tiers starting at $30.
Dataiku offers users state-of-the-art machine learning libraries and permits people to use R and Python to customize code for advanced tweaks. The interface gives feedback about the importance of different variables, too. Then, you can instantly understand which features in the Dataiku program impact your predictions the most.
Also, if it’s necessary to retrain a model, there’s no need to start from scratch. You can track and save the lifetime progression of a model and revert to an old version with a click.
Dataiku doesn’t publish pricing details on its website because the rates vary depending on needs. However, you can contact the sales team to learn more after deciding how machine learning fits your data science requirements best.
Access to Technology Without On-Site Infrastructure
One of the past challenges for data scientists and people in other career paths who wanted to explore machine learning was the costs involved in buying and setting up pricey on-site equipment. These cloud-based MLaaS options eliminate that aspect and offer scalable solutions to current and potential customers.