Artificial intelligence | 18 May 2022 | 11 min
Machine learning has grown to be quite the popular trendsetter in recent years for different organizations. Hence, it is unsurprising to see major cloud companies coming up with custom cloud solutions to support data scientists in every way possible.
The growing trends of shifting data storage to cloud, scalability, data security, maintenance, and deriving insights from data have given rise to MLaaS at a federated cost. Machine learning is not just restricted to building the model, rather it consists of many complex components such as data ingestion, data preparation, model training, hyperparameter tuning, model deployment, model monitoring, explainability, and many more. It also requires collaboration across teams, from Data Engineering to Data Science to ML Engineering. With machine learning as a service, data scientists can manage these intricacies.
Machine Learning as a Service (MLaaS) is a collection of services that provide machine learning tools as part of cloud computing services. It is based on machine learning technology that implies teaching machines to recognize patterns. A typical MLaaS will consist of services that will cover most of the aspects of a machine learning life cycle such as:
MLaaS services are often ready-made services that can be adapted by any organization as a part of their working needs. The key is that the organizations do not need to worry about the infrastructure and computations. The providers’ data centers manage them remotely.
Let’s understand some benefits of these services:
MLaaS platforms offer these solutions and many more. Let’s now look at a brief overview of some platforms offering these MLaaS solutions.
These are some of the leading providers of MLaaS services:
Following are some of the MLaaS services offered by the key players in the market:
In this blog, we will focus mainly on AWS machine learning services, as it captures 70% of the market as of today. AWS is the oldest of its peers and being the oldest, it has already established trust and reliability factors. Read on for more details…
Amazon-tailored machine learning services are one of the best automated solutions in the market. Amazon Web Services offers machine learning via two ways. The first one is through pretrained models which are ready-to-use services which you can use without any machine learning expertise and the second one is via a machine learning development IDE called Amazon Sagemaker which you can use to build powerful machine learning models.
Source- https://www.infoworld.com/article/3608413/review-aws-ai-and-machine-learning-stacks-up-and-up.html
A few important ready-to-use services are as follows:
These services contain quite exhaustive implementation documentation that is simple to follow and apply.
Now let’s take a look at some of the disadvantages of MLaaS.
Well, there aren’t any major disadvantages associated with these services, but if your data needs to be in premise and there are security issues, then you shouldn’t move to MLaaS. Also sometimes, you want to train a model locally and then deploy the model. In this case at least you can save on the training part, and you shouldn’t be using MLaaS.
There are a lot of complexities involved in building a machine learning application end to end, and hence MLaaS is helping us to do so with just a few clicks. But we cannot deny that to use these services we will need some data science or domain expertise. In the next series, we will dig into one of the most important services of Amazon – AWS Sagemaker – where we will train and deploy a model. Deep learning has made a lot of practical applications of machine learning possible!
Do check out this blogthis blog our blog to learn about discover an automated machine learning approach with H2O.ai.
Additionally read some interesting things about AI Engineering.
Reach out to us at Nitor Infotech if you’d like to know more about pioneering technology, we leverage to partner businesses like yours through the digital transformation journey.
we'll keep you in the loop with everything that's trending in the tech world.