Machine Learning Development

What exactly is Machine Learning as a Service?

Machine Learning as a service (MLaaS) isn't new on the block of aaS (no implied) However, recently it's been receiving much attention due to the value and power it has been for machine learning researchers, data scientists engineers, data engineers, and other professionals working in machine learning.

Machine learning as a service is a broad term used to describe the various cloud-based platforms that utilize machine learning tools to offer solutions that assist ML teams in:

* Out-of-the-box predictive analysis to address various situations,

* Data pre-processing

* Model training and tuning

* Run orchestration

* model deployment.

It makes use of cloud computing's power to provide machine learning services while on the move.

What can we expect from the MLaaS platform?

Data Management: As many businesses shift their data from their storage on-premises to cloud storage systems, the necessity to correctly manage the data comes into. Because MLaaS platforms are basically cloud service providers, which means they offer cloud storage. They offer ways to effectively handle data to support machine learning research and data pipelining, making it simpler for engineers working with data to process and access the data.

The ability to use ML Instruments: MLaaS providers offer tools that include predictive analytics as well as visualization of data for companies. They also offer APIs to analyze sentiment facial recognition, creditworthiness assessments healthcare, business intelligence, etc.

Data scientists do not have to think about the actual processing of these tasks since they are abbreviated through MLaaS providers. Certain MLaaS providers also provide an interface that you can drag and drop to experiment with machine learning and modeling (with the limitations obviously).

Easy to use: MLaaS offers Data scientists the chance to start quickly in machine learning, without the long and tedious process of installing software or even provide their own servers, as they do with other cloud computing options. With MLaaS the data centers provided by the provider take care of the actual computations and it's a great solution to businesses everywhere.

Cost-efficiency: The construction of an ML workstation can be expensive. At the moment of writing this article one, Nvidia GPU is priced at $699, while a Google cloud TPU version 2 retails for $4.50.

Therefore, in choosing an in-cloud TPU Data scientists will have already completed 150 hours of tests before getting to the price of purchasing the GPU from Nvidia. Additionally, the chipset requires huge amounts of power in order to function. So the cost of electricity will go up.

MLaaS is also beneficial in the process of development because you only purchase hardware when it actually is employed.

MLaaS platforms can provide these solutions and others. Let's take a look at a brief review of the various platforms that offer this MLaaS solution and the ways to access them.

When not to use MLaaS

1. If your data has to be protected and stored on-premises the data is on-premise, then you shouldn't employ MLaaS.

2. If you're in need of a lot of flexibility and the most up-to-date algorithm implementation, then you do not need MLaaS (but it can be beneficial)

3. If you are looking to improve the cost of training or to serve complicated algorithms, then you may need to consider putting your infrastructure in-prem

When to use MLaaS

1. If you're already using one of these MLaaS companies mentioned above by the firm, adding their MLaaS services into your system could be a beneficial addition.

2. If there are many use-cases that can be outsourcing via a prediction API MLaaS is the method to take.

3. If your application produces a lot of data and you have to run tests frequently using the data, you should definitely consider MLaaS.

4. If you are running a microservices-based structure in your business, MLaaS would help in managing the correct administration of some of these services.

To read more - https://www.leewayhertz.com/machine-learning-services/