Machine learning has grown multifold in recent years. Several organizations have invested in this technology and are reaping its benefits. The impact has forced many more companies to dive into this field and get the biggest piece of the pie, though there's plenty to go around.
With such a growing demand for AI applications, the supply has also been growing rapidly in the form of tools and technologies. Today, various machine learning platforms claim to take care of the end-to-end ML cycle.
Having the luxury of such options has led to organizations questioning what platform would be the best for them.
At such a crossroads, we advise our partners to dig deep and identify what exactly they need in their ML lifecycle - whether it is the ease of building data pipelines, training the models, or rapidly deploying them.
However, most organizations desire a “perfect” machine learning platform that could help them glide through all the stages in an ML lifecycle - build, train, and deploy. Though we believe there is no perfect platform that could fit the bill, AWS SageMaker comes pretty close due to its flexibility and scalability.
In this blog post, we have listed why AWS SageMaker is an ideal machine learning platform and what are its shortcomings.
The Advantages of AWS SageMaker
Dynamic computing instances
SageMaker offers a range of computing instances with various numbers of CPUs, GPUs, and RAM. You can select a computing instance based on your application requirements.
From 2 core CPUs to 96 core CPUs, and 4GB RAM to 768GB RAM, you can select any instance of your choice with just one click. There is no need to manage security groups, AMIs, or the IP addresses of these machines.
AWS rich algorithm library
SageMaker comes with Amazon’s libraries containing high-performing algorithms that can be leveraged to train your model using Amazon’s pre-trained models. Both the algorithms and the models are highly optimized to run on AWS services.
SageMaker also offers you the option to use your algorithms that can be brought in using industry-leading containers.
Hosting the model in an endpoint
SageMaker offers the flexibility to host the model in an endpoint and then call it from a code written in any common coding language. You can call the endpoint from a Lambda function, or if you have a hosted API, you can call the endpoint using the API code.
Additionally, you can host multiple models in a single endpoint. This is a little complex though, as you will have to create a custom Docker image.
AWS ML Community
AWS also offers a diverse community of data scientists, software developers, AI researchers, and industry experts with rich experience in the field of machine learning. Using SageMaker will give you access to tap this community and collaborate with these experts.
You will also be able to learn and explore the various features and possibilities with AWS SageMaker irrespective of your experience in machine learning.
Smart hyperparameter tuning
SageMaker offers smart hyperparameter tuning settings for a variety of models. You can schedule a tuning job whenever you wish, and SageMaker will showcase the best hyperparameter based on that.
Pay as you use model
The fee of using AWS SageMaker is one of its best advantages. Typically, you pay a comparatively large sum for using such platforms because of the need to install, run the code, and then shut down the system.
Whereas with SageMaker, you can use a computing instance for less than a minute, billed at a few dollars per hour, and you will still be charged for the seconds you use the instance. You can leverage this dynamic pricing by using a cheap instance to host your notebook and a high-performing GPU to train it.
You can choose from a plethora of computing instances offered by SageMaker based on your need and your budget. With SageMaker, you don’t have to pay for the resources you don’t use.
Provides Jupyter notebooks
AWS SageMaker provides Jupyter notebooks to data scientists. This advantage is highly appreciated by the data scientist community as they can use Jupyter notebooks for the entire ML lifecycle - building, training, and deploying.
These notebooks run R or Python kernels, and data scientists can process, clean, and transform the data into any form they prefer, using the traditional methods like Pandas & Matplotlib, R & ggplot2, etc.
SageMaker Has Its Shortcomings
With all the above-mentioned advantages, AWS SageMaker is still not perfect and comes with its cons.
- With SageMaker, you will have to still code your data engineering needs, whereas other platforms may offer a no-code(drag and drop) solution
- Configuring the preprocessing and post-processing pipelines and the Docker containers for endpoints with multiple models is a tedious task
- AWS documentation is not very easy to get around
Summing it up
AWS SageMaker isn’t a perfect machine learning platform, because such a perfect ML platform doesn’t exist. It offers the scalability and flexibility needed to build a high-performing model while still being cost-effective. And with AWS’s constant additions, it’s only going to get better. But you can leverage the host of benefits it offers with the right SageMaker experts. If you have a ML use case to discuss with our team, you can drop us a message here.