Choosing the right machine learning platform for a business is important if you want to manage your business in a well-organized and more powerful manner. It allows you to predict your upcoming expenses as well as manage your inventory system more productively.
Machine learning platforms provide the necessary tools to develop, deploy, and improve machine learning.
Today we’ll discuss two of the most utilized machine learning platforms; AWS SageMaker and Google Cloud ML, and will find out which machine learning platform is right for you.
What is AWS SageMaker
Let’s first understand what AWS SageMakeris is and then compare these two platforms.
AWS SageMaker or Amazon SageMaker is a machine learning service used by scientists and developers. With the help of this service, they can create and train machine learning models after which they can deploy them for predictive analytics apps.
AWS SageMaker Features
AWS SageMaker includes the following features:
- 1
One-Click feature for Training the models
By using Amazon SageMaker service you can easily specify the location of the data and then mention the type of the SageMaker cases. You can start just with one click.
- 2
Distributed training
You can use this service to efficiently perform distributed training and split data across several GPUs. This results in a near-linear scale of productivity. It automatically distributes the model with fewer than 10 lines of code.
- 3
Automatic Model Tuning of models
AWS SageMaker has the feature of automatic model tuning of machine learning models. It uses a technique that enables it to quickly tune the model.
- 4
Profiling and Debugging Training Runs
Before you may deploy the production model, this feature allows you to capture the metrics as well as profiles training jobs, therefore debugging training runs.
- 5
Spot Training
Due to this feature, AWS SageMaker helps you to reduce training costs by up to 90%. Spot Training keeps the training jobs automatically running when the compute capacity becomes available.
- 6
Reinforcement Learning
Besides traditional supervised and unsupervised learning, AWS SageMaker also supports reinforcement learning. This is another big feature it has.
- 7
Major Frameworks
This service also supports Major Frameworks including TensorFlow, Apache MXNet, and PyTorch. These are always up-to-date frameworks and are optimized for AWS SageMaker performance.
- 8
AutoML
AWS SageMaker Automated machine learning allows you to build, train, and tune the best machine learning models according to your data. You can deploy the model to the production with just one click and give it a command to improve the model quality.
Advantages of using AWS SageMaker
Disadvantages of using AWS SageMaker
Use cases of AWS SageMaker
Now let’s pass on to the main use cases of AWS SageMaker to understand whether you need it or not.
- 1
RStudio Interface
AWS comes with a feature of providing an RStudio interface meaning you can easily launch RStudio with a single click. With the help of this license, developers can dial-up computers without odd and time-consuming interruptions.
- 2
Pre-built solutions
As for pre-built solutions, there are truly good features for open-source models. With the help of pre-built solutions, you can quickly and easily get started with Machine Learning using AWS SageMaker JumpStart.
- 3
AutoML
With the help of AWS SageMaker Auto machine learning features, you can easily build, train and tune ML machines. This Provides direct deployment and helps to improve the model quality.
- 4
Major Frameworks
As mentioned above AWS supports major frameworks and this is the case when you can use various popular frameworks including TensorFlow, Apache MXNet, PyTorch, etc.
- 5
Local Mode
You can test and prototype locally with the help of Apache MXNet and TensorFlow Docker containers. They are available on GitHub and you can download them and use the Python SDK for test scripts.
AWS SageMaker vs Google Cloud ML: Pricing and Support
When it comes to pricing and support, we see several differences between AWS SageMaker and Google Cloud ML.
What is Google Cloud ML
Google Cloud ML is a hosted platform developed to run machine learning training jobs as well as predictions at scale.
You can use Google Cloud ML Engine to learn and train a complex model with the help of GPU and TPU infrastructure. Let’s go into the details and discuss the features of Google Cloud ML.
Google Cloud ML Features
The main features of Google Cloud ML are the following:
- 1
Computing Services
Some of the compute services Google Cloud offers are Compute engine, Google app engine, and Kubernetes engines.
- 2
Networking
The network services Google Cloud ML offers are Cloud Load Balancing, VPC, and Content Delivery network.
- 3
Storage Services
The storage services included in GCML are Cloud SQL, Google Cloud Storage, and Cloud Bigtable.
- 4
Big data
The services related to big data are Google Cloud Datastore, Google Cloud Dataproc, and BigQuery.
- 5
Security and Management tools
Security and management tools include Cloud data loss prevention, Cloud IAM, data management, google cloud console app, google Stackdriver.
- 6
IoT
IoT, in its turn, is the same as the Internet of Things and it includes Cloud IoT Core and Cloud IoT Edge.
- 7
Cloud AI
Cloud AI is all about Cloud Auto machine learning and cloud machine learning engine.
Advantages of using Google Cloud ML
The biggest advantages of using Google Cloud ML are:
Disadvantages of using Google Cloud ML
According to the user reviews the main disadvantages of Google Cloud ML are:
Use Cases of Google Cloud ML
Here are the use cases of Google Cloud ML and if you have decided to use this platform for your business, it’s important to know where and in which circumstances you can use this service.
- 1
Service Deployment
You can deploy models and versions and then create a model resource. After this, you can create a model version.
- 2
Data Storage
You can store your model in Google Cloud Storage and set up your Cloud Storage bucket. Then you can upload the exported model to Cloud Storage and custom code.
- 3
Secure low-level Infrastructure
This is the case when you design and build your own data centers, which unites multiple layers of physical security.
Google Cloud Ml vs AWS SageMaker: Support and pricing
AWS SageMaker vs Google Cloud ML: features, functionality, and ease of use
Amazon SageMaker and Google Cloud ML are the top-rated machine learning platforms that you may choose for your business. They are both secure, powerful, and reliable but each comes with specific features.
So, you need to decide which one suits your business needs. Let’s compare both and find out the differences.
Why AWS SageMaker?
So, why do most companies choose to use AWS SageMaker to build their apps? Perhaps the fact that AWS has quite flexible features and a depth of services, makes it a better place for the team to work.
It offers a wide range of tools, including databases, analytics, and management as well as IoT, security, and enterprise applications.
Why Google Cloud ML?
Companies that choose Google Cloud ML appreciate its limitless internal research. Compared to many other platforms, it has the superpower in the search engine system.
It’s one of the best platforms, especially for startups and companies that give priority to technologies and new approaches.
Conclusion
Although the choice is not that easy, each should do it according to their business requirements. So, before you get started, make sure it provides all the essentials for your company starting from the price to the features and use cases.
It’s important to note that start-ups widely use Google Cloud ML as it’s cheaper and provides the necessary tools for starters. More outstanding and established companies give priority to AWS SageMaker.
