Streamline Machine Learning Development with AWS SageMaker

AWS SageMaker is one of the leading end-to-end machine learning platforms used currently. With its significant advantages, it does make sense to consider it for building high performing ML models

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.

• The Rise of Machine Learning: Machine learning (ML) has become a transformative force across industries. Organizations are leveraging its power to unlock new possibilities, from product development to customer experience optimization. This surge in demand has fueled the creation of a robust ecosystem of ML tools and platforms.
• Navigating the ML Platform Landscape: Choosing the right platform is crucial for a successful ML journey. AWS SageMaker stands out as a leader due to its comprehensive capabilities:
• Simplified End-to-End Workflow: SageMaker streamlines the entire ML lifecycle, encompassing data preparation, model building, training, deployment, and management. This allows data scientists and developers to focus on core tasks without getting bogged down in infrastructure complexities.
• High-Performance Model Building: SageMaker offers a rich library of pre-built algorithms and frameworks, enabling the creation of powerful and accurate models. Additionally, features like AutoML facilitate efficient hyperparameter tuning, reducing development time.
• Scalability and Cost-Effectiveness: SageMaker leverages the power of the cloud, providing access to a vast array of computing resources. This allows seamless scaling based on project needs, while the pay-as-you-go model ensures cost optimization.
• Addressing Specific Needs: While SageMaker offers a comprehensive solution, it’s crucial to identify your organization’s specific requirements. Whether it’s building robust data pipelines, training complex models, or ensuring rapid deployment, SageMaker’s modular design caters to diverse needs.

Call to Action: Take the next step towards building high-performing ML models. Explore how AWS SageMaker can empower your organization’s journey.

AWS SAGEMAKER ML MODELS

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, AWSSAGEMAKER 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 and Algorithms:

• Limited Built-in Library: SageMaker itself doesn’t possess a vast library of algorithms like traditional ML frameworks (e.g., TensorFlow, PyTorch).
• Algorithm Integration: Its strength lies in seamless integration with various algorithm sources:
• Pre-built Containers: Users can bring their own algorithms packaged in containers compatible with SageMaker. This allows leveraging custom algorithms or those from popular open-source libraries.
• Amazon SageMaker Marketplace: This marketplace offers pre-trained models and algorithms from various vendors, covering a wide range of tasks like image recognition, natural language processing, and more.
• Focus on Manageability: SageMaker simplifies the process of using these algorithms by providing tools for:
• Deployment: Effortlessly deploying models trained with these algorithms.
• Management: Managing and monitoring models in production.

Key Points:

• SageMaker excels in integrating and managing algorithms rather than offering a comprehensive built-in library.
• Users have the flexibility to leverage various sources for their algorithmic needs.
• Pre-trained models from the SageMaker Marketplace can serve as a starting point for specific tasks.

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.

AWS CLOUD AND JUPYTER NOTEBOOK FOR AWS SAGEMAKER

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.

Advantages of Using AWS SageMaker

• Productivity: It allows the user to deploy and manage efficiently thereby reducing the number of delays in working and increasing productivity.
• Scalability: AWS SageMaker is highly scalable and allows users to scale up or down as per requirements. It also promotes faster model training.
• Storage: Working with ML models can get storage-intensive pretty quickly. However, AWS SageMaker allows you suitable storage to help with this problem. Now, you can store all necessary ML models and components in one place.
• Cost: AWS SageMakers reduces the costs of building and deploying ML models by up to 70%.
• Time Efficient: It helps to create and manage Ec2 compute instances in a time-efficient manner.
• Continuous Deployment: AWS Sagemaker will analyze the raw data and create, deploy and train a model automatically with open and absolute visibility.
• Reduces Labeling Tasks: It helps to reduce the overall time which is required for the various data labeling tasks.

Machine Learning possibilities with AWS SageMaker

SageMaker Has Its Shortcomings

With all the above-mentioned advantages, AWS SageMaker is still not perfect and comes with its cons.

1. With SageMaker, you will have to still code your data engineering needs, whereas other platforms may offer a no-code(drag and drop) solution
2. Configuring the preprocessing and post-processing pipelines and the Docker containers for endpoints with multiple models is a tedious task
3. AWS documentation is not very easy to get around

Conclusion

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