In this day and age, storage is cheap, causing organizations to keep large amounts of data that are product specific, client-related, HR-related, financial, etc. The list goes on and on. Unfortunately, creating actionable insights from data is difficult. Applying the skills required for Analytics and Machine Learning (ML) with large amounts of data is not an easy task, and it turns out to be impossible without a reliable platform that can process the data.
How to Integrate AI into Your Product
Amazon Web Services (AWS) provides services to integrate Artificial Intelligence (AI) into organizations. These services, such as Amazon Personalize, Amazon Comprehend and Amazon Polly, allow companies without significant ML experience to ‘inject intelligence’ into their products. These services integrate easily with each other for quick development, are fully-scalable, and have a pay-for-what-you-use pricing model. This is a perfect place for organizations to get started for agile development and scaling with their organic growth.
Amazon SageMaker Makes an Entrance
While the AI services are made for users with minimal knowledge of ML, SageMaker is there for experienced users. AWS launched SageMaker with a vision of bringing AI and ML to the hands of every developer. With Jupyter Notebook support, click-to-train, click-to-deploy, built-in algorithms, automatic hyperparameter tuning, a data labeling platform and additional features coming in on a regular basis – this is not far from the truth.
Why Start Using SageMaker?
If you need more reasons to use SageMaker, it supports industry-standard leading frameworks for Deep Learning, including TensorFlow, PyTorch and mxnet, among others. This reduces the overhead of the notoriously difficult step of setting up an ML environment, and allows data teams to focus on their data instead of the infrastructure. SageMaker can also reduce the costs of ML training by using Spot Instances and specialized hardware. The list of features mentioned here only scratches the surface when it comes to SageMaker’s capabilities and how it can enable quick innovation within an organization.
AllCloud Chooses SageMaker
Here at AllCloud, we have the AWS expertise, Data Engineering experience and Machine Learning know-how to help customers produce real value and actionable insights from their data. We use SageMaker as our #1 platform of choice for everything Machine Learning related.
Bottom Line for Customers
If you want to integrate AI and ML into your organization, the go-to platform for this on the cloud is SageMaker because of its pace of innovation, built-in support for industry-standard technologies and a variety of cost-reducing strategies. To stay ahead of the curve, AllCloud’s data team is well equipped to get you started on your Machine Learning Journey. Get in touch!