Machine learning (ML) and artificial intelligence (AI) provide decision support and automation to increase product quality, whilst lowering costs. Machine learning is an artificial intelligence technology that enables applications to learn without being explicitly programmed, and become smarter based on the frequency and volume of new data they ingest and analyze.
Developers must recognize and evaluate opportunities where ML/AI will make a significant improvement. Today's standard cognitive APIs offered by companies as Amazon, Google, IBM and Microsoft give developers a gist of how ML/AI could impact productivity and product quality output by covering simple, standard scenarios, such as voice to text, language translation, object or facial recognition and image classification. However, once the requirements exceed the capabilities of these standard cognitive APIs, the next step is to trigger a dedicated DevOps machine learning or AI environment, with all the resources and deployment complexity that comes along.
Amazon launched SageMaker to lower the barriers of entry for ML/AI. SageMaker helps train developers to enable faster and cheaper DevOps machine learning experimentation and pilot projects. Developers will be more willing to engage with a project if the proof of concept takes an hour or two to create. Here, Amazon SageMaker comes in to picture.
SageMaker is a fully managed service for developers and data scientists who wish to build, train and manage their own machine learning models. Developers can choose among ten of the most common deep learning algorithms, specify their data source, and the tool installs and configures the underlying drivers and frameworks.
SageMaker provides a machine learning process template that is composed of numerous Amazon Elastic Compute Cloud (EC2) services. It lowers the barriers of experimentation by addressing some of the ML/AI pain that full-stack developers typically experience.
Developers need a certain level of experience to set model hyperparameters that, for example, determine the number of layers of a neural network, the number of iterations the algorithm should take and the maximum of how each sample record should change the model's values.
SageMaker is rolling out autoconfiguration for hyperparameters, where the AI service tests the model with different configurations and selects the one that fits best.
Overfitting ML/AI models is a situation wherein the model loses predictive power because the training effort accommodates many cases. SageMaker offers some protection against overfitting but cannot fully prevent the problem.
While SageMaker gives full-stack developers a chance to benefit from ML/AI, there are also some limitations to it. Developers still need to dive into the DevOps machine learning mindset and spend time and resources to experiment with ML/AI. They need to nurture the ability to identify ML/AI use cases.
Lastly, Amazon's Sagemaker services are changing the way data is stored, processed and trained these days. With a variety of algorithms in place, developers can wet their hands with the various concepts of Machine Learning, allowing them to understand what actually goes on behind the scenes. All this can be achieved without bothering about the algorithm preparations and logic creation. An ideal solution for companies which are looking forward to help their developers focus more on drawing analysis from tons and tons of data.