Machine learning often feels a lot harder than it should be to most developers because the
process to build and train models, and then deploy them into production is too complicated
and too slow. First, you need to collect and prepare your training data to discover which
elements of your data set are important. Then, you need to select which algorithm and
framework you’ll use. After deciding on your approach, you need to teach the model how
to make predictions by training, which requires a lot of compute. Then, you need to tune
the model so it delivers the best possible predictions, which is often a tedious and
After you’ve developed a fully trained model, you need to integrate the model with your application
and deploy this application on infrastructure that will scale. All of this takes a lot
of specialized expertise, access to large amounts of compute and storage, and a lot of
time to experiment and optimize every part of the process. In the end, it's not a surprise
that the whole thing feels out of reach for most developers.
Amazon SageMaker is a fully-managed platform that enables developers and data scientists
to quickly and easily build, train, and deploy machine learning models at any scale.
Amazon SageMaker removes all the barriers that typically slow down developers who want
to use machine learning.
Auxenta is now in the process of building a scalable Machine Learning platform for a large
Sillicon Valley producer, leveraging AWS Sagemaker Machine Learning platform along with
some of the AWS and open source big data platforms such as AWS EMR, Spark, Tensorflow,
SciKit, Apache Atlas and Apache Livy.