Amazon Web Services unveils RedShift ML

For the successful working of modern day businesses, it is important to divert the focus on choosing the right cloud platform. The cloud system can ensure appropriate content delivery, it can synchronize data, it keeps the data secured and enables faster decision making for the business enterprise.

 

Amazon Web Services(AWS) is one of the best cloud service providers. AWS has recently unveiled RedShift ML, it will enable developers to train ML models through SQL. 

 

Through RedShift developers can create, train and deploy machine learning models from Amazon RedShift cluster space. RedShift will make use of SQL queries. It will compile the SQL query with exabyte and semi- structured data across enterprises data warehouse and operational database.

 

Along with RedShift developers can employ AQUA (Advanced Query Accelerator) to improve the performance upto 10 times. AQUA will make no changes in the code; it will only polish the SQL query retrieval process. AQUA is also generally available. 

 

Previously to train a ML model for data processing developers were required to export the training data from Amazon RedShift to Amazon Simple Storage Service (S3) bucket. After the export developers were asked to configure to begin the training process.

 

To create a Machine Learning model in RedShift developers can use a simple SQL query to specify the data(that will be used for the training model) and the output value to be predicted. With a single query RedShift can do a lot. 

 

After running the SQL command that will be used for creating the model RedShift ML will export the data to Amazon Sage Maker. Further developers will be asked to select the appropriate pre- built algorithm , the pre- built algorithm can be applied for specific model’s training. 

 

(Recommended blog: Understanding Amazon Web Services)

 

When the model is properly trained RedShift employs  Amazon Sage Maker Neo, to optimize the model for classification it further makes the model available as an SQL function. Customers can also RedShift to make simple SQL product queries. 

 

(Must check: SQL vs NoSQL)

 

Features of RedShift

 

• It is easy to import a Sagemaker model into the Amazon RedShift cluster.

 

• Simple SQL functions that use Sagemaker endpoints to make predictions can be created in RedShift. 

 

Business enterprises can use simple SQL queries for multiple purposes. For example, prior to a product launch to create a model that showcases the previous product success rate input is defined by selecting all the previous product categories and their respective sales, the output column that organization wants to predict. 

 

According to VentureBeat Amazon Redshift has the largest number of Cloud data warehouse deployments, with more than 6,500 to date.