Even though the above diagram is a bit of simplification, this is how most ETL workflows may look like. To put simply, ETL is an automated process to move data from source systems to target systems, involving various stages for Extract, Transform and Load sub-processes, without data-loss and while maintaining data-integrity. This also, is usually referred to as data-migration. The objective of ETL is to have a clean, classified, enriched and curated data at one place (data warehouse or data lake). Machine-learning models and analytic tools are run against this data to fetch useful information and predictions, based on which business decisions can be taken.

Even though the above diagram is a bit of simplification, this is how most ETL workflows may look like. To put it simply, ETL is an automated process to move data from source systems to target systems, involving various stages for Extract, Transform and Load sub-processes, without data-loss and while maintaining data-integrity. This also, is usually referred to as data-migration. The objective of ETL is to have a clean, classified, enriched and curated data at one place (data warehouse or data lake). Machine Learning models and analytic tools are run against this data to fetch useful information and predictions, based on which business decisions can be taken.

Amazon has been introducing many useful tools to make machine learning easier for developers around the world. Continuing this trend of making machine learning more accessible and comfortable, Amazon's AWS launched a brand new approach in adding machine learning predictions to developer's products and processes by directly integrating those predictions with their database. "This announcement is a ball about making it easier for developers to add machine learning predictions to their products and their processes by integrating those predictions directly with their databases," said Matt Wood, VP of artificial intelligence at AWS. It won't be long until all applications have machine learning and artificial intelligence used inside them. It can be challenging to incorporate the machine learning models into your databases, analytics and business intelligence reports.

As artificial intelligence takes off in enterprise settings, so will data center power usage. AI is many things, but power efficient is not one of them. For data centers running typical enterprise applications, the average power consumption for a rack is around 7 kW. Yet it's common for AI applications to use more than 30 kW per rack, according to data center organization AFCOM. That's because AI requires much higher processor utilization, and the processors – especially GPUs – are power hungry.

Microsoft co-founder Bill Gates, Uber co-founder Travis Kalanick's 10100 fund and current Uber CEO Dara Khosrowshahi have invested in Luminous, a small start-up building an artificial intelligence chip. The investment shows key figures in the technology industry believe there is still an opportunity for a new standard to emerge when it comes to hardware for AI, which can be incorporated into a variety of software applications. In all, the company raised $9 million in this seed round. Several start-ups have been working on next-generation hardware in recent years as AI has become trendy. Intel bought one, called Nervana, in 2016.