Integrating AI with SaaS-based Cloud Data Warehouses

1. Why: Overview and Value

Development History of AI

Artificial intelligence (AI) is a concept that emerged as early as the 1950s. After that, due to various reasons, AI experienced a long process of dormancy for decades. It is not until the last few years that AI became popular again. In fact, AI has actually enjoyed three “golden periods” of development in its history.

Reasons for MaxCompute Integrated with AI

Ten trends in data and analytics are predicted by Gartner as follows.

  • Fast data access speed with data finding by algorithms.
  • High scalability.
  • Easier to use by pure SQLML or Python.

Existing AI Capabilities of MaxCompute

The product features of MaxCompute have been covered previously and will not be described in detail here. Here, it introduces the main capabilities of, MaxCompute integrated with AI.

  • It uses scientific computing and third-party machine learning libraries of Python through Mars.
  • It allows users to perform intelligent analysis by using Spark-ML.
  • It seamlessly integrated with PAI, a machine learning platform, to provide powerful machine learning capabilities.

2. What: Capabilities and Applications

The name of this project is Mars. At the beginning, it meant matrix and array. Now it is no longer limited to them. Since the data dimensions of the project can be very high, Alibaba Cloud has set off for a higher goal and constantly challenge itself.

  • Traditional scientific computing is mostly based on stand-alone computers, while large-scale scientific computing needs to use supercomputers, which is beyond the capabilities of ordinary people.
  • The traditional SQL model is inefficient in scientific computing because it can only perform some simple scientific computations with low efficiency, such as matrix transposition.
  • Currently, R and Python are based on stand-alone devices with weak distributed scaling.

3. How: Best Practices

The following is a simple demo of SQLML.

Original Source:

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