AI will become more industry-specific

According to Elad Ziklik, vice president of product management for AI services and data science, most firms aren’t in the business of constructing AI models. Many firms want a Lego kit, a comprehensive set of all the tools and instructions they need for a given use case, rather than a disjointed collection of construction pieces with no clear instructions, according to Ziklik.

As a result, AI will increasingly gravitate toward domain-specific models in areas such as finance and manufacturing. However, these models need a significant amount of data as well as subject expertise, both of which Oracle possesses.

“Oracle is the only software firm that has a general-purpose cloud platform as well as being a world leader in SaaS apps and domain-specific apps,” he added.

With that, he announced the Oracle Cloud Infrastructure (OCI) AI Services, which would allow businesses to apply pre-trained AI models to their operations without the requirement for in-house AI or machine learning professionals.

Prioritize identifying the business issue

Many presenters at Developer Live agreed that defining the business problem first is critical for your team to discover the proper data and models for your use case.

Suhas Uliyar, vice president of product management, explained, “It starts with the defining of the problem and then obtaining the correct data.”

“Start thinking about what issues you might want to tackle in the future, and how you might start gathering and classifying data today to prepare for them,” said Ian Wilson, IntelligentSuite’s director of engineering.

It is important to prepare the data

More than merely constructing models is required in the machine learning lifecycle. According to an IDC and Oracle analysis, over half of the time spent on AI initiatives is spent integrating and maintaining data rather than doing data science tasks.

“While there is a lot of attention today on algorithms, the many models that are accessible, and the services that are available, I feel that data is a new programming language – that is, you begin to manage the AI model by managing its training data,” Uliyar stated.

To make the greatest use of your data, you must first understand what you have. OCI Data Catalog, a metadata management solution that makes it simpler to identify useful trustworthy data in the company, was discussed by Abhiram Gujjewar, director of product management.

Data science teams must also prepare and process data before it can be used in modeling. OCI Data Integration, a fully managed serverless ETL service, and OCI Data Flow, which helps customers deliver Apache Spark-based applications faster, were highlighted by Carter Shanklin, senior director of product management, and Julien Testut, senior principal product manager, as two solutions for more easily integrating and preparing data for data science.

AutoML speeds up data science work, but it won’t take the position of people

“While a major portion of the machine learning process can be automated,” said Mark Hornick, senior director of data science and machine learning product management, “there are restrictions we face today.”

AutoML, for example, removes repeated activities in model development, allowing teams to enhance productivity while lowering computation costs. Business issue conceptualization and data interpretation, on the other hand, need a human viewpoint; individuals provide domain-specific expertise to these phases and determine success criteria, according to Hornick.

To speed up model training, OCI Data Science includes its AutoML engine as part of the Accelerated Data Science (ADS) toolbox.

“With this AutoML engine, you can effectively take a dataset and spit out a really solid candidate for a model for that data in a very short amount of time with very little work,” Elena Sunshine, director of product management, explained.

AutoML’s job is to complement, not replace, the work of data scientists.

Don’t overlook the use of machine learning

The process isn’t complete once you’ve developed the model; you still need to deploy it and monitor it while it’s in use. Machine learning models are arithmetic functions, according to Marcos Aranciba, product manager for data science and big data, and “model deployment is transforming that math formula into a result.”

He notes many important hurdles for machine learning deployment, including developing a model in a different environment from where it would be deployed and integrating models into end-user applications. In this talk, you’ll learn more about the obstacles of machine learning implementation and how Oracle Machine Learning tackles them.

Data science teams include more than just data scientists

Last but not least, AI isn’t only for data scientists. Anyone who uses AI, from engineers to corporate users, is welcome to participate.

Developers should learn about machine learning principles and methods, according to Hornick. “This will better prepare you to collaborate with your organization’s data science staff,” he stated.

The OCI AI Services, according to Ziklik’s presentation, seek to “empower data science teams to efficiently engage with developers, data engineers, and operators, and provide AI-powered solutions” by delivering pre-trained AI models.

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