Machine learning is delivering real profits to more companies by automating decision-making and enhancing digital products. Yet the flood of data pouring into these statistical prediction systems has created another problem: How to get reams of data ready for powerful computers to analyze amid of a dearth of machine learning expertise.<\/p>\n
Twenty-seven percent of companies\u00a0surveyed by McKinsey & Company<\/a>\u00a0last year reported that at least 5% of their operating profit was attributable to artificial intelligence (AI) usage, up from 22% of companies in 2020. In the survey of 1,843 global companies, 56% said they\u2019re using AI in at least one area, chiefly to automate customer service, improve products, segment customers, and model risk.<\/p>\n Yet organizations have their data\u00a0hived away in myriad databases<\/a>\u00a0with different departments using different approaches. Ferrying information to machine learning systems and putting it in a form that\u2019s ready to analyze adds cost and time drag. \u201cAI needs data, and for enterprises, moving data around is cumbersome, slow, and expensive,\u201d says\u00a0Holger Mueller<\/a>, vice president and principal analyst at tech advisory firm Constellation Research.<\/p>\n The latest version of\u00a0Oracle MySQL<\/a>\u00a0offers a better approach: Build machine learning models into the database, so you can leave the data where it is. \u201cBringing the machine learning to the data is a smart architecture and design choice,\u201d Mueller says.<\/p>\n First released two years ago, MySQL HeatWave runs as a service on Oracle Cloud Infrastructure and lets users of the widely adopted MySQL Database combine transaction processing and data analysis in a single database. It runs database queries in computer memory to\u00a0increase their speed<\/a>.<\/p>\n The latest iteration of the software puts\u00a0machine learning capabilities<\/a>\u00a0inside the MySQL HeatWave Database and can automate the process of creating and training ML models, then unleashing them on live data. MySQL HeatWave can train models on available data and tune them to improve accuracy based on how well they predict outcomes. That means when organizations\u2019 data is inside MySQL Database, as much of it already is, they can skip the work of extracting it, changing its format, and loading it into another ML system\u2014a process that can take hours. Developers and data analysts can also build ML models using the familiar database SQL language without having to learn new tools or languages.<\/p>\n \u201cHeatWave is easy to use, period,\u201d says\u00a0Matt Kimball<\/a>, an analyst at Moor Insights & Strategy. \u201cThe biggest barrier to adoption in the world of ML is complexity\u2014organizations can\u2019t find the right talent and the best hardware. With HeatWave, they can realize the promise of machine learning more quickly.\u201d<\/p>\n Sales of cloud data warehouses are expected to\u00a0grow sharply this year\u00a0<\/a>as businesses use them for tasks, such as managing supply chains, hunting for sales opportunities, or analyzing medical data. The systems tap databases, the web, business applications, and sensors to gather data in one place, analyze it, and give companies a clearer view of operations, sales, and customer behavior.<\/p>\n kehanet\u00a0published machine learning benchmarks\u00a0<\/a>performed across a large number of publicly available machine learning datasets using different cloud providers\u2014research noted by several analysts as an industry first. On average, on the smallest cluster, MySQL HeatWave ML-trained machine learning models were 25 times faster at 1% of the cost of Amazon Web Services\u2019 (AWS) Redshift ML. The benchmarks based on public ML datasets show MySQL HeatWave delivering better price performance than Google\u2019s BigQuery, Microsoft Azure Synapse, and Snowflake, as well as AWS\u2019s Redshift with its query accelerator.<\/p>\n Less time spent using a cloud service can mean lower computing bills, and the new MySQL HeatWave version also lets customers scale up without downtime.<\/p>\n Last year, Oracle\u00a0introduced software called My SQL Autopilot<\/a>\u00a0for using AI to optimize and automate the MySQL HeatWave service. \u201cAutopilot used machine learning to operate the database. Now it\u2019s about using ML on the data,\u201d says Mueller.<\/p>\n MySQL HeatWave can also take models tuned in MySQL and use them to answer queries from popular machine learning notebook services for code and data, such as\u00a0Jupyter\u00a0<\/a>ve\u00a0Apache Zeppelin<\/a>.<\/p>\n Oracle plans to release MySQL HeatWave to run on other cloud providers\u2019 infrastructure, including AWS, Oracle Executive Chairman and Chief Technology Officer Larry Ellison said during a conference call with financial analysts last month.<\/p>\n Edward Screven, Oracle\u2019s chief corporate architect, said during an\u00a0online presentation<\/a>\u00a0that companies are finding it hard to achieve their machine learning ambitions. \u201cIf you use conventional tools, if you use what comes out of the box from most vendors, from most clouds, machine learning is hard, and it’s expensive,\u201d he said. In contrast, \u201cyou don\u2019t need to be an expert to use machine learning with your data stored in MySQL HeatWave.\u201d<\/p>\n The new capabilities are available at no extra charge for MySQL HeatWave customers in all of Oracle\u2019s global cloud regions. Oracle is also making both the ML and\u00a0TPC-DS benchmark<\/a>\u00a0tests public for customers to\u00a0perform themselves<\/a>. \u201cThat really lends credibility to the price and price-performance claims,\u201d says Moore Insights\u2019 Kimball.<\/p>\nMachine learning built in<\/h2>\n