Liwaiwai Liwaiwai
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
    • Architecture
    • Design
    • Software
    • Hybrid Cloud
    • Data
  • Learning
  • About
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
    • Architecture
    • Design
    • Software
    • Hybrid Cloud
    • Data
  • Learning
  • About
Liwaiwai Liwaiwai
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
    • Architecture
    • Design
    • Software
    • Hybrid Cloud
    • Data
  • Learning
  • About
  • Machine Learning

Oracle Announces MySQL HeatWave ML—The Easiest, Fastest, And Least Expensive Way for Developers To Add Powerful Machine Learning Capabilities To Their MySQL Applications

  • April 2, 2022
  • liwaiwai.com

Oracle announced that Oracle MySQL HeatWave now supports in-database machine learning (ML) in addition to the previously available transaction processing and analytics—the only MySQL cloud database service to do so. MySQL HeatWave ML fully automates the ML lifecycle and stores all trained models inside the MySQL database, eliminating the need to move data or the model to a machine learning tool or service. Eliminating ETL reduces application complexity, lowers cost, and improves security of both the data and the model. HeatWave ML is included with the MySQL HeatWave database cloud service in all 37 Oracle Cloud Infrastructure (OCI) regions.

Until now, adding machine learning capabilities to MySQL applications has been prohibitively difficult and time consuming for many developers. First, there is the process of extracting data out of the database and into another system to create and deploy ML models. This approach creates multiple silos for applying machine learning to application data and introduces latency as data moves around. It also leads to the proliferation of data out of the database, making it more vulnerable to security threats, and adds complexity for developers to program in multiple environments. Second, existing services expect developers to be experts in guiding the ML model training process; otherwise, the model is sub-optimal, which degrades the accuracy of predictions. Finally, most existing ML solutions don’t include functionality to provide explanations about why the models that developers build deliver specific predictions.


Partner with liwaiwai.com
for your next big idea.
Let us know here.



From our partners:

CITI.IO :: Business. Institutions. Society. Global Political Economy.
CYBERPOGO.COM :: For the Arts, Sciences, and Technology.
DADAHACKS.COM :: Parenting For The Rest Of Us.
ZEDISTA.COM :: Entertainment. Sports. Culture. Escape.
TAKUMAKU.COM :: For The Hearth And Home.
ASTER.CLOUD :: From The Cloud And Beyond.
LIWAIWAI.COM :: Intelligence, Inside and Outside.
GLOBALCLOUDPLATFORMS.COM :: For The World's Computing Needs.
FIREGULAMAN.COM :: For The Fire In The Belly Of The Coder.
ASTERCASTER.COM :: Supra Astra. Beyond The Stars.
BARTDAY.COM :: Prosperity For Everyone.

MySQL HeatWave ML solves these problems by natively integrating machine learning capabilities inside the MySQL database, eliminating the need to ETL the data to another service. HeatWave ML fully automates the training process and creates a model with the best algorithm, optimal features, and the optimal hyper-parameters for a given data set and a specified task. All models generated by HeatWave ML can provide model and prediction explanations.

No other cloud database vendor provides such advanced ML capabilities directly inside their database service. Oracle published ML benchmarks performed across a large number of publicly available machine learning classification and regression datasets such as Numerai, Nomao, and Bank Marketing, among others. On average, on the smallest cluster, HeatWave ML trains machine learning models 25 times faster at one percent of the cost of Redshift ML. Additionally, the performance advantage over Redshift ML increases when training is done on a larger HeatWave cluster. Training is a time-consuming process and since it can be done very efficiently and rapidly with MySQL HeatWave, customers can now retrain their models more often and keep up with changes to data. This keeps the models up-to-date and improves the accuracy of predictions.

Read More  MosaicML Trains Generative AI Models Faster With Oracle

“Just as we integrated analytics and transaction processing within a single database, we are now bringing machine learning inside MySQL HeatWave,” said Edward Screven, chief corporate architect, Oracle. “MySQL HeatWave is one of the fastest growing cloud services at Oracle. An increasing number of customers have migrated from Amazon and other cloud database services to MySQL HeatWave and have gained significant performance improvements and lower costs. Today, we are also announcing a number of other innovations which enrich HeatWave’s capabilities, improve availability, and lower the cost. Our new and fully transparent benchmark results again demonstrate that Snowflake, AWS, Microsoft, and Google are slower and more expensive than MSQL HeatWave by a large margin.”

HeatWave ML offers the following capabilities compared to other cloud database services:

Fully Automated Model Training: All of the different stages in creating a model with HeatWave ML are fully automated and do not require any intervention from developers. This results in a tuned model which is more accurate, requires no manual work, and the training process is always completed. Other cloud database services such as Amazon Redshift provide integration with machine learning capabilities in external services, which require extensive manual inputs from developers during the ML training process.

Model and Inference Explanations: Model explainability helps developers understand the behavior of a machine learning model. For example, if a bank denies a client a loan, the bank needs to be able to determine which parameters of the model were taken into account, or if the model contains any bias. Prediction explainability is a set of techniques that help answer the question of why a machine learning model made a specific prediction. Prediction explanations are becoming increasingly important these days as companies must be able to explain the decisions made by their machine learning models. HeatWave ML integrates both model explanation and prediction explanations as a part of its model training process. As a result, all models created by HeatWave ML can offer model as well as inference explanations without the need of training data at inference explanation time. Oracle has augmented existing explanation techniques to improve performance, interpretability, and quality. Other cloud database services do not offer such rich explainability for all of their machine learning models.

Hyper-Parameter Tuning: HeatWave ML implements a new gradient search-based reduction algorithm for hyper-parameter tuning. This enables the hyper-parameter search to be executed in parallel without compromising the model accuracy. Hyper-parameter tuning is the most time-consuming stage of ML model training, and this unique capability provides HeatWave ML with a significant performance advantage over other cloud services for building machine learning models.

Algorithm Selection: HeatWave ML uses the notion of proxy models—which are simple models exhibiting the properties of a full complex model—to determine the best ML algorithm for training. Using a simple proxy model, algorithm selection is done very efficiently without loss of accuracy. No other database services for building machine learning models have this proxy modeling capability.

Read More  Coop Reduces Food Waste By Forecasting With Google’s AI And Data Cloud

Intelligent Data Sampling: During model training, HeatWave ML samples a small percentage of the data in order to improve performance. This sampling is done in such a manner that all representative data points are captured in the sample data set. Other cloud services for building machine learning models take a less efficient approach—using random data sampling—which samples a small percentage of data without considering the data distribution characteristics.

Feature Selection: Feature selection helps determine the attributes of the training data which influence the machine learning model behavior for making predictions. The techniques in HeatWave ML for feature selection have been trained over a broad swath of data sets across multiple domains and applications. From these gathered statistics and meta information, HeatWave ML is able to efficiently identify the relevant features in a new data set.

In addition to machine learning capabilities, Oracle released more innovations to the MySQL HeatWave service. Real-time elasticity enables customers to upsize and downsize their HeatWave cluster to any number of nodes, without any downtime or read-only time, and without the need to manually rebalance the cluster. Also included is data compression, which enables customers to process twice the amount of data per node and lowers costs by nearly 50 percent, while maintaining the same price performance ratio. Finally, a new pause-and-resume function enables customers to pause HeatWave to save costs. Upon resuming, both the data and the statistics needed for MySQL Autopilot are automatically reloaded into HeatWave.

Customer and Partner Momentum on MySQL HeatWave

Astute Business Solutions is a leading Oracle Cloud MSP Partner. “We recently had an opportunity to use the machine learning capabilities of HeatWave ML. We found it very innovative, easy-to-use, very fast and most importantly, it is secure since the data or the model don’t leave the database,” said Arvind Rajan, Co-Founder and CEO of Astute Business Solutions. “We believe that providing in-database machine learning is of significant interest to our clients and will further accelerate the adoption of MySQL HeatWave.”

Estuda.com is an educational SaaS provider for K-12 student testing in Brasil. “MySQL HeatWave improved our complex query performance by 300X for responses in seconds and at 85 percent of the cost compared to Google BigQuery with no code changes. Now we can better deliver real-time analytics at a scale of three million users and continually improve our application to enhance student performance,” said Vitor Freitas, Co-founder and CTO, Estuda.com.

Read More  Global Study: Adoption of AI Will Fundamentally Change The Next Generation Of Finance Leaders

VRGlass is a Brasilian SaaS producer of metaverse apps and equipment for corporate clients. “Motivated by the progress achieved within the Oracle for Startups program, VRGlass migrated all application data to MySQL HeatWave from AWS EC2. Within three hours, we achieved a 5X increase in database performance for a virtual event that accommodated more than one million visitors and 1.7 million sessions with greater security and at half the cost,” said Ohmar Tacla, CEO, VRGlass.

Genius Sonority is video game designer, developer, and operator in Japan. “We found MySQL HeatWave improved performance by 90X, which solved all our challenges and concerns we had in moving data to realize real-time analysis. It was a big surprise for us. The extreme performance improvements help us to continually improve the gaming experience for joyful entertainment to customers around the world,” said Masayuki Kawamoto, Director, CTO, Genius Sonority.

Neovera is a trusted provider of managed cybersecurity solutions for more than 20 years. “MySQL HeatWave on OCI increased our query performance by 300X with an 80 percent TCO reduction compared to our on-premises MySQL database environment. Now we can get real-time analytical reporting within our OLTP database to accelerate enhancing our security application,” said Arman Rawls, Sr. Oracle Database Architect, Neovera Inc.

“Oracle announced MySQL HeatWave with Autopilot last August, which may very well have been the single greatest innovation in open source cloud databases in the last 20 years to that point,” said Carl Olofson, Research Vice President, Data Management Software, IDC. “Now Oracle has gone beyond its original unifying of OLTP and OLAP in HeatWave, with MySQL HeatWave ML. Oracle is bringing all of the machine learning processing and models inside the database, so that customers not only avoid managing ML databases apart from the core database, but also eliminate the hassles of ETL, gaining speed, accuracy, and cost-effectiveness in the bargain.”

Additional Resources

  • Watch the Oracle Live with Edward Screven
  • Run your own benchmarks here
  • Watch the MySQL HeatWave explainer video
  • Read the technical blog for more insights, including TPC-DS* benchmarks
  • Read the MySQL HeatWave ML technical white paper

* Benchmark queries are derived from TPC-DS benchmark, but results are not comparable to published TPC-DS benchmark results since they do not comply with TPC-DS specification.

 

About Oracle

Oracle offers integrated suites of applications plus secure, autonomous infrastructure in the Oracle Cloud. For more information about Oracle (NYSE: ORCL), please visit us at www.oracle.com.

Trademarks

Oracle, Java, and MySQL are registered trademarks of Oracle Corporation.


For enquiries, product placements, sponsorships, and collaborations, connect with us at [email protected]. We'd love to hear from you!

Our humans need coffee too! Your support is highly appreciated, thank you!

liwaiwai.com

Related Topics
  • HeatWave ML
  • MySQL HeatWave
  • OCI
  • Oracle
You May Also Like
View Post
  • Artificial Intelligence
  • Machine Learning
  • Technology

IBM And NASA Are Building An AI Foundation Model For Weather And Climate

  • December 4, 2023
View Post
  • Machine Learning
  • Technology

Huawei’s Core Network Autonomous Driving Network (ADN) Ranked No.1 By ABI Research

  • December 1, 2023
Cloud
View Post
  • Artificial Intelligence
  • Machine Learning

DigitalOcean Currents Report Finds That Adoption Of AI/ML, And Investments In Cybersecurity And Multi-Cloud Strategies Are On The Rise At Small Businesses

  • November 20, 2023
View Post
  • Artificial Intelligence
  • Data
  • Machine Learning

The Importance Of Data In Machine Learning: Fueling The AI Revolution

  • November 18, 2023
View Post
  • Artificial Intelligence
  • Machine Learning
  • Platforms

IBM And VMware Help Enterprises Adopt Generative AI With Watsonx On Premises

  • November 14, 2023
View Post
  • Artificial Intelligence
  • Machine Learning
  • Platforms

Azure Sets A Scale Record In Large Language Model Training

  • November 14, 2023
View Post
  • Artificial Intelligence
  • Machine Learning

XGSleeve: Harnessing The Power Of Hidden Markov Models In Shale Oil Production

  • November 5, 2023
View Post
  • Engineering
  • Machine Learning

Five Strategies To Become Top ML Backend Engineer

  • October 27, 2023
A Field Guide To A.I.
Navigate the complexities of Artificial Intelligence and unlock new perspectives in this must-have guide.
Now available in print and ebook.

charity-water



Stay Connected!
LATEST
  • 1
    Here’s What AWS Revealed About Its Generative AI Strategy At re:Invent 2023
    • December 9, 2023
  • Sound 2
    Transforming The Future Of Music Creation
    • December 7, 2023
  • 3
    Bard Gets Its Biggest Upgrade Yet With Gemini
    • December 7, 2023
  • Gemini 4
    Introducing Gemini: Our Largest And Most Capable AI Model
    • December 7, 2023
  • 5
    Members Of Fort Peck Tribes And Googlers Meet To Learn, Celebrate And Support Socially Beneficial Technology
    • December 5, 2023
  • 6
    FAIR Progress And Learnings Across Socially Responsible AI Research
    • December 4, 2023
  • 7
    IBM And NASA Are Building An AI Foundation Model For Weather And Climate
    • December 4, 2023
  • 8
    AI For Impact: How Google Cloud Is Bringing AI To Accelerate Climate Action
    • December 4, 2023
  • 9
    Huawei’s Core Network Autonomous Driving Network (ADN) Ranked No.1 By ABI Research
    • December 1, 2023
  • Birthday Cake 10
    How ChatGPT Altered Our World in Just One Year
    • December 1, 2023

about
About
Hello World!

We are liwaiwai.com. Created by programmers for programmers.

Our site aims to provide materials, guides, programming how-tos, and resources relating to artificial intelligence, machine learning and the likes.

We would like to hear from you.

If you have any questions, enquiries or would like to sponsor content, kindly reach out to us at:

[email protected]

Live long & prosper!
Most Popular
  • 1
    Boehringer Ingelheim And IBM Collaborate To Advance Generative AI And Foundation Models For Therapeutic Antibody Development
    • November 28, 2023
  • 2
    IBM Collaborates with AWS to Launch a New Cloud Database Offering, Enabling Customers to Optimize Data Management for AI Workloads
    • November 27, 2023
  • 3
    Why Student Experiments With Generative AI Matter For Our Collective Learning
    • November 30, 2023
  • OpenAI 4
    Sam Altman Returns As CEO, OpenAI Has A New Initial Board
    • November 30, 2023
  • Oracle | Microsoft 5
    Oracle Cloud Infrastructure Utilized by Microsoft for Bing Conversational Search
    • November 7, 2023
  • /
  • Artificial Intelligence
  • Explore
  • About
  • Contact Us

Input your search keywords and press Enter.