Oracle Machine Learning

There is so much data available today. But it only makes a difference when you transform that data into actionable intelligence.   In this episode, hosts Lois Houston and Nikita Abraham, along with Nick Commisso, discuss how you can harness the capabilities of Oracle Machine Learning to solve key business problems and accelerate the deployment of machine learning–based solutions.   Oracle MyLearn: https://mylearn.oracle.com/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ Twitter: https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Ranbir Singh, and the OU Studio Team for helping us create this episode.   -------------------------------------------------------   Episode Transcript:   00;00;00;00 - 00;00;39;06 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started. Hello and welcome to the Oracle University Podcast. I'm Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Product Innovation and Go to Market Programs.   00;00;39;08 - 00;01;07;15 Hi there. For the last two weeks, we've been off the Oracle Database train, but today we're back on it, focusing on Oracle Machine Learning with our Cloud Engineer Nick Commisso. Hi Nick. Before we get into Oracle Machine Learning, I think we should start with the basics. What is machine learning? Machine learning is focused on enabling data science teams to add ML-based intelligence to both their applications and their dashboards.   00;01;07;17 - 00;01;37;07 With machine learning, we can automate the entire data analysis process workflow, from collaborating in order to obtain data from many sources to creating and analyzing the data, and showing the results and reports. We can perform predictions and easily visualize the data to provide a deeper and faster and more comprehensive insight to enable effective business decisions. I think we can safely say that machine learning is the future of analytics in every industry, right?   00;01;37;09 - 00;02;11;26 So where does Oracle Machine Learning come in? Oracle Machine Learning provides a reliable, AI-driven environment that truly encapsulates the power of machine learning. Enhanced performance and scalability is achieved in part by eliminating data movement for database data and providing algorithms that have been redesigned specifically for scalability and performance. Next is simpler solution architecture and management, where we want to avoid requiring separately maintained analytic engines or tools for data and model governance.   00;02;11;26 - 00;02;38;10 In-database machine learning also offers flexible architectures for deployment tests, in-production spanning the cloud, on-premises, and hybrid environments. And because of its SQL and REST interfaces, it's easy to integrate with the broader Oracle stack. Now the third is that OML empowers a broader range of users with machine learning. It's readily available in the database from multiple interfaces, including third-party package support.   00;02;38;13 - 00;03;06;19 So do you have to be an expert to use machine learning? To make even non-experts productive with machine learning, OML supports AutoML from a Python API, and a no code user interface. And there's also other built-in automation features like automatic data preparation, integrated text mining, and partition models. And these make machine learning even more accessible to a broader range of users.   00;03;06;22 - 00;03;33;04 What can you tell us about the pricing structure? Machine learning capabilities are included in the core product at no additional cost with Autonomous Database, and the OML components of ADB are pre-provisioned and ready to use. And an on-premises database is included with the database license. So overall, the takeaway is that OML helps reduce costs and complexity while increasing productivity and access.   00;03;33;06 - 00;04;01;06 What are the areas or fields in which OML is useful? Modern businesses and modern problems require solution best delivered by Oracle Machine Learning. Medical science has been leveraging machine learning successfully to perform quick and accurate diagnosis or creating curative solutions using vast quantities of data. Physical robots use a combination of machine learning solutions to sense their environment and respond appropriately.   00;04;01;08 - 00;04;37;07 Computational biology makes use of machine learning to analyze biological data, such as genetic sequences or organic samples, and make predictions. Analysis with financial or security data can identify clients with high risk profiles or cybersecurity surveillance to pinpoint warning signs of fraud. The recent growth in the popularity of machine learning has been aided by the fact that we now have improved machine learning algorithms, which are supported by the advent and frequent innovation in technology related to data capture, networking and computing power.   00;04;37;11 - 00;05;02;24 So you basically don't need to write complex software for every change in the data. And the machine learning model evolves as the historical data evolves. We have more advanced sensors and I/O devices which support machine learning models with accurate and real-time data. Customers of various services are now looking for more customization options, which can be efficiently supported with machine learning solutions.   00;05;02;26 - 00;05;24;16 The historical challenges of manually trawling through data to extract actionable knowledge is no longer a problem now because machine learning algorithms supported by powerful modern computers are designed for that particular purpose.   00;05;24;19 - 00;05;52;18 Are you attending Oracle CloudWorld 2023? Learn from experts, network with peers, and find out about the latest innovations when Oracle CloudWorld returns to Las Vegas from September 18 through 21. CloudWorld is the best place to learn about Oracle solutions from the people who build and use them. In addition to your attendance at CloudWorld, your ticket gives you access to Oracle MyLearn and all of the cloud learning subscription content, as well as three free certification exam credits.   00;05;52;23 - 00;06;09;20 This is valid from the week you register through 60 days after the conference. So what are you waiting for? Register today. Learn more about Oracle CloudWorld at www.oracle.com/cloudworld.   00;06;09;22 - 00;06;39;21 Welcome back! Nick, I was hoping you could share some use cases where machine learning can really be leveraged. Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights and data and to prevent fraud. The insights can identify investment opportunities to help investors know when to trade, and machine learning can also identify clients with high risk profiles or use cyber surveillance to pinpoint warning signs of fraud.   00;06;39;23 - 00;07;10;23 Machine learning is a fast growing trend in the healthcare industry. The technology can help medical experts analyze data to identify trends or red flags that may lead to improved diagnostics and treatment. Finding new energy sources, analyzing minerals in the ground, predicting refinery sensor failure, streamlining oil distribution to make it more efficient and cost effective. The number of machine learning use cases for this industry is fast and still expanding.   00;07;10;25 - 00;07;46;27 Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation, and other transportation organizations. Shopping websites also use machine learning, right? Websites recommending items you might like based on previous purchases are used with machine learning to analyze your buying history and promote other items you might be interested in.   00;07;46;29 - 00;08;14;17 The ability to capture that data and analyze it and use it to personalize a shopping experience or implement a marketing campaign is the future of retail. Government agencies, such as public safety and utilities, have a particular need for machine learning because they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money.   00;08;14;20 - 00;08;42;12 Machine learning can also help detect fraud and minimize theft. Retail industries can use machine learning to recognize customer spending patterns for targeted marketing or optimize supply chain logistics by recognizing outliers or anomalies in the data. All that a data science needs to do is identify the problem domains, such as transportation, find the data, and let Oracle Machine Learning take care of the rest.   00;08;42;14 - 00;09;08;18 GPS navigation services make use of historical data to predict travel time based on the current traffic levels. Video surveillance systems uses facial recognition systems to identify situations which require attention from emergency services. Social media uses machine learning to study the patterns of user interactions to suggest connections, item of interests, targeted ads, and so on. And we can use it to find spam, I'm sure.   00;09;08;20 - 00;09;33;27 Machine learning helps email services recognize spam or malicious emails by recognizing the common patterns among offending examples. And the well-known and almost essential Internet searches use machine learning to refine results based on the search patterns of the individual users. Nick, now that you've given us a really good idea about all of the places machine learning can be used, let's talk about the features of Oracle Machine Learning.   00;09;34;00 - 00;10;09;12 Oracle Machine Learning provides access to a wide array of features in addition to the collaborative notebooks, which include templates, user administration tools, and schedulers. In-database algorithms allow you to implement machine learning solutions on your data residing in Oracle databases without having to move your data anywhere else. OML provides support for SQL, PL/SQL, R, Python languages, and Markdown, which you should be familiar with if you've worked with databases before, making implementing machine learning solutions lot easier.   00;10;09;15 - 00;10;38;04 OML also provides support for the deployment of enterprise machine learning methodologies within the Autonomous Data Warehouse. What are the different parts of Oracle Machine Learning? The components that make up Oracle Machine Learning are the machine learning user administrative application, which is a web-based user interface for managing your Oracle Machine Learning user, as well as mapping your machine learning to the Autonomous Data Warehouse database users.   00;10;38;07 - 00;11;03;22 Now you can also access machine learning user interface for the administrator. The OML application is a web-based application for your data scientists to help create workspaces and projects, as well as notebooks. Earlier in our conversation, you spoke about these powerful machine learning algorithms. Can you tell us more about that, please? The OML tagline is move the algorithms, not the data.   00;11;03;25 - 00;11;34;26 To realize this, we’ve placed powerful machine learning algorithms in the database kernel software operating below the user security layer. Other tools simply can't do that. OML eliminates data movement for database data and simplifies the solution architecture as there's no need to manage and test workflows involving third-party engines. OML extends the database to enable users to augment applications and dashboards with machine learning– based intelligence quickly and easily.   00;11;34;28 - 00;12;05;14 It delivers over 30 in-database algorithms accessible through multiple language interfaces, and it's important to note that the broader Oracle ecosystem for data analytics and machine learning also include tools like Oracle Analytics Server and Analytics Cloud, OCI, Data Science, AI services, and others. And OML is included with Oracle Autonomous Database instances and Oracle Database licenses. So you already have free access to it to start using it.   00;12;05;18 - 00;12;33;25 And what are the benefits of using OML, Nick? Whether minimizing or eliminating data movement, support from multiple personas or multiple languages and both code and no code interfaces. These and other benefits resonate with customers needing powerful and integrated machine learning to meet their scalability and performance needs, while simplifying their solution and deployment architecture. What are the various OML components?   00;12;33;29 - 00;13;19;07 Build ML models and score data with no data movement with the OML4SQL API. Leverage the database as a high-performance compute engine from Python with in-database ML with OML4Py API. Leverage the database as a high-performance compute engine from R with in-database ML with OML4R API. OML Notebooks is a collaborative notebook user interface supporting SQL, PL/SQL, Python, R, and Markdown. OML AutoML UI is a no-code automated modeling interface. And OML Services is a RESTful model management and deployment.   00;13;19;09 - 00;13;44;19 With Oracle Data Miner, there's a SQL Developer extension with a drag-and-drop interface for creating ML methodologies. Let's talk about the life cycle of a machine learning project. The life cycle of a machine learning project is divided into six phases. The first phase of the machine learning process is to define business objectives. The initial phase of the project focuses on understanding the project objectives and requirements.   00;13;44;22 - 00;14;10;28 In this phase, you're going to specify the objectives, determine the machine learning goals, define success criteria, and produce a project plan. The data understanding phase involves data collection and exploration, which includes loading the data and analyzing the data for your business problem. In this phase, you will access and collect the data, explore data, and understand data quality. Alright, then.   00;14;10;28 - 00;14;40;22 So what's next? The preparation phase involves finalizing the data and covers all of the tasks involved in making the data in a format that you can use to build the model. In this phase, you will clean, join, and select the data, transform data, and engineer new features. In the modeling phase, you'll select and apply various modeling techniques and tune the algorithm parameters called hyperparameters to your desired values.   00;14;40;24 - 00;15;07;20 In this phase, you're going to explore different algorithms and build, evaluate, and tune models. At the evaluation phase, it's time to evaluate how well the model satisfies the originally stated business goal. In this phase, you'll review the business objectives, assess results against success criteria, and determine the next steps. Deployment is the use of machine learning with a targeted environment.   00;15;07;22 - 00;15;42;04 In the deployment phase, one can derive data-driven insights and actionable insights. In this phase, you will plan enterprise deployment, integrate models with application for business needs, monitor, refresh, retire, and archive models, and you'll report on model effectiveness. Thank you so much, Nick, for sharing your expertise with us. This was great. To learn more about Oracle Machine Learning, please visit mylearn.oracle.com and take a look at our Using Oracle Machine Learning with Autonomous Database course.   00;15;42;06 - 00;16;07;19 Once you're done with it, you can take the associated specialist certification exam with confidence. That brings us to the end of this episode. Next week, we'll talk about MySQL and why it's everywhere. Until then, this is Nikita Abraham and Lois Houston signing off. That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes.   00;16;07;22 - 00;18;40;10 We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.