Bringing machine learning to more builders through databases and analytics services

Machine learning (ML) is becoming more mainstream, but even with the increasing adoption, it’s still in its infancy. For ML to have the broad impact that we think it can have, it has to get easier to do and easier to apply. We launched Amazon SageMaker in 2017 to remove the challenges from each stage of the ML process, making it radically easier and faster for everyday developers and data scientists to build, train, and deploy ML models. SageMaker has made ML model building and scaling more accessible to more people, but there’s a large group of database developers, data analysts, and business analysts who work with databases and data lakes where much of the data used for ML resides. These users still find it too difficult and involved to extract meaningful insights from that data using ML.

This group is typically proficient in SQL but not Python, and must rely on data scientists to build the models needed to add intelligence to applications or derive predictive insights from data. And even when you have the model in hand, there’s a long and involved process to prepare and move data to use the model. The result is that ML isn’t being used as much as it can be.

To meet the needs of this large and growing group of builders, we’re integrating ML into AWS databases, analytics, and business intelligence (BI) services.

AWS customers generate, process, and collect more data than ever to better understand their business landscape, market, and customers. And you don’t just use one type of data store for all your needs. You typically use several types of databases, data warehouses, and data lakes, to fit your use case. Because all these use cases could benefit from ML, we’re adding ML capabilities to our purpose-built databases and analytics services so that database developers, data analysts, and business analysts can train models on their data or add inference results right from their database, without having to export and process their data or write large amounts of ETL code.

Machine Learning for database developers

At re:Invent last year, we announced ML integrated inside Amazon Aurora for developers working with relational databases. Previously, adding ML using data from Aurora to an application was a very complicated process. First, a data scientist had to build and train a model, then write the code to read data from the database. Next, you had to prepare the data so it can be used by the ML model. Then, you called an ML service to run the model, reformat the output for your application, and finally load it into the application.

Now, with a simple SQL query in Aurora, you can add ML to an enterprise application. When you run an ML query in Aurora using SQL, it can directly access a wide variety of ML models from Amazon SageMaker and Amazon Comprehend. The integration between Aurora and each AWS ML service is optimized, delivering up to 100 times better throughput when compared to moving data between Aurora and SageMaker or Amazon Comprehend without this integration. Because the ML model is deployed separately from the database and the application, each can scale up or scale out independently of the other.

In addition to making ML available in relational databases, combining ML with certain types of non-relational database models can also lead to better predictions. For example, database developers use Amazon Neptune, a purpose-built, high-performance graph database, to store complex relationships between data in a graph data model. You can query these graphs for insights and patterns and apply the results to implement capabilities such as product recommendations or fraud detection.

However, human intuition and analyzing individual queries is not enough to discover the full breadth of insights available from large graphs. ML can help, but as was the case with relational databases it requires you to do a significant amount of heavy lifting upfront to prepare the graph data and then select the best ML model to run against that data. The entire process can take weeks.

To help with this, today we announced the general availability of Amazon Neptune ML to provide database developers access to ML purpose-built for graph data. This integration is powered by SageMaker and uses the Deep Graph Library (DGL), a framework for applying deep learning to graph data. It does the hard work of selecting the graph data needed for ML training, automatically choosing the best model for the selected data, exposing ML capabilities via simple graph queries, and providing templates to allow you to customize ML models for advanced scenarios. The following diagram illustrates this workflow.

And because the DGL is purpose-built to run deep learning on graph data, you can improve accuracy of most predictions by over 50% compared to that of traditional ML techniques.

Machine Learning for data analysts

At re:Invent last year, we announced ML integrated inside Amazon Athena for data analysts. With this integration, you can access more than a dozen built-in ML models or use your own models in SageMaker directly from ad-hoc queries in Athena. As a result, you can easily run ad-hoc queries in Athena that use ML to forecast sales, detect suspicious logins, or sort users into customer cohorts.

Similarly, data analysts also want to apply ML to the data in their Amazon Redshift data warehouse. Tens of thousands of customers use Amazon Redshift to process exabytes of data per day. These Amazon Redshift users want to run ML on their data in Amazon Redshift without having to write a single line of Python. Today we announced the preview of Amazon Redshift ML to do just that.

Amazon Redshift now enables you to run ML algorithms on Amazon Redshift data without manually selecting, building, or training an ML model. Amazon Redshift ML works with Amazon SageMaker Autopilot, a service that automatically trains and tunes the best ML models for classification or regression based on your data while allowing full control and visibility.

When you run an ML query in Amazon Redshift, the selected data is securely exported from Amazon Redshift to Amazon Simple Storage Service (Amazon S3). SageMaker Autopilot then performs data cleaning and preprocessing of the training data, automatically creates a model, and applies the best model. All the interactions between Amazon Redshift, Amazon S3, and SageMaker are abstracted away and automatically occur. When the model is trained, it becomes available as a SQL function for you to use. The following diagram illustrates this workflow.

Rackspace Technology – a leading end-to-end multicloud technology services company, and Slalom –  a modern consulting firm focused on strategy, technology, and business transformation are both users of Redshift ML in preview.

Nihar Gupta, General Manager for Data Solutions at Rackspace Technology says “At Rackspace Technology, we help companies elevate their AI/ML operationsthe seamless integration with Amazon SageMaker will empower data analysts to use data in new ways, and provide even more insight back to the wider organization.”

And Marcus Bearden, Practice Director at Slalom shared “We hear from our customers that they want to have the skills and tools to get more insight from their data, and Amazon Redshift is a popular cloud data warehouse that many of our customers depend on to power their analytics, the new Amazon Redshift ML feature will make it easier for SQL users to get new types of insight from their data with machine learning, without learning new skills.”

Machine Learning for business analysts

To bring ML to business analysts, we launched new ML capabilities in Amazon QuickSight earlier this year called ML Insights. ML Insights uses SageMaker Autopilot to enable business analysts to perform ML inference on their data and visualize it in BI dashboards with just a few clicks. You can get results for different use cases that require ML, such as anomaly detection to uncover hidden insights by continuously analyzing billions of data points, to do forecasting, to predict growth, and other business trends. In addition, QuickSight can also give you an automatically generated summary in plain language (a capability we call auto-narratives), which interprets and describes what the data in your dashboard means. See the following screenshot for an example.

Customers like Expedia Group, Tata Consultancy Services, and Ricoh Company are already benefiting from ML out of the box with QuickSight. These human-readable narratives enable you to quickly interpret the data in a shared dashboard and focus on the insights that matter most.

In addition, customers have also been interested in asking questions of their business data in plain language and receiving answers in near-real time. Although some BI tools and vendors have attempted to solve this challenge with Natural Language Query (NLQ), the existing approaches require that you first spend months in advance preparing and building a model on a pre-defined set of data, and even then, you still have no way of asking ad hoc questions when those questions require a new calculation that wasn’t pre-defined in the data model. For example, the question “What is our year-over-year growth rate?” requires that “growth rate” be pre-defined as a calculation in the model. With today’s BI tools, you need to work with your BI teams to create and update the model to account for any new calculation or data, which can take days or weeks of effort.

Last week, we announced Amazon QuickSight Q. ‘Q’ gives business analysts the ability to ask any question of all their data and receive an accurate answer in seconds. To ask a question, you simply type it into the QuickSight Q search bar using natural language and business terminology that you’re familiar with. Q uses ML (natural language processing, schema understanding, and semantic parsing for SQL code generation) to automatically generate a data model that understands the meaning of and relationships between business data, so you can get answers to your business questions without waiting weeks for a data model to be built. Because Q eliminates the need to build a data model, you’re also not limited to asking only a specific set of questions. See the following screenshot for an example.

Best Western Hotels & Resorts is a privately-held hotel brand with a global network of approximately 4,700 hotels in over 100 countries and territories worldwide. “With Amazon QuickSight Q, we look forward to enabling our business partners to self-serve their ad hoc questions while reducing the operational overhead on our team for ad hoc requests,” said Joseph Landucci, Senior Manager of Database and Enterprise Analytics at Best Western Hotels & Resorts. “This will allow our partners to get answers to their critical business questions quickly by simply typing and searching their questions in plain language.”

Summary

For ML to have a broad impact, we believe it has to get easier to do and easier to apply. Database developers, data analysts, and business analysts who work with databases and data lakes have found it too difficult and involved to extract meaningful insights from their data using ML. To meet the needs of this large and growing group of builders, we’ve added ML capabilities to our purpose-built databases and analytics services so that database developers, data analysts, and business analysts can all use ML more easily without the need to be an ML expert. These capabilities put ML in the hands of every data professional so that they can get the most value from their data.

About the Authors

Swami Sivasubramanian is Vice President at AWS in charge of all Amazon AI and Machine Learning services. His team’s mission is “to put machine learning capabilities in the hands on every developer and data scientist.” Swami and the AWS AI and ML organization work on all aspects of machine learning, from ML frameworks (Tensorflow, Apache MXNet and PyTorch) and infrastructure, to Amazon SageMaker (an end-to-end service for building, training and deploying ML models in the cloud and at the edge), and finally AI services (Transcribe, Translate, Personalize, Forecast, Rekognition, Textract, Lex, Comprehend, Kendra, etc.) that make it easier for app developers to incorporate ML into their apps with no ML experience required.

Previously, Swami managed AWS’s NoSQL and big data services. He managed the engineering, product management, and operations for AWS database services that are the foundational building blocks for AWS: DynamoDB, Amazon ElastiCache (in-memory engines), Amazon QuickSight, and a few other big data services in the works. Swami has been awarded more than 250 patents, authored 40 referred scientific papers and journals, and participates in several academic circles and conferences.

Herain Oberoi leads Product Marketing for AWS’s Databases, Analytics, BI, and Blockchain services. His team is responsible for helping customers learn about, adopt, and successfully use AWS services. Prior to AWS, he held various product management and marketing leadership roles at Microsoft and a successful startup that was later acquired by BEA Systems. When he’s not working, he enjoys spending time with his family, gardening, and exercising.