Learn data science best practices

Learn how to achieve real business value from machine learning projects.

Learn how to achieve real business value from machine learning projects.

Today, robotics, AI, and machine learning can be found outside of Star Wars movies. Yet, do you really understand the difference between the three technologies? Or what projects are under way at Oracle to build next-generation solutions?

Today, robotics, AI, and machine learning can be found outside of Star Wars movies. Yet, do you really understand the difference between the three technologies? Or what projects are under way...

Learn how to scale reinforcement learning through Deep Q-Learning.

Learn how to scale reinforcement learning through Deep Q-Learning.

When it comes to AI and machine learning, it's sometimes a Wild West out there. How can university business officers navigate the hype? We have some advice for Higher Education from the recent Educause Horizon Report.

When it comes to AI and machine learning, it's sometimes a Wild West out there. How can university business officers navigate the hype? We have some advice for Higher Education from the...

Accelerated Data Science (ADS) provides a single library that covers all the steps in the lifecycle of predictive machine learning models.

Accelerated Data Science (ADS) provides a single library that covers all the steps in the lifecycle of predictive machine learning models.

Discover how to build a custom environment for reinforcement learning.

Discover how to build a custom environment for reinforcement learning.

Learn how the Bayes factor can help maintain fairness in machine learning models for domains like financial services and hiring.

Learn how the Bayes factor can help maintain fairness in machine learning models for domains like financial services and hiring.

Celebrate women in tech at Oracle for International Women’s Day.

Celebrate women in tech at Oracle for International Women’s Day.

Are fairness and accuracy inversely related in machine learning? Learn more in this blog post based on a NeurIPS 2019 paper.

Are fairness and accuracy inversely related in machine learning? Learn more in this blog post based on a NeurIPS 2019 paper.

Learn how to build a Q-Learning application on an OpenAI gym environment.

Learn how to build a Q-Learning application on an OpenAI gym environment.

85% of big data projects fail by imploding under their own complexity. Oracle Cloud Infrastructure Data Flow represents a more efficient, more secure, and simply easier way to take projects to completion.

85% of big data projects fail by imploding under their own complexity. Oracle Cloud Infrastructure Data Flow represents a more efficient, more secure, and simply easier way to take projects...

Oracle announces Oracle Cloud Infrastructure Data Catalog, a brand new tool to manage and govern your big data. Organize, enrich, search, and consolidate data in a way that expedites and optimizes your data lake.

Oracle announces Oracle Cloud Infrastructure Data Catalog, a brand new tool to manage and govern your big data. Organize, enrich, search, and consolidate data in a way that expedites and...

Oracle Cloud Infrastructure Data Science is an enterprise grade data science service where teams of data scientists can collaborate to build, train, and deploy machine learning models.

Oracle Cloud Infrastructure Data Science is an enterprise grade data science service where teams of data scientists can collaborate to build, train, and deploy machine learning models.

See how Deep Learning can help reduce traffic congestion and pollution by providing real-time parking availability.

See how Deep Learning can help reduce traffic congestion and pollution by providing real-time parking availability.

Learn how to ensure both accuracy and privacy for machine learning models.

Learn how to ensure both accuracy and privacy for machine learning models.

A new survey shows companies that have embraced emerging technologies are growing their profits 80% faster than peers who haven’t.

A new survey shows companies that have embraced emerging technologies are growing their profits 80% faster than peers who haven’t.

Learn about unpredictability of intelligent systems, which poses a challenge to AI Safety by limiting the ability to understand the impact of intelligent systems.

Learn about unpredictability of intelligent systems, which poses a challenge to AI Safety by limiting the ability to understand the impact of intelligent systems.

Attend our Feb. 4 webinar to learn how to overcome key roadblocks in the AI development lifecycle and make AI a sustainable competitive advantage for your company.

Attend our Feb. 4 webinar to learn how to overcome key roadblocks in the AI development lifecycle and make AI a sustainable competitive advantage for your company.

Learn how to overcome AI roadblocks, and how to unite data scientists and business users for better AI adoption.

Learn how to overcome AI roadblocks, and how to unite data scientists and business users for better AI adoption.

These six best practices will help data scientists design effective data visualizations.

These six best practices will help data scientists design effective data visualizations.

Artificial intelligence usage will grow in 2020. Here are some predictions of what to expect with AI in Finance, Supply chain, sales, marketing, HR.

Artificial intelligence usage will grow in 2020. Here are some predictions of what to expect with AI in Finance, Supply chain, sales, marketing, HR.

Artificial intelligence usage will grow in 2020. Here are some predictions of what to expect with AI in Finance, Supply chain, sales, marketing, HR.

Artificial intelligence usage will grow in 2020. Here are some predictions of what to expect with AI in Finance, Supply chain, sales, marketing, HR.

For a successful and scalable data science team, don’t leave it all to your data scientists. Data engineers and machine learning engineers can improve outcomes.

For a successful and scalable data science team, don’t leave it all to your data scientists. Data engineers and machine learning engineers can improve outcomes.

Learn why a collaborative data science environment matters, and how to build one for your team.

Learn why a collaborative data science environment matters, and how to build one for your team.

Learn how machine learning model lifecycle management compares to database patch lifecycle management.

Learn how machine learning model lifecycle management compares to database patch lifecycle management.

Learn how AI/ML can help augment the user experience in the retail/commerce & entertainment industries.

Learn how AI/ML can help augment the user experience in the retail/commerce & entertainment industries.

Learn how automation, robots, and artificial intelligence are already changing how relationships between workers and managers will look in the future workplace.

Learn how automation, robots, and artificial intelligence are already changing how relationships between workers and managers will look in the future workplace.

Learn how the DataFox Data Cleaning team manages company data to help customers get a unique, more complete view of their market.

Learn how the DataFox Data Cleaning team manages company data to help customers get a unique, more complete view of their market.

Due to the volume, heterogeneity, and interdependency of data, IoT benefits cannot be derived without artificial intelligence. This is why, every Oracle IoT SaaS application is built with AI at its core.

Due to the volume, heterogeneity, and interdependency of data, IoT benefits cannot be derived without artificial intelligence. This is why, every Oracle IoT SaaS application is built with AI at...

Learn how to expose a machine learning model trained on the Autonomous Database as a REST service.

Learn how to expose a machine learning model trained on the Autonomous Database as a REST service.

Learn how AutoML on Oracle Cloud can help simplify machine learning tasks.

Learn how AutoML on Oracle Cloud can help simplify machine learning tasks.

Evaluate which machine learning models elicit the greatest profit for your business with the Expected Value Framework.

Evaluate which machine learning models elicit the greatest profit for your business with the Expected Value Framework.

How can CFOs harness the power of new digital technologies such as AI and machine learning? Here are six strategies to help your organization compete in the digital age of finance.

How can CFOs harness the power of new digital technologies such as AI and machine learning? Here are six strategies to help your organization compete in the digital age of finance.

Victor Lu, Consulting Senior Principal Consultant for Core Database, provides his analysis of NoSQL solutions. 

Victor Lu, Consulting Senior Principal Consultant for Core Database, provides his analysis of NoSQL solutions. 

Artificial Intelligence and the Oracle CX Cloud – The Power Behind Informed and Productive Human Interactions

Artificial Intelligence and the Oracle CX Cloud – The Power Behind Informed and Productive Human Interactions

Learn how to develop machine learning applications using the Autonomous Database.

Learn how to develop machine learning applications using the Autonomous Database.

Recommendation systems are key to solving recruitment challenges. Learn how we use Cold Start to enhance the process.

Recommendation systems are key to solving recruitment challenges. Learn how we use Cold Start to enhance the process.

We’ve all heard news stories about self-driving cars or DeepMinds beating the Go world champion. But is AI up to the hype? We separate reality from myth.

We’ve all heard news stories about self-driving cars or DeepMinds beating the Go world champion. But is AI up to the hype? We separate reality from myth.

Amidst rising user privacy concerns, developers are working to protect data at the software level. Learn how differential privacy can help.

Amidst rising user privacy concerns, developers are working to protect data at the software level. Learn how differential privacy can help.

Emerging tech capabilities for finance have arrived. Isn’t it time you took advantage of them?

Emerging tech capabilities for finance have arrived. Isn’t it time you took advantage of them?

To learn how Oracle Cloud Platform and its technologies can help you in your cloud journey, please attend the Platform as a Service Cloud Day, a free virtual event hosted by the Quest Oracle Community.

To learn how Oracle Cloud Platform and its technologies can help you in your cloud journey, please attend the Platform as a Service Cloud Day, a free virtual event hosted by the Quest...

Businesses will reap more success from AI projects by setting short-term, achievable goals instead of pursuing extremely ambitious ones, industry executives advise.

Businesses will reap more success from AI projects by setting short-term, achievable goals instead of pursuing extremely ambitious ones, industry executives advise.

At my first Oracle Open World as a recent employee, the excitement about artificial intelligence was everywhere: observations, customer story, and product announcements.

At my first Oracle Open World as a recent employee, the excitement about artificial intelligence was everywhere: observations, customer story, and product announcements.

Savita Raina sits down with singer and songwriter Jennifer Johns to discuss AI and data.

Savita Raina sits down with singer and songwriter Jennifer Johns to discuss AI and data.

Learn from AI Influencer Subhash Kak about the debate on quantum computing for data scientists.

Learn from AI Influencer Subhash Kak about the debate on quantum computing for data scientists.

Jan Teichmann of Zoopla covers delivery management for successful data science projects.

Jan Teichmann of Zoopla covers delivery management for successful data science projects.

Surprises in learning models can expand knowledge, but they also increase risk. Read insights from Roman Yampolskiy on how to make AI safer.

Surprises in learning models can expand knowledge, but they also increase risk. Read insights from Roman Yampolskiy on how to make AI safer.

Graph data processing is already an integral part of big-data analytics with many applications in various domains including Finance, Cyber Security, Compliance, Retail, and Health Sciences. The adoption of graph processing is expected to further grow in the upcoming years. This is partially because graphs can naturally represent data that captures fine-grained relationships among entities. Graph analysis can provide valuable insights about such data by examining these relationships. Oracle Labs PGX has been providing graph solutions both for Big Data and for Relational Database customers.  In this post, I will describe our new distributed graph traversal solution that significantly improves performance and memory consumption of Oracle PGX's in-memory distributed graph query engine. That is especially true on very large graph queries where our competitors either fail to execute due to memory usage (see the performance figures later in the post), or fall back to slow and inefficient disk-based joins.  Typically, graph analysis is performed with two distinct but correlated methods, namely computational analysis (a.k.a graph algorithms) and pattern matching queries. Most graph engines nowadays, such as Oracle Labs PGX and Apache Spark GraphX/GraphFrames, support both graph algorithms and graph queries. With computational analysis, the user executes various algorithms that traverse the graph, often repeatedly, and calculate certain (numeric) values to get the desired information, e.g., PageRank or shortest paths. Pattern matching queries are declaratively given as graph patterns. The system finds every subgraph of the target graph that is topologically isomorphic/homomorphic to the query graph and satisfies any accompanying filters. For example, the following PGQL (for Property Graph Query Language) query: returns the persons p1 (called "John Doe") and p3 who have the largest number of common friends. Such queries can be used for example for friend recommendation.  Graph queries are a very challenging workload because they focus on the connections in the data. By following connections, i.e., edges, graph query execution can possibly explore large parts of the graph, generating large intermediate and final result sets with a combinatorial explosion effect. For example, on a very old snapshot of Twitter (known as the "Twitter graph" in academic graph-related research papers), a single-edge query (e.g., (v0)→(v1)) matches the whole graph, counting 1.4 billion results. A two-edge query (e.g., (v0)→(v1)→(v2)) returns more than nine trillion matches. Additionally, graph queries can exhibit extremely irregular access patterns and therefore require low-latency data accesses. For this reason, high-performance graph query engines try to keep data in main memory and scale out to a distributed system in order to handle graphs that exceed the capacity of a single node. Graph data processing and querying is an increasing market that has many applications in various domains including Finance, Cyber Security, Compliance, Retail, and Health Sciences.  These applications often require querying very large graph data in a fast and efficient manner. Oracle has been providing graph solutions both for Big Data and Relational Database audiences, while there are also commercial competitors like Amazon Neptune, Neo4J , as well as open source alternatives including Spark GraphFrame. Our invention can provide significant differentiation for Oracle's solution over those competitors.  Our invention significantly improves performance and memory consumption of Oracle's current in-memory distributed graph query engine, especially on very large graph queries where our competitors either fail to execute due to memory usage (e.g., Spark GraphFrame) or to fall back to slow and inefficient disk-based joins (Neptune or Neo4J).  Traditional Distributed Graph Traversal Approaches In a distributed system, graphs are typically partitioned across machines by vertices, meaning that each machine is storing a partition of the vertices of the overall graph, plus the edges corresponding to that vertex. For example, in the graph below, machine 0 stores vertices v0, v1, and v2, while machine 1 holds vertices v3 and v4. The edge between v0→v1 is local to machine 0, while the edge connecting v2→v3 is remote, as it spans machines 0 and 1. For large distributed graphs, none of the traditional graph exploration/traversal approaches is suitable for distributed queries. Breadth-first traversals and distributed joins quickly explode in terms of intermediate results and pose a performance challenge over the network. Depth-first traversals are challenging to paralellize and result in completely random data access patterns. In practice, most engines use breadth-style traversals combined with synchronous / blocking communication across machines.  Breadth-First Traversals In breadth-first traversals, the execution expands the query in width. The query pattern is matched to the target graph edge-after-edge. For example, matching pattern (a)→(b)→(c) to the example graph above could proceed by matching edge (a)→(b) to all graph edges, namely (v0)→(v1), (v0)→(v2) etc., and then proceed with expanding these intermediate results to match edge (b)→(c). Typically, the execution proceeds with synchronous traversals, i.e., the first edge is completely matched before moving to the next edge to match.  Expanding the query breadth-first is not ideal for a distributed system. First, materializing large sets of intermediate results at every step leads to an intermediate-result explosion. Breadth-first traversals typically have the benefit of locality (i.e., accessing adjacent edges one after the other). Unfortunately, locality in distributed graphs is much more limited, since many of the edges that are followed are remote. As a result, a large part of the intermediate results produced at each part of the query must be sent to the remote machines, creating large network bursts. Distributed Joins Graph traversals can be also expressed as relational joins. Following the edge (a)→(b) can be mapped to a join (or two) between the "vertex table" (holding the graph vertices) and the "edge table" (holding the edges): Distributed joins face the same problems as breadth-first traversals, plus an additional important problem. They perform table joins instead of graph traversals on top of specialized graph data structures. Unsurprisingly, graph-specific data structures are much faster than generic joins (see later in this post the performance comparison of Oracle Labs PGX Distributed to Apache Spark GraphFrames). Depth-First Traversals In depth-first traversals, the execution expands the query in depth. The query pattern is matched to the target graph as a whole, result-by-result. For example, matching the pattern (a)→(b)→(c) to the example graph above could proceed by matching (v0)→(v1)→(v2), then (v0)→(v2)→(v3), etc.  The main advantage of expanding the query depth-first is that intermediate results can be eagerly expanded to final results, thus reducing the memory footprint of query execution. Nevertheless, depth-first traversals have the disadvantages of not leveraging locality and of more complicated parallelism. The lack of locality is depth-first results in "edge chasing" – i.e., following one edge after the other as dictated by the query pattern – thus not accessing adjacent edges in order. The complication for parallelism manifests because the runtime cannot know in advance if there is enough work for all threads at any stage during the query. For instance, a query like the `MATCH (p1:person)-[:friend]->(p2:person)<-[:friend]-(p3:person) WHERE p1 <> p2 AND p2 <> p3 AND p1.name = "John Doe"` that I described in the beginning of the post will probably produce a single match for (p1). If this intermediate result is expanded in a depth-first manner, the amount of intermediate results (hence the parallelism) will grow slowly.  Dynamic Asynchronous Traversals For Distributed Graphs The Parallel Graph AnalytiX (PGX) toolkit, developed at Oracle Labs, is capable of executing graph analysis in a distributed way (i.e., across multiple servers); we refer to this capability as PGX.D. We are experimenting in PGX.D with a new hybrid approach to executing graph traversals that offers the best of breadth-first and depth-first traversals. Competing graph engines include the classic trade-off between performance and memory consumption for graph query execution: Sacrifice performance: One option is to use a fixed memory area (typically several gigabytes) for the execution, but spill intermediate results that do not fit to disk. Sacrifice memory: Another option is to perform the whole computation in memory. If the intermediate results do not fit the memory, this approach cannot compute this query on that graph. PGX.D enables the in-memory execution of any-size query without sacrificing memory or performance. In particular, graph queries in PGX.D: Can operate with a fixed, predefined amount of memory for storing intermediate results; Only use this memory for computations, i.e., do not spill any intermediate results to disk; and Can essentially calculate queries of any size, because intermediate results are turned to final results "on-demand", to keep memory consumption within limits. On the technical side, PGX.D achieves the aforementioned characteristics by deploying: Dynamic traversals, using Depth-first execution when needed, thus aggressively completing intermediate results and keeping the memory consumption within limits; and Breadth-first execution when possible, thus removing the performance complexities of depth-first traversals. Asynchronous communication of intermediate results from one machine to the other, thus not blocking/delaying local computation due to remote edges. Flow-control and incremental termination to keep global memory consumption (including messaging) within limits and guarantee the query termination (i.e., avoid deadlocks). Example Consider matching the pattern (a)→(b)→(c) to our example graph presented above and consider a worker thread currently starting to match from vertex v0. The thread can bind v0 as (a) and then try to expand to the edge (a)→(b). The worker could match (b) with v1. At this point, the dynamic traversal approach in PGX.D, based on how much memory does the query already consume, will dictate whether the worker will continue matching (a)→(b) edges (breadth) or should continue with the (b)→(c) edge (depth). In either case, the worker will eventually match v3 for (b) in which case PGX.D simply buffers the intermediate match to a message destined for machine 1 and continues matching the next (a)→(b) edge in a bread-expanding manner. Of course, PGX.D controls the number of outgoing messages / intermediate results in order to maintain the execution within limits. You can find more details in our GRADES 2017 publication that describes the main runtime queries in PGX.D (missing the local dynamicity which is described in a follow-up publication currently under submission). Comparing PGX.D to Open-Source Systems We use the LDBC social network benchmark graph (scale 100, 283 million vertices, 1.78 billion edges) and queries (we adapted the queries to reflect the current features of PGX.D; these changes mainly include the removal of HAVING clause, subqueries, and regular path queries).  We compare PGX.D to Apache Spark GraphFrames (version 0.7 on top of Spark 2.4.1) and PostgreSQL (version 11.2). Both PGX.D and GraphFrames execute on 8 machines connected with Infiniband. We perform 15 repetitions and report the median run.   Clearly, PGX.D is significantly faster than both GraphFrames and the traditional PostgreSQL RDBMS. PGX.D executes the total query suite 29.5- and 17.5-times faster than GraphFrames and PosgreSQL, respectively. In addition, PGX.D is configured to use approximately 16GB runtime memory for intermediate results, while the other two engines are configured to use the whole 756GB (8x for GraphFrames) of memory available in the underlying machines. As I mentioned earlier in this post, GraphFrames implements graph traversals on top of distributed joins on dataframes.  Large-Scale Queries  In this experiment, we evaluate the engines with very large queries. In particular: Q1: Simple cycle; pattern (v1)→(v2)→(v1) with (a) a COUNT(*) aggregation and (b) AVG aggregations of vertex data; Q2: Two-hop match; pattern (v1)→(v2)→(v3) with (a) a COUNT(*) aggregation and (b) AVG aggregations of vertex data. We execute these queries on graphs of increasing size: Graph # Vertices # Edges Description Livejournal  484K  68.9M Users and friendships Uniform Random  100M  1B  Uniform random edges Twitter  42.6M 1.47B Tweets and followers Webgraph-UK 77.7M 2.97B  2006 .uk domains This experiment highlights the true need for the scalable in-memory distributed graph traversal methodology of PGX.D. As the query exploration size increases, GraphFrames and PostgreSQL cannot keep up with the workload. Even with the two smallest graphs, PGX.D is on average 48- and 115-times faster than GraphFrames and PostgreSQL, respectively. Clearly, joins in PostgreSQL are significantly slower than graph traversals in PGX.D. With Q2 on Twitter and Webgraph-UK we see that even the 8 x 756GB = 6TB of total memory (backed by 1+TB of disk) is not sufficient for GraphFrames. As in the previous experiment, PGX.D completes these queries with approximately 16GB of memory in each machine.  What's Next We have explored dynamic asynchronous traversals in PGX.D only for graph querying and pattern matching with PGQL. We are currently exploring how to further leverage these fast in-memory explorations for machine learning. For example, we are developing large-scale random walks on top of PGX.D that will serve as the backbone for graph machine-learning solutions.  Conclusions I briefly presented our new dynamic asynchronous traversal approach for distributed graphs in PGX distributed mode. Using this approach PGX.D achieves fast, scalable, fully in-memory, with a small memory-footprint distributed graph queries, thus enabling graph processing on a whole new scale of graphs and queries. The hybrid/dynamic traversal functionality is on PGX.D roadmap so stay tuned for new information on its availability. For more information and for trying PGX, you can visit Oracle Labs PGX Technology Network Page.  

Graph data processing is already an integral part of big-data analytics with many applications in various domains including Finance, Cyber Security, Compliance, Retail, and Health Sciences. The adoption...

How can AI help you grow your business? Check out these sessions at OpenWorld 2019 to learn more.

How can AI help you grow your business? Check out these sessions at OpenWorld 2019 to learn more.

Oracle Enterprise Cloud is putting the power of artificial intelligence into the hands of customers today. Join us at Oracle OpenWorld this September to learn how. And Check out these featured sessions and demo about Oracle AI Applications.

Oracle Enterprise Cloud is putting the power of artificial intelligence into the hands of customers today. Join us at Oracle OpenWorld this September to learn how. And Check out these...

KDnuggets is where data professionals go for answers. Editor Matthew Mayo answers questions about data science trends.

KDnuggets is where data professionals go for answers. Editor Matthew Mayo answers questions about data science trends.

Still holding off on adopting AI and other advanced technologies via the cloud? Here are 3 reasons that’s putting your business at risk.

Still holding off on adopting AI and other advanced technologies via the cloud? Here are 3 reasons that’s putting your business at risk.

Kirk Borne, Principal Data Scientist at Booz Allen Hamilton, breaks down pattern discovery for common business use cases.

Kirk Borne, Principal Data Scientist at Booz Allen Hamilton, breaks down pattern discovery for common business use cases.

Read about Victor Lu's experience with Oracle Database, from the evolution of the database optimizer to the ways that the Autonomous Database helps customers develop data science and machine learning solutions.

Read about Victor Lu's experience with Oracle Database, from the evolution of the database optimizer to the ways that the Autonomous Database helps customers develop data science and machine...

Compute accurate, real-time predictions from a Named Entity Disambiguation algorithm using graph embeddings.

Compute accurate, real-time predictions from a Named Entity Disambiguation algorithm using graph embeddings.

Connect machine learning outcomes to business metrics through the Expected Value Framework.

Connect machine learning outcomes to business metrics through the Expected Value Framework.

Learn about Named Entity Disambiguation, and how to construct a powerful Knowledge Graph from Wikipedia.

Learn about Named Entity Disambiguation, and how to construct a powerful Knowledge Graph from Wikipedia.

Learn a quick way of choosing the optimum Oracle Cloud Infrastructure Compute shape for your machine learning needs.

Learn a quick way of choosing the optimum Oracle Cloud Infrastructure Compute shape for your machine learning needs.

Data maturity measures and evaluates a business’ habits, behaviours, and tools.

Data maturity measures and evaluates a business’ habits, behaviours, and tools.

Artificial intelligence is making a huge impact in the banking and finance sector, and the future of AI has even more to offer.

Artificial intelligence is making a huge impact in the banking and finance sector, and the future of AI has even more to offer.

Learn how to build a simple facial recognition application with a Python API and four lines of code.

Learn how to build a simple facial recognition application with a Python API and four lines of code.

Create a Jupyter Notebook and use python to connect to an Oracle Autonomous Transaction Processing Database instance to run a classification using the machine learning library called Scikit-Learn.

Create a Jupyter Notebook and use python to connect to an Oracle Autonomous Transaction Processing Database instance to run a classification using the machine learning library called Scikit-Learn.

Learn about 7 artificial intelligence (AI) industry trends that help users simplify and scale the overall machine learning (ML) lifecycle. Find out why each trend matters for operational machine learning and what factors should be considered as a business decides to exploit a trend to accelerate their operational machine learning practice.

Learn about 7 artificial intelligence (AI) industry trends that help users simplify and scale the overall machine learning (ML) lifecycle. Find out why each trend matters for operational...

In this post, we will take a deep dive into how to build knowledge graphs followed by a demonstration of how Knowledge Graphs could be employed with Graph Machine Learning towards enhanced healthcare services.

In this post, we will take a deep dive into how to build knowledge graphs followed by a demonstration of how Knowledge Graphs could be employed with Graph Machine Learning towards enhanced...

Marketing data scientist Abhijit Sanyal presents a framework to use social media data to develop an audience attribution and research platform.

Marketing data scientist Abhijit Sanyal presents a framework to use social media data to develop an audience attribution and research platform.

Learn how artificial intelligence is impacting the way companies do business and how to start “plugging AI” into your company’s existing data and systems.

Learn how artificial intelligence is impacting the way companies do business and how to start “plugging AI” into your company’s existing data and systems.

In this series of two blog posts, I will show how we can employ Graph Machine Learning techniques to tackle a healthcare use case- the problem of automatically predicting diagnoses for patients staying in intensive care units. In this first post, we will introduce how we can build Knowledge Graphs (KGs) from heterogeneous sources. In the second post, we will deep-dive in the healthcare domain to demonstrate how to employ Graph Machine Learning over the constructed healthcare KG for diagnosis prediction.

In this series of two blog posts, I will show how we can employ Graph Machine Learning techniques to tackle a healthcare use case- the problem of automatically predicting diagnoses for...

In part 2 of this transfer learning tutorial, we learn how to create a transfer learning class and train it on Kaggle's much larger dataset.

In part 2 of this transfer learning tutorial, we learn how to create a transfer learning class and train it on Kaggle's much larger dataset.

In part 1 of this transfer learning tutorial, we learn how to build datasets and DataLoaders for train, validation, and testing using PyTorch API, as well as a fully connected class on top of PyTorch's core NN module.

In part 1 of this transfer learning tutorial, we learn how to build datasets and DataLoaders for train, validation, and testing using PyTorch API, as well as a fully connected class on top of...

ActivePDF Director of Marketing Derek Gerber outlines four main categories of data analytics and their functions.

ActivePDF Director of Marketing Derek Gerber outlines four main categories of data analytics and their functions.

Learn how to set up a machine learning sandbox on Oracle Cloud Infrastructure (OCI). In this tutorial, you'll learn how to provision a GPU compute environment on Oracle Cloud Infrastructure Compute and install Anaconda Distribution, an open source tool for developing and testing machine learning models.

Learn how to set up a machine learning sandbox on Oracle Cloud Infrastructure (OCI). In this tutorial, you'll learn how to provision a GPU compute environment on Oracle Cloud Infrastructure Compute...

Black Hills IP CTO Tom Marlow explains why patent data is a fertile ground for analysis and insight on research and development activity around the world.

Black Hills IP CTO Tom Marlow explains why patent data is a fertile ground for analysis and insight on research and development activity around the world.

Oracle Principal Data Scientist Taylor Foust tackles the common issue of label bias in positive and unlabeled learning, and shares some techniques that may be useful in identifying and mitigating these problems.

Oracle Principal Data Scientist Taylor Foust tackles the common issue of label bias in positive and unlabeled learning, and shares some techniques that may be useful in identifying and...

Learn about five key considerations for adopting AI to achieve business objectives.

Learn about five key considerations for adopting AI to achieve business objectives.

Oracle Senior Principal Data Scientist Su-Ming Wu presents a problem with maximum likelihood introduced in Part I of the series and offers up his solution.

Oracle Senior Principal Data Scientist Su-Ming Wu presents a problem with maximum likelihood introduced in Part I of the series and offers up his solution.

Oracle Senior Principal Data Scientist Su-Ming Wu shares how to use machine learning techniques to estimate size profiles from historical sales data.

Oracle Senior Principal Data Scientist Su-Ming Wu shares how to use machine learning techniques to estimate size profiles from historical sales data.

AI that is ready-to-go, ready-to-build, and ready-to-work can make achieving value easy. Learn about Oracle’s AI strategy and product offerings, and find out why Oracle is the right partner to guide your journey with AI.

AI that is ready-to-go, ready-to-build, and ready-to-work can make achieving value easy. Learn about Oracle’s AI strategy and product offerings, and find out why Oracle is the right partner to...

Learn Tableau with this tutorial that walks you through connecting data, building visualizations, setting up a dashboard, and saving to a server.

Learn Tableau with this tutorial that walks you through connecting data, building visualizations, setting up a dashboard, and saving to a server.

Artificial intelligence has been changing the landscape for all kinds of businesses. Discover how the world of retail using AI to transform the in-store environment, to offer superior experiences and achieve higher revenues.

Artificial intelligence has been changing the landscape for all kinds of businesses. Discover how the world of retail using AI to transform the in-store environment, to offer superior experiences and...

Learn about the Convolutional Neural Network, its architecture, how it's built, and its many applications.

Learn about the Convolutional Neural Network, its architecture, how it's built, and its many applications.

Learn what it takes to make a chatbot intelligent and human-like, and create more meaningful experiences for your customers.

Learn what it takes to make a chatbot intelligent and human-like, and create more meaningful experiences for your customers.

Scientist Sinisa Vukovic showcases the business value of graph implementation, specifically Combinatorial Subgraph Search (CSS).

Scientist Sinisa Vukovic showcases the business value of graph implementation, specifically Combinatorial Subgraph Search (CSS).

Machine learning practitioner Shashank Shekhar Rai goes over the different types of missing data as well as the common methods to handle missing data.

Machine learning practitioner Shashank Shekhar Rai goes over the different types of missing data as well as the common methods to handle missing data.

365 Data Science's Iliya Valchanov shares the results of a study of 1,001 data scientist LinkedIn profiles. Find out how the typical data scientist in 2019 prepares for the rigors of the job.

365 Data Science's Iliya Valchanov shares the results of a study of 1,001 data scientist LinkedIn profiles. Find out how the typical data scientist in 2019 prepares for the rigors of the job.

Learn a practical way to predict customer demand with machine learning. We will use the Oracle Autonomous Data Warehouse Cloud with its built-in machine learning capabilities. One of the things I like especially about this approach rather than using an external ML tool is that it lets us work directly with the data at its source, without any data movements.

Learn a practical way to predict customer demand with machine learning. We will use the Oracle Autonomous Data Warehouse Cloud with its built-in machine learning capabilities. One of the things I like...

Oracle Cloud Solution Hub leader Jukka Paajanen presents an IoT use case around understanding human behavior through biometrics.

Oracle Cloud Solution Hub leader Jukka Paajanen presents an IoT use case around understanding human behavior through biometrics.

Learn how to move from a machine learning (ML) experiment into a production grade application.

Learn how to move from a machine learning (ML) experiment into a production grade application.

Carol Hargreaves, director of the data analytics consulting centre at the University of Singapore, presents six steps to building a powerful customer analytics system.

Carol Hargreaves, director of the data analytics consulting centre at the University of Singapore, presents six steps to building a powerful customer analytics system.

Oracle Conference Center, Redwood Shores, CA All Analytics, All Data, No Nonsense! Analytics and Data Summit 19 is a once-a-year opportunity for Oracle technology users.   Designed for Oracle technical professions, it is like a mini-Oracle World or Code One event held on the Oracle Campus for customers, users and partners, but focused on "novel and interesting" use cases of Oracle technologies.   The 3-day event is a great way to network with peers, share, and learn from “novel and interesting use cases” of Oracle’s amazing Analytics and Data technologies.    Analytics and Data Summit 19 delivers: More than 100 technical sessions delivered by technical experts, customers, partners, product managers and developers.  Oracle Database, Oracle Autonomous, Oracle Machine Learning, Oracle Advanced Analytics, Oracle Analytics Cloud, Oracle DVD, Oracle Business Intelligence EE, Oracle Spatial & Graph and more! IoT, Python, R, Blockchain, Kafka, Streaming, RDF and more! Hands on Labs training taught by Product Managers and technical experts Networking opportunities to meet with Oracle ACEs, Customers and Global Leader Customers, Product Managers, Developers and Partners and Consultants. Latest product updates and insider information from Oracle executive management, product managers and developers Partners and Consultants who can help you solve your specific challenges See this 2 min Analytics and Data Summit overview video on Twitter for more information. Click on the Analytics and Data Summit 19 Schedule for the full detailed 3-day Agenda. Analytics and Data Summit 19 is run by an independent user group (BIWA Inc.); not Oracle. Check out #AnD_Summit on Twitter for any current applicable discount codes to receive $50-$75 off the then current registration fee.  Please helps spread the word to friends, colleagues and users and Oracle technical professionals to register.  Being run by an independent user group (BIWA Inc.), we rely on word of mouth and grass roots user community initiatives—a lot, so really appreciate the community support! We have another great lineup this year and hope to have our biggest Analytics and Data Summit event ever!  Hope to see you there. 

Oracle Conference Center, Redwood Shores, CA All Analytics, All Data, No Nonsense! Analytics and Data Summit 19 is a once-a-year opportunity for Oracle technology users.   Designed for Oracle...

Innovator and educator Amit Mishra, Ph.D. presents a methodology that fuses Design Thinking and System Engineering that can increase the success of a data science project.

Innovator and educator Amit Mishra, Ph.D. presents a methodology that fuses Design Thinking and System Engineering that can increase the success of a data science project.

Finance leaders want to know how emerging technologies can help their businesses.

Finance leaders want to know how emerging technologies can help their businesses.

A random forest is an ensemble machine learning algorithm that is used for classification and regression problems. In this tutorial, learn how to build a random forest, use it to make predictions, and test its accuracy.

A random forest is an ensemble machine learning algorithm that is used for classification and regression problems. In this tutorial, learn how to build a random forest, use it to make predictions,...

Oracle Principal Data Scientist Setareh Borjian looks at the disadvantages of a pre-packaged software solution and considers the practicality of a hybrid solution.

Oracle Principal Data Scientist Setareh Borjian looks at the disadvantages of a pre-packaged software solution and considers the practicality of a hybrid solution.

Oracle Digital Assistant Architect and Software Lead Gautam Singaraju, Ph.D. gives an introduction on word embeddings along with techniques that embed higher dimensional artifacts into low dimensional vectors.

Oracle Digital Assistant Architect and Software Lead Gautam Singaraju, Ph.D. gives an introduction on word embeddings along with techniques that embed higher dimensional artifacts into low dimensional...

Oracle Data Scientist Joe Hahn presents a toy-model simulation to assess a predictive maintenance strategy that can be applied to upstream oil and gas.

Oracle Data Scientist Joe Hahn presents a toy-model simulation to assess a predictive maintenance strategy that can be applied to upstream oil and gas.

Is data science helping your company build the systems that automate operational decisions? If not, Senior Data Scientist Sebastian Neubauer believes that adopting a DevOps mentality in your data science efforts can get you a higher ROI.

Is data science helping your company build the systems that automate operational decisions? If not, Senior Data Scientist Sebastian Neubauer believes that adopting a DevOps mentality in your data...

The creative use of machine learning has made databases -- always good at tracking historical events -- much better at predicting future activity as well.

The creative use of machine learning has made databases -- always good at tracking historical events -- much better at predicting future activity as well.

Oracle Data Scientist Mats Stellwall goes over four techniques that are considered traditional machine learning, including clustering, classification, regression, and market basket analysis.

Oracle Data Scientist Mats Stellwall goes over four techniques that are considered traditional machine learning, including clustering, classification, regression, and market basket analysis.