Posts

• Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, Part 4

  In Part 1 of this series, we wrote about our goal to explore a use case and use various machine learning platforms to see how we might build classification models with those platforms to predict flight cancellations. In Part 2, we started our exploration with IBM SPSS Modeler and APIs from The Weather Company. Part 3 looked at doing this with Watson Studio in a python Jupyter notebook. In Part 4 we look at using the new SPSS Modeler flows in Watson Studio.

• Football Fan Sentiment

  In August, students from Yale's Center for Customer Insights presented their findings from a study that used various IBM Analytics tools to analyze social media data from NFL fans. This data was used to gauge fans’ perceptions of the league and popular topics surrounding it and to then use these insights to drive effective social media interactions.

• Leveraging Watson’s Machine Learning GPUs to accelerate your Deep Learning project in Python

  Use GPUs from the Watson Machine Learning Service for any deep learning project in Python

• Statistical Modeling and Machine Learning Applications for Time-Series Problems

  Different ways to work with time series use cases

• Reinforcement Learning: The Business use-cases Part 2

  Reinforcement Learning in Trading

• Reinforcement Learning: The Business use-cases Part 1

  First blog of the Reinfocement Learning: The Business Use-cases series; Introduction to Reinforcement Learning

• Predictive Maintenance Scheduling with IBM Data Science Experience and Decision Optimization

  How to keep your assets healthy and repair costs low by scheduling maintenance at exactly the right time

• DSX, GitHub and forking

  Forking a DSX project in GitHub creates a greater separation between users and allows for a more flexibility in merging assets. A greater separation allows DSX users to commit to their linked GitHub repository frequently without immediately impacting other users of the project. Assets are merged between users explicitly using GitHub pull-requests and can only be done when and if the owner(s) of the repository agree. Solving merge conflicts can be performed outside of DSX, either on GitHub or using any of the available Git tools on your local workstation.

• Using Machine Learning to Predict Outcomes for Sepsis Patients

  Sepsis is a complex disease that is difficult to identify and treat. IBM and Geisinger Healthcare System partnered to build a model to predict all-cause mortality of Sepsis patients, which could guide medical teams to closely monitor and take preventive measures for those patients with high probability of death.

• GitHub for DSX projects

  Linking a DSX project to a GitHub repository has a number of advantages: backup of your code and assets, easy migration to new DSX clusters, working on multiple clusters simultaneously, working off-line on your local work station, sharing your code, collaboration, etc. This post contains step-by-step instructions of the setup and work flow for the GitHub integration in DSX.

• The Journey to Digital: Part 3, Insight Transformation

  What needs to be done differently at this stage? This is about mapping out the decisions people make across the enterprise. You’ll use this full map of those decisions to create a backlog of decisions for your data science teams to tackle. Then to prioritize the backlog, you’ll assign a value to each decision, taking into account the likelihood and ease of implementation.

• Using Excel to manage scenarios in Decision Optimization for DSX

  Excel can be used to manage scenario data consisting of multiple tabular data sets in one file. This allows for an easier creation of scenarios for what-if analysis using the Decision Optimization add-on for DSX.

• Don’t Let Data Science Become a Scam

  Companies have been sold on the alchemy of data science. They have been promised transformative results. They modeled their expectations after their favorite digital-born companies. They have piled a ton of money into hiring expensive data scientists and ML engineers. They invested heavily in software and hardware. They spend considerable time ideating. Yet despite all this effort and money, many of these companies are enjoying little to no meaningful benefit. This is primarily because they have spent all these resources on too much experimentation, projects with no clear business purpose, and activity that doesn’t align with organizational priorities.

• The Journey to Digital: Part 2, Data Transformation

  This stage is about defining the core assets that create value for the enterprise, and it’s about identifying, discovering and governing the right data without necessarily expecting — or forcing — upheaval. At this stage, data consumers might ask for data to support their preconceived notions. That’s actually fine. More often than not, aligning with existing expectations is a necessary step as you build consensus for data science to transform the organization.

• The Journey to Digital: Part 1, Table Stakes

  Effectively transforming a company requires a commitment to do things differently, and requires that your partners and vendors do things differently too. To my mind, these days transforming a company means transforming it digitally and any successful digital transformation requires three things. I think of these as the minimum requirements for getting a seat at the table, the table stakes if you will:

• Raiders of Every Industry: Journey to Digital

  Every industry will be disrupted in the coming years. None are safe. The safer they seem, the more susceptible they probably are. In fact, only two things stand a chance of protecting incumbents:

• Hybrid Use Cases Dominate Machine Learning - Part 2

  [Part 2] The importance of information architecture in meeting enterprise AI needs, as well as how ML can be applied in hybrid scenarios

• Hybrid Use Cases Dominate Machine Learning - Part 1

  [Part 1] The importance of information architecture in meeting enterprise AI needs, as well as how ML can be applied in hybrid scenarios

• How IBM builds an effective data science team

  Data Science is a team sport. While I’m not sure where this phrase was coined, it is an accurate phrase. This must be ringing true with enterprises as well since we often get the question: “What should the structure of a data science team be?” and “Where should a data science team report into?” The first question is probably a little more straight forward and that is what we will address here. The answer to the second question really is it depends on your organizations maturity in this space, but is should be some sort of federated or hub and spoke model eventually. Breaking down what is required to successfully execute a data science project and acknowledging that very few individuals possess all these skills helps to define what a team should look like.

• 3 Scenarios for Machine Learning on Multicloud

  More and more cloud-computing experts are talking about “multicloud”. The term refers to an architecture that spans multiple cloud environments in order to take advantage of different services, different levels of performance, security, or redundancy, or even different cloud vendors. But what sometimes gets lost in these discussions is that multicloud is not always public cloud. In fact, it’s often a combination of private and public clouds. As machine learning (ML) continues to pervade enterprise environments, we need to understand how to make ML practical on multicloud — including those architectures that span the firewall. Let’s look at three possible scenarios.

• Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, Part 3

  In Part 1 of this series, we wrote about our goal to explore a use case and use various machine learning platforms to see how we might build classification models with those platforms to predict flight cancellations. Specifically, we hoped to predict the probability of the cancellation of flights between the ten U.S. airports most affected by weather. We used historical flight data and historical weather data to make predictions for upcoming flights. In Part 2, we started our exploration with IBM SPSS Modeler and APIs from The Weather Company. With this post, we look at IBM’s Data Science Experience (DSX).

• What IBM looks for in a data scientist

  Job seekers sometimes ask how IBM defines “data scientist.” It’s an important question since more and more would-be data scientists are fighting for attention in an increasingly lucrative labor market.

• So what exactly is IBM doing different with machine learning?

  Machine Learning itself is not new. The concepts have been around for decades, and many companies have been building ML models and doing predictive analytics for a while. So what exactly is IBM doing in this space?

• Tools for data science: Using the right ones for the job

  You’ve probably heard the sayings many times: “You’re only as good as your tools.” and “Use the right tool for the right job.” These have never been truer than in the world of data science and machine learning.

• Exploring insights on sepsis from medical literature

  Today, many industries are using machine learning to empower their businesses and solve unique challenges. And it’s not just businesses. The medical field is using machine learning to solve problems for the general betterment of humanity. Let’s walk through a medical use case on an infectious disease, sepsis, where we’ll use an unsupervised learning technique to discover interesting insights.

• Text Analytics: Finding Insights in Scientific Publications

  Can you read fast? Really, really fast? As you start reading this blog, consider that a person reads an average page of text in about 2 minutes, so it could take you about 10 minutes to read this whole post, more or less. Now imagine reading 10 to 20 pages of a scientific paper. Next imagine reading hundreds, thousands, or even millions of such papers. Not an easy — if even feasible — task for a single person or even for a group of avid readers. And even if a group of people can read many scientific publications in a reasonable time, how would they then combine their acquired knowledge and establish correlations between articles and terms of interest, find common patterns related to a specific subject, and so on?

• Hello, Watson! A Bot-Building Tutorial

  Whether we are part of a large business, a small startup, or a non-profit organization, there’s one objective that tends to arise when we seek to design and improve our organizations: providing our customers and users with an engaging experience. Although there are many ways to approach this objective, one of the recent trends to accomplish this is the rise of chatbots.

• Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, Part 2

  As we described in Part 1 of this series, our objective is to help predict the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. We use historical flights data and historical weather data to make predictions for upcoming flights.

• Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, Part 1

  This is the first in a series of blog posts where we’ll explore a use case and a few different machine learning platforms to see how we might build a model, using platforms that can help predict flight cancellations. In part one, we’ll talk about the use case, how and why we limited the scenario, and about the data we gathered to start the data science / machine learning process.

• Ingest and Analyze Streaming Event Data at Scale with IBM EventStore

  In the connected world of today’s digital economy, apps, IoT devices, vehicles, appliances and servers are generating endless stream of event data. Event data is typically in small size per event but streams in at high speed generating huge volumes over time. The stream of events describes what is happening over time and offers the opportunity to track and analyze things as they happen. With access to complete sets of event data, businesses can derive critical insights from comprehensive analysis on all the data.

• Walk Through a Data Scientist’s Experience with a Retail Use Case

  Data Scientists are compared to Unicorns, a rare and mythical creature with great powers. While one can find folks with machine learning skills, it’s extremely rare to find one who’s also a domain expert with a tremendous understanding of the business. The business landscape is changing fast today. One example is retail, an industry that is seeing tremendous disruption due to the shift from brick-and-mortar to online buying. Machine learning is heavily used in this industry to augment the customer buying experience with individualized just-in-time cross-sell and up-sell offers. Let’s walk through a retail use case. We’ll start by identifying the business problem and then we’ll use data science to find a solution.

• Machine Learning & Apache Spark: A Dynamic Duo

  The Machine Learning revolution is underway and is changing industries and delivering outcomes that were unimaginable a few years ago. In this video of John J. Thomas’s keynote at ApacheCon on May 17, 2017, learn how Apache Spark and other related projects are being used by innovative companies to remake products and services and enabling data-driven decision making.

• Six Steps Up: From Zero to Data Science for the Enterprise

  Data has intrinsic value to the enterprise, but how to quantify these data assets has been a struggle for many organizations and for many enterprises as they establish modern data practices and data organizations. In most organizations, data in and of itself doesn’t have intrinsic value. The data’s value emerges only after we build platforms for data science.