Machine learning (ML) is on the rise at businesses hungry for greater automation and intelligence with use cases spreading across industries. At the same time, most projects are still in the early phases. From selecting datasets and data platforms to architecting and optimizing data pipelines, there are many success factors to keep in mind. The advantages that ML offers organizations—the ability to automatically build models that can analyze huge volumes of data and deliver lightning-fast results—have also led to a growth in the availability of both commercial and open source frameworks, libraries and toolkits for engineers. Attend this workshop for a hands-on course in the enabling technologies, techniques, and applications you need to know to succeed in today’s environments.

DataOps has emerged as an agile methodology to improve the speed and accuracy of analytics through new data management practices and processes, from data quality and integration to model deployment and management. By leveraging automation, data democratization, and greater collaboration among data scientists, engineers, and other technologists, DataOps can help organizations improve the time-to-value of their data. Attend this workshop to hear about the key supporting technologies, real-world strategies, and success stories and how to get started on your DataOps journey.

Data science, the ability to sift through massive amounts of data to discover hidden patterns and predict future trends, may be the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. Extracting actionable knowledge from all your data to make decisions and predictions requires a number of skills, from statistics and programming to data visualization and business domain expertise. Attend this workshop for a deep dive into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.

A new era of cognitive computing is unfolding, and its impact is already being felt across industries, from preventative maintenance at manufacturing plants and patient diagnosis at hospitals to the rise of sophisticated chatbots ready to assist us across the connected world. The goal of cognitive computing is straightforward: to simulate human thought processes in a computerized model. However, building cognitive systems and applications that can perform specific, humanlike tasks in an intelligent way is far from easy. Attend this workshop to get a full understanding of how cognitive computing works, popular use cases, and best practices IT leaders and practitioners can apply today.

Stonebraker focuses on the current market for Big Data products, specifically those that deal with one or more of “the 3 V’s.” On the one hand, the Volume problem for business intelligence applications is pretty well solved by data warehouse vendors. However, upcoming data science tasks are poorly supported at present. On the other hand, there is rapid technological progress, so we need to stay tuned. In the Velocity arena, recent “new SQL” and stream processing products are doing a good job, albeit with some storm clouds on the horizon. The Variety space has a collection of mature products, along with considerable innovation from startups. He identifies opportunities, particularly those enabled by possible disruption from new technology. And then there’s that 800-pound gorilla in the corner.

While organizations have dramatically more data readily available, few are leveraging this data for true competitive advantage. McKinsey found that data-centric companies are driving a 9-fold increase in customer loyalty and an almost 20-fold increase in customer profitability. As data and analytics leaders, you have the opportunity to drive organizational focus on identifying, curating, and leveraging valuable data to support better strategic decision making. Levitt shares information management frameworks and anectotes that highlight the value of thinking outside box, discusses critical success factors, and recommends specific actions to improve your organization's competitive advantage.

Lynda Partner, VP Analytics at Pythian and her team of big data and analytics professionals work to solve the toughest data challenges for their clients. As veterans in the continually-evolving big data space, her team has been helping clients break down their data silos by bringing together data from disparate sources, and enabling use cases from BI to ML. When it comes to big data they’ve seen it all. In this session, you will hear how data professionals like you are dealing with the most challenging issues with data and keeping up with innovations. Learn through real-world examples how you too can be ready to address tomorrow’s opportunities with ever-advancing cloud analytics technologies and emerging practices, — in other words, what’s coming and how can you maneuver today to take advantage.

Artificial intelligence (AI) had the potential to completely revolutionize how we do business and increasingly affects people’s daily lives.

Depending on who you talk to, AI will either enable massive productivity gains from your employees or replace them entirely. Hype aside, AI is coming, and companies need to understand how to harness it. Despite the promise of “plug and play” technology, real AI requires varying degrees of information architecture (IA), knowledge engineering, product and content architecture, and high-quality data sources to be effective. 

The power of machine learning is particularly evident when used to predict events in the real world.

Machine learning (ML) has become top of mind for many businesses. Wilde shares his experience and insights about ML in the real world.

The customer experience (CX) is prime territory for employing AI technologies.

Let’s take a look at enhanced customer experiences for hospitality consumers delivered via virtual concierge services that provide resort guests with personal and contextual hospitality services. Built upon mobile use and location data, bot services help customers with a variety of services, ranging from in-room amenities and dinner reservations to event tickets. Don Spaulding provides an overview of the technical solution and powerful results of this virtualized solution.

The combination of Big Data with AI technologies creates both challenges and opportunities. Identifying the factors that turn challenges to opportunities leads to success.

With edge computing becoming a thing, AI on-the-edge is quickly following suit. It unlocks a whole new world of possibilities, including predicting customer needs before they even know them. But edge AI seems like it’s only a game for the most cutting-edge companies like Apple, Amazon, or Tesla, to name a few. Traditional enterprises aren’t really embracing it out of fear it may cost too much or due to uncertainty about the potential ROI. To tap into this opportunity, organizations don’t need to choose a risky “all in” approach; a small iterative approach reduces the risk while ensuring your edge AI projects aligns with your overall business strategy. Join this session to learn how to apply a Minimal Viable Prediction (MVP) approach to your next edge AI project.

Everyone’s talking about machine learning, but we hear much less about how to put it into practice.

Only 10 years ago, you needed access to extensive academic and computing resources to make use of machine learning (ML). Fast-forward to today, and we’ve seen revolutionary changes in the hardware and software that are making ML accessible for any developer or data scientist. Whether you’re completely new to ML or you’ve already trained and deployed your own model from scratch, Google Cloud Platform has a variety of tools to help you start using ML right now. Sara Robinson starts with the basics: how to use a pre-trained ML model with one REST API call. Then she explains how to use your own dataset to customize a pre-trained model with transfer learning, how to build your own model from scratch with TensorFlow, and how to train and serve it in the cloud with GCP.

Learn about next-generation cloud data warehousing and what it takes to deliver insights from real-time data economically and at scale with hybrid data regardless of location, in the cloud, on-premises or both, while you enjoy Boston's finest deep fried pastries!

Elsevier is a 130 year old company with a long legacy of providing quality content that serves professionals in their fields. Its digital transforming is aimed at transforming that content into actionable intelligence for users. However, digital transformation is not just a matter of updating legacy systems and workflows, it also requires the capacity to transform the entire business; from pricing models to company culture. In this talk, Deus explores Elsevier’s journey to transform its business so it is able to incorporate advanced technologies like machine learning and AI technology to better serve customers.

We find ourselves continuously copying, transforming, and aggregating data into various large scale, complex, proprietary systems for the purpose of Digital Transformation in order to gain some competitive edge. Unfortunately, this process ends up exacerbating the problem by creating more silos, complexity and making the process of extracting knowledge more difficult overall. Deyette reviews where we are, how we got here, and what must come next with regard to leveraging Big Data Analytics.

There are many ways to implement ML projects, but not all of them work out as planned.

EnCata’s work is organized according to lean principles, which mean reducing delivery time and eliminating time and money losses, connected with knowledge transfer and materials logistics. We used machine learning to create a neural network as a means of monitoring the working tool in production.

Deep learning brings many opportunities that businesses can use to augment current processes.

Using ML-based deep neural networks for prescriptive analytics via recommendations, followed by predictive analytics using the results of recommendation is the focus of Lakshman Bulusu’s presentation. He explains the pragmatics of using R and provides an industry use case as a proof of concept about how business can leverage this to augment BI at an enterprise level. He includes a demonstration of how AI Meets BI via integration with a BI platform using Oracle/Tableau.

Think big, start small, and just get going is an excellent approach to take full advantage of AI’s transformational technologies.

AI is already having a significant impact for the U.S. government, including Defense and Intelligence Community use cases, and is also providing game-changing capabilities for global enterprises in a range of industries, including financial services, life sciences, and technology. Guarino provides real-world examples of how AI is driving measurable benefits in a range of industry sectors. She discusses the importance of Explainable AI to regulated industries like financial services and healthcare, where being able to justify the reasoning behind algorithmic decisions is essential.

As we move into a new computing era, technology has outstripped society in its ability to upend labor markets and industries, as well as personal lives.  This panel discussion by researchers and industry experts, and moderated by the Cognitive Computing Consortium, presents a number of ethical and legal issues that computing advances have raised.  Areas covered include: security, privacy, decision making by humans vs. machines, self driving vehicles, effects on labor markets and industries.  Presenters will avoid hype and discuss practical approaches to these issues.

Companies are not lacking in technology options; in most cases, more advanced technologies exist than can be absorbed into the organization all at once. As data and analytics leaders, you need to determine how to make the biggest business impacts with advances in AI and ML for analytics, enable self-service with governance, and support BI and data engineering processes with the data lake. To establish this solid enterprise foundation, it is essential to recommit to data management principles and prioritize platform technologies and ecosystems accordingly. In his closing keynote, O’Brien shares a clear path forward based on the evolving concepts in data and analytics within the context of an enterprise-scale data strategy.