Machine learning is changing the way organizations look at analytics. Data scientists are being recognized as a key component in organizational analytics, but management often doesn't understand their work or know how to effectively manage them.
Many businesses understand that analytics has moved beyond the data warehouse, and are pushing analysts and IT to grab and analyze data from new sources, even though they may not be ready to derive business value from it.
Open source is seen as the path to machine learning innovation, despite challenges with deployment and approachable user interfaces. For organizations using or looking to adopt machine learning techniques, moving forward may be a challenge and measuring success even trickier.
In this webcast, we will:
-Discuss how different organizations are finding success with machine learning.
-Look at how organizations are feeding the creativity of data scientists, making analytics accessible to business experts, and pushing the analytics closer to the data.
-Identify how organizations are automating analytics processes in order to free up time for new analytics, new data and new business problem domains, ultimately creating real competitive advantage.
The rise of social learning has created a new landscape for L&D professionals to explore. This webinar will examine the foundations of Social Learning and discuss how our learners are able to adapt and thrive in this space - mastering the technology and understanding the facets of Social Leadership and ways we engage as part of online communities.Read more >
In this webinar, Dr. Griffin Fernandez from The Educe Group will share best practices for implementing and driving adoption of learning management systems within colleges and universities. You’ll also hear from campus leaders at two top universities as they share their experience with implementing learning management systems. Sondra Hornsey, HIPAA Privacy Officer at Washington University in St. Louis, will discuss the human resources training requirements that drove the institution’s decision to implement Saba Cloud, while Dr. Michael Blayney, Executive Director of Research Safety at Northwestern University, will focus more narrowly on the unique requirements associated with university research compliance. Each will discuss the challenges that led them to seek out a learning management system, the planning and vendor evaluation processes, and how they have implemented Saba Cloud to achieve institutional goals and improve university business processes.Read more >
In this webinar you’ll learn why (and how) leading companies are moving their learning strategies from "push" to "pull" models, enhancing the employee experience while reinforcing standards for critical topics like safety and compliance. L&D leader Lauren Clarke of Delaware North will also share her organization’s content curation journey and how they successfully implemented a pull learning strategy.Read more >
Saba Summer School Series:
Taking a collaborative approach to learning can help you better connect, engage, and retain your workforce, while at the same allowing you to achieve greater ROI from your L&D investments. In this webinar, you’ll discover why learning is most effective when it’s collaborative and ways you can foster a culture of collaborative learning within your organization.
The human brain makes it look easy. What our eyes see, we decode immediately and effortlessly. But is it that simple? In truth, how we process images is staggeringly complex. Inspired in part by our remarkable neurons, deep learning is a fast-growing area in machine learning research that shows promising breakthroughs in speech, text and image recognition. It’s based on endowing a neural network with many hidden layers, enabling a computer to learn tasks, organize information and find patterns on its own.
Recently, SAS took on a classical problem in machine learning research, the MNIST database, a data set containing thousands of handwritten digit images. Learn how we did – and what it reveals about the future of deep learning.
In this 30 min webcast you will get an overview of how SAP Hybris enriches the digital transformation plus a detailed walk through of how you can get enabled. Join us to see how simple it is through our personalised learning maps.Read more >
Learn the way you live, Love the way you learn!
State of the art learning management system brought to you by SAP SuccessFactors
Artificial Intelligence is definitely the buzzword for 2016, and there is a good chance it will continue to be a hot topic in 2017. However from an operational standpoint, there are still no reference implementation of an integration data science pipeline applied to Deep Learning.
In this webinar, we will discuss the challenges faced by data scientists when assessing which solutions will best suit their needs. We will then show how data science teams can leverage Juju to rapidly model various alternatives, making the decision making process much more agile.
Already strategic partners, Pivotal Software and Hortonworks deepened their relationship in Spring 2016 with the goal of providing enterprises the most complete modern data platform for advanced analytics and machine learning. As part of the expanded relationship, Hortonworks has introduced Hortonworks HDB, the market’s leading Hadoop Native SQL database and big data SQL machine learning engine based on Apache HAWQ and Apache MADlib (incubating).
Join Pivotal’s Jeff Kelly and Hortonworks’ Ian Fyfe in this webinar to learn more about:
• Advantages of Hortonworks HDB
• How it works
• When to consider HDB vs. Apache Hive
• HDB industry use cases
One of the main benefits of Machine Learning is being able to analyse a large amount of data at the speed and efficiency that would require a huge team of humans. This is something that has proven to be very necessary in the Financial Services industry, where insurance companies, banks, and lenders need actionable insights quickly.
Join this panel where we will discuss:
-Why is Machine Learning such a hot topic? What are the benefits/challenges?
-What is needed to do Machine Learning right?
-Case studies of how Machine Learning is helping financial institutions — better customer experience, faster actionable insights
-How ML is able to spot trends and patterns to mitigate risk
Machine learning continues to be a hot topic for organizations looking to get more accuracy from their analytic models. But there are several practical issues that should be considered in applying it to real-world industry problems.
In this this webinar, Wayne Thompson of SAS delves into those issues and provides an overview of machine learning, as well as key business applications of this technique, including fraud detection, model factories and recommendation systems.
-Learn about four focus areas of machine learning: unsupervised learning, supervised learning, semisupervised learning and reinforcement learning.
-Get useful tips on feature engineering, ensemble modeling, bias variance, shrinkage and model evaluation.
-Get insight into the future of machine learning, including cognitive computing and robot learning.