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Cloudera

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  • Challenges and Opportunities for Scaling Enterprise AI and Machine Learning
    Challenges and Opportunities for Scaling Enterprise AI and Machine Learning
    Hilary Mason - GM, Machine Learning Cloudera Recorded: Dec 5 2018 54 mins
    Machine learning and artificial intelligence are exciting technologies, but success means excitement about business outcomes, not technology. Could success make AI boring? In a world where hundreds or thousands of automations are rapidly and repeatedly deployed, operating silently across a business, we imagine today's hype around these technologies dying altogether - as with electricity before them.

    We'll cover:
    - Current challenges and opportunities for scaling enterprise machine learning across many dimensions, including people, process, strategy, and technology

    - What’s required to make the process of building and deploying machine learning models automated, repeatable and predictable

    - A vision for an enterprise AI platform that turns data into predictions, at any scale, anywhere
  • Making Self-Service BI a Reality in the Enterprise
    Making Self-Service BI a Reality in the Enterprise
    Romain Rigaux Lead Software Engineer, Cloudera Recorded: Nov 15 2018 56 mins
    For most analysts, the pace of analytics and data science can be frustrating. The common waterfall approach works well for the fixed reports, but it can be a lengthy process to request additional data sets, create new reports, or serve new use cases. So it’s no surprise that organizations are looking to shift towards a self-service model, empowering business users to discover and iterate quickly.

    However, it’s not just about opening up this access, but also ensuring the results are accurate and trusted. When there are petabytes of data, how does a user know which tables to use and which are most relevant? How do you strike the balance between discovery and agility, while still meeting enterprise governance standards to truly get more value from your data?

    During this webinar, you’ll learn how to empower end-users to make self-service BI a reality within your organization while fostering governance collaboration between all data stakeholders. We’ll discuss and demo:

    - Strategies of consolidating data across silos for fast, flexible access
    - Enabling easy discovery and exploration, including understanding which data to trust and where to start
    - New capabilities for intelligent query assistance as well as immediate performance optimizations and recommendations as-you-go
    - Collaboration and access outside of just SQL for data science and beyond

    In addition, we will walk through best practices and considerations when developing your organizational strategy around self-service analytics, and highlight several real-world success stories from a wide range of industries.
  • Cloudera Enterprise 6.0 Update: GA and Beyond
    Cloudera Enterprise 6.0 Update: GA and Beyond
    Matthew Schumpert, Product Management Director Recorded: Nov 7 2018 56 mins
    Cloudera Enterprise 6.0 is now available and provides a major upgrade to our modern platform for machine learning and analytics with significant advances in productivity and enterprise quality.
    We have tuned compute resources to maximize performance and minimize total cost of ownership (TCO). We also updated and tightly integrated a range of open-source projects including Apache Spark for data science, Apache Kafka for streaming data pipelines, and Apache Solr for search. Scalability, security, governance, and management capabilities have all been enhanced to provide the safest and easiest enterprise-grade platform to power multi-disciplinary analytics for your business in the cloud or on-premises.
  • Powering Possibilities in Machine Learning and Advanced Analytics
    Powering Possibilities in Machine Learning and Advanced Analytics
    Wim Stoop, Senior Product Marketing Manager EMEA, Cloudera, Dr. Chris Royles, Systems Engineer, Cloudera Recorded: Apr 13 2017 49 mins
    Machine learning is all about the data, but it's often out of reach for analytics teams working at scale. Cloudera customers such as Wargaming.net can store, process and analyse 550 million events each day to help them improve gamers’ experiences and increase their customer lifetime value.

    Whether you are new to machine learning and advanced analytics, or you already take advantage of the possibilities, this session will explore practical examples and give you some new ideas to take away. Discover how enterprise organisations can accelerate machine learning from exploration to production by empowering their data scientists with R, Python, Spark and more in one unified platform.

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