Name: Data Science Operations and Engineering: Roles, Tools, Tips, & Best Practices
Start: 2017-02-02T18:00:00Z
End: 2017-02-02T19:03:03.000Z
Location: BrightTALK
Rating: 4.33

Hewlett Packard Enterprise (HPE) is transforming how enterprises deploy AI / Machine Learning (ML) and Big Data analytics. HPE’s container-based software platform makes it easier, faster, and more cost-effective for enterprises to innovate with AI / ML and Big Data technologies – either on-premises, in the public cloud, or in a hybrid architecture. With HPE, our customers can spin up containerized environments within minutes, providing their data scientists with on-demand access to the applications, data, and infrastructure they need.

Enterprises often make building large-scale analytics or AI / ML systems much harder than it needs to be.  Rather than trying to build around limitations in their data infrastructure, there is another way.  By using a scale-efficient system that adapts to change easily and in a cost-effective way⁠—scaling up or down, open to a wide variety of applications and tools, and with the ability to add new locations quickly⁠—organizations don't need to scale up IT resources.  In this webinar, we will discuss:  -How to tell if your system is scale-efficient -What steps you can take to improve scale-efficiency -How customers are taking advantage of scale-efficient data fabric

AI and Analytics at Scale: Lessons from Real-World Production Systems

Business-critical analytical applications depend on Hadoop ​​–​​ but technology innovations like object-based data platforms, hybrid cloud deployment, and open-source compute options (i.e., Apache Spark and Presto SQL) have emerged leaving enterprises questioning the future of their data lake investment.

Find out how to de-risk your business with a new strategy to modernize your data analytics platform – allowing you to gain more value from your data. Our experts will address the following topics:  

- Should you consider moving on from HDFS?
- What is a data lakehouse, and do you need one?
- How do you deliver security for a consistent user experience across various compute and storage components?
- How do you integrate in open-source apps to get the most value from your data? 
- What deployment strategies should be considered? On-premises, cloud, hybrid cloud, or multi-cloud?

This session highlights best practices of other enterprises who have realized there is life after Hadoop. HPE Ezmeral software and solutions can help take you there.

Is there life after Hadoop? Of course there is!

Enterprises often make building large-scale analytics or AI / ML systems much harder than it needs to be.

Rather than trying to build around limitations in their data infrastructure, there is another way.

By using a scale-efficient system that adapts to change easily and in a cost-effective way⁠—scaling up or down, open to a wide variety of applications and tools, and with the ability to add new locations quickly⁠—organizations don't need to scale up IT resources.

In this webinar, we will discuss:

-How to tell if your system is scale-efficient
-What steps you can take to improve scale-efficiency
-How customers are taking advantage of scale-efficient data fabric

Attend this webinar and receive a free copy of the O'Reilly ebook, AI and Analytics at Scale: Lessons from Real World Production Systems!

MLOps - The Data Centric Approach to AI/ML

Want to find out how you can use Kubernetes on premise and at scale? Florian Bettges, HPE Greenlake expert, will show you how to do it!

Running Kubernetes at scale on premise and from edge-to-cloud

Tune in to hear Vice President and Managing Director for CERTA region at HPE, Biljana Weber, to speak about importance of software, impact of culture and future of IT.

Biljana Weber at HPE Ezmeral Data World

Fabian Wilckens speaking at Ezmeral Data World 2021 explaining how data are at the heart of digital and business transformation. Watch to learn how to fuel your edge-to-cloud digital transformation!

Data at the heart of digital and business transformation

The ability to rapidly convert data into business decisions is a competitive differentiator with an increasing emphasis on acclerated analytics, improved data literacy, and adoption of data science, AI, and machine learning functionality. 

Investing in data, AI, and analytics products and skills​—​as well as the underlying infrastructure needed for agile application deployment​—​is the key to success. 

During this webinar featuring 451 Research, we explore what enterprises should consider when evaluating plans for data and application modernization. Topics covered will include:

-The game-changing potential of application agility
-Hybrid and multi-cloud modernization of the data estate
-The containerization of stateful workloads across the ISV ecosystem
-The role of container platforms as an accelerator of data science

View this webinar and receive a copy of the new 451 analyst report on application modernization!

Application Modernization in the Age of Insight - with 451 Research

Realizing the promise of AI/ ML remains a challenge for many organizations and they often face problems in the "last mile" of model deployment. 

HPE, along with the HPE Ezmeral ISV partners, collaborate to deliver real world solutions to unlocking the business value of data through successful AI / ML deployments at scale. 

Key topics that will be covered:

- Maximizing return on big data analytics and AI technology investments
- How to approach data analytics, AI workloads and containerization
- Customer examples of building and deploying AI workloads into production

Watch this on-demand webinar and learn how you can maximize your big data.

Deploying AI/ML workloads into production in hybrid cloud environments

How do we fast forward to the future of data science?

Meet HPE Ezmeral, a hybrid cloud platform that accelerates the exploitation of data by bringing DevOps-like speed and agility to analytic workflows with support for every stage of the analytics lifecycle.

Attend this webinar for a hands-on demo of how HPE Ezmeral software delivers:

-Access to all of your data from edge to cloud
-Self-service kubernetes deployment of your analytic tools and frameworks of choice
-Enterprise data protection and management with integrated HPE Ezmeral Data Fabric (formerly MapR)

It’s time to unlock the potential of your data.

Hands on Data Science with HPE Ezmeral

Over the past decade, cloud computing has taken the world by storm delivering self-service, agility, elastic scalability, and lower upfront costs. But, extensive on-premises information estates still exist due to data gravity issues, performance needs, security and compliance regulations, and lower cost at scale.

HPE’s Global Services IT didn’t see cloud vs. on-premises benefits as mutually exclusive. They’ve been working for years to deliver a business-enabling solution that delivers the cloud experience, on-premises. After testing multiple technologies, they found the right solution using HPE Ezmeral.

In this session, we will share how HPE Global Services IT:

- Pivoted to be being a business enabler delivering the full cloud experience on-premises
- Enabled multiple tenants and varied use cases; including advanced analytics, log management, Kubernetes as a service, and elasticity on demand
- Gained operational efficiencies running and managing infrastructure and improved overall utilization

Learn how you can provide the cloud experience in an on-premises environment saving you money, resources and time!

Delivering a Cloud Experience in an On-Premises Environment

Your business-critical analytical applications depend on Hadoop - but the technology innovations in data management, data analytics, and data science have accelerated in recent years. Data platform and deployment alternatives have emerged, and you may be questioning the future return on your Big Data investment. Many enterprise organizations are reevaluating their path forward, embarking on a new strategy to modernize their Big Data estate.

Attend this industry roundtable to find out how to de-risk your business with an open and modern Big Data analytics strategy that allows you to:

• Leverage containerization to deliver immediate infrastructure efficiencies and a consistent cloud experience for data analytics and data science
• Get more value from your data by embracing best-of-breed apps and the open source ecosystem  of new technologies
• Expand your Big Data information estate from edge-to-cloud, with hybrid-cloud and multi-cloud flexibility
• Learn from the best practices of other enterprises, so you can achieve these benefits and efficiencies for your organization

We hope you can join us and see how you can move beyond Hadoop, to modernize your Big Data estate with HPE Ezmeral software and solutions.

A New Era: Modern Big Data Solutions Roundtable with HPE Ezmeral

Edge and IoT promise great benefits, but deployment challenges​—​including real-time analytics, connectivity, and space constraints​—​are limiting the technology’s full potential. 

The HPE Ezmeral Data Fabric team (formerly MapR) has perfected an edge-to-cloud data fabric that allows you to act locally and learn globally. 

This vision and technology provides secure local processing, quick aggregation of insights, and the ability to push intelligence back to the edge​—​providing a faster and more significant business impact by delivering data analytics where and when you need them. 

Join us for this session as we:
- Detail the typical paths of data as it flows from creation to analysis
- Highlight how companies in all industries use HPE’s technology to power mission-critical Edge and IoT solutions
-Demo HPE Ezmeral Data Fabric’s key features for edge-to-cloud capabilities

Don't miss this session!

Stretching HPE Ezmeral Data Fabric from Edge-To-Cloud

Enterprises are facing challenges with operationalizing their ML models as they move from PoCs to production. 

The emerging field of ML Ops – machine learning operations – aims to deliver agility and speed to the ML lifecycle similar to what DevOps processes have done for the software development lifecycle. 

In this webinar, we will discuss how to:
- Overcome the barriers of deploying and operationalizing ML models
- Gain faster time-to-value, increase productivity, and reduce risk with a flexible end-to-end ML Ops solution
- Deploy and access data more efficiently whether on premises, in the cloud, or a hybrid environment

Join this webinar to learn how Dataiku and HPE are bringing speed and agility to the ML lifecycle.

Speakers: 
- Sandeep Deshmukh, Product Manager ML Ops at Hewlett Packard Enterprise
- Dietrich Zinsou, Senior Solutions Architect at Hewlett Packard Enterprise
- Daniel Hladky, Senior Partner Manager at Dataiku

Please be aware that by registering for this webinar, you agree to have your personal information shared with both partners Dataiku and Hewlett Packard Enterprise. They may contact you with information that could be of interest to you.

End-To-End Operational ML with Dataiku and HPE Ezmeral ML Ops

Join this webinar with Hewlett Packard Enterprise and TWIML ​–​ a community and podcast focused on AI and Machine Learning (ML) ​–​ as we explore the use of Kubernetes for AI / ML deployments in the enterprise. 

As organizations mature in their use of AI and ML, they need to build repeatable, efficient, and sustainable processes for model development and deployment. Containers and Kubernetes provide essential building blocks to help operationalize these processes and support ML Operations (MLOps). 

In this webinar, we will discuss:
-The challenges of moving from pilot to production with Machine Learning and Deep Learning, at enterprise scale
-How containers and Kubernetes can help address these challenges, as a foundation for MLOps
-Lessons learned and best practices from enterprises who have successfully leveraged Kubernetes for AI / ML

Register today and you'll receive the accompanying TWIML e-book on deploying Kubernetes for Machine Learning, Deep Learning, and AI.

Kubernetes for AI, Machine Learning, and Deep Learning

Watch this on-demand webinar to learn how to bring DevOps agility to data science and big data analytics.

It’s no longer just about building a prototype, or provisioning Hadoop and Spark clusters. How do you operationalize the data science lifecycle? How can you address the needs of all your data science users, with various skillsets? How do you ensure security, sharing, flexibility, and repeatability?

In this webinar, we discussed best practices to:

-Increase productivity and accelerate time-to-value for data science operations and engineering teams.

-Quickly deploy environments with data science tools (e.g. Spark, Kafka, Zeppelin, JupyterHub, H2O, RStudio).

-Create environments once and run them everywhere – on-premises or on AWS – with Docker containers.
 
-Provide enterprise-grade security, monitoring, and auditing for your data pipelines. 

Don’t miss watching this webinar. Learn about data science operations – including key roles, tools, and tips for success.

Data Science Operations and Engineering: Roles, Tools, Tips, & Best Practices

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Data Science Operations and Engineering: Roles, Tools, Tips, & Best Practices

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