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

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!

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.

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!

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!

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.

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.

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!

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.

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!

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.

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.

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.

Join this webinar with HPE and guest speaker Forrester Research to learn about the key technological and organizational challenges that impact the success of machine learning (ML) projects.

As enterprises operationalize their ML models, they often face problems in the "last mile" of model deployment. The emerging field of ML Ops 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'll discuss:

-Forrester's latest findings on the state of machine learning in the enterprise
-The 7 key requirements for successful ML Ops deployment
-Organizational best practices to improve productivity of data science teams

Register today and you'll receive the accompanying research report too!

There are a multitude of distributed, scale-out data management technologies in the marketplace; but the key is finding a solution that supports a wide range of analytic applications across diverse data types without time-consuming data movement and transformation.

A new IDC report details how the MapR Data Platform provides a single platform environment for collecting, curating, and analyzing data.

In this webinar, we'll share these findings and show how MapR provides:

-A 5-year ROI of 567% and 11 month payback period
-Analytics improvements and team efficiencies
-Improved cost of operations and higher productivity

Register today to learn how you can improve the value of your data. You'll receive the full report too!

Organizations are using advanced analytics, machine learning, and deep learning for competitive advantage and have turned to GPUs to boost performance and speed innovation. But these valuable compute resources can take months to deploy and are often underutilized.

This webinar will discuss how to enable a cloud-like experience to drive maximum ROI from GPU-accelerated compute for enterprise AI and data science.

Learn how you can:

-Easily share GPU resources to improve utilization and data science productivity.
-Accelerate model development with the performance of bare metal.
-Shorten time-to-value provisioning a GPU environment in minutes instead of months.

Register for this webinar and receive a new benchmark paper from analyst firm Enterprise Strategy Group (ESG) which details how to get more value from your GPUs.

Join our webinar with an industry analyst from Enterprise Strategy Group (ESG), and learn how you can accelerate your AI initiative.

Enterprises in all industries are recognizing the game-changing business impact of Artificial Intelligence (AI) and Machine Learning (ML).

To be successful in your AI initiative, your data science teams need the ability to quickly build and deploy ML models in large-scale distributed environments. But this is easier said than done.

In this webinar, we'll share industry research from ESG and discuss how to:

- Address the skills gap in data science and AI / ML
- Utilize containers to accelerate your AI / ML deployment
- Run AI / ML workloads on either on-premises or in the cloud
- Achieve faster-time-to value for your AI initiative

Don't miss this informative webinar!

Watch this webinar to learn how you can accelerate your deployment of H2O and AI / ML in Financial Services.

Keeping pace with new technologies for data science, machine learning, and deep learning can be overwhelming. And it can be challenging to deploy and manage these tools – including H2O and many others – for data science teams in large-scale distributed environments.

This webinar will discuss how to deploy H2O and other ML / DL tools in Financial Services. Learn about:

-Example use cases for AI / ML / DL in Financial Services
-Using H2O and other ML / DL tools with containers
-Overcoming deployment challenges for distributed environments
-How to ensure enterprise-grade security, high performance, and faster-time-to-value

Join this webinar to learn how you can accelerate innovation using AI / ML / DL in Healthcare and Life Sciences.

Healthcare professionals and researchers have access to immense volumes of data from a variety of sources. Early adopters of Machine Learning (ML) and Deep Learning (DL) are uncovering new insights from this data to improve patient care and transform the industry with AI-driven innovations.

But it can be challenging to deploy and manage these tools – including TensorFlow and many others – for data science teams in large-scale distributed environments.
In this webinar, we'll discuss:

- Example AI use cases – including precision medicine, drug discovery, and claims management

- Data access, data security, and other key requirements for implementing AI in Healthcare and Life Sciences

- How to overcome deployment challenges for distributed ML / DL environments using containers

- How to ensure enterprise-grade security, high performance, and faster-time-to-value for ML / DL

Don't miss this webinar. Register today!

Join this webinar to learn how you can accelerate your deployment of TensorFlow and AI / ML in Financial Services.

Keeping pace with new technologies for data science, machine learning, and deep learning can be overwhelming. And it can be challenging to deploy and manage these tools – including TensorFlow and many others – for data science teams in large-scale distributed environments.

This webinar will discuss how to deploy TensorFlow and other ML / DL tools in the Banking, Insurance, and Capital Markets industries. Learn about:

-Example use cases for AI / ML / DL in Financial Services – with an enterprise case study

-Using TensorFlow and other ML / DL tools with GPUs and containers

-Overcoming deployment challenges for distributed environments – including operationalization

-How to ensure enterprise-grade security, high performance, and faster-time-to-value

Don't miss this webinar. Register today!