Modernize your data practices with DataOps, MLOps, and FinOps
By: Informatica View more from Informatica >>
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Ultimate guide to machine learning operations
By: DataBricks
Type: eBook
If you were to break down machine learning operations (MLOps) into a formula, this e-book by Databricks explains, it would the following: MLOps = DataOps + DevOps + ModelOps.
Now that you know what MLOps is, why should you care about it? Along with answering that question, The Big Book of MLOps explores:
- The guiding principles of machine learning operations
- What’s new with MLOps
- An MLOps perspective on large language models
- The elements of LLM-powered applications
- And much more
Dig into the 78-page ultimate guide to unlock the complete insights.
These are also closely related to: "Modernize your data practices with DataOps, MLOps, and FinOps"
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New Gartner Magic Quadrant for data integration tools released
By: Informatica
Type: Gartner Research Report
The Gartner Magic Quadrant for Data Integration Tools evaluates 20 vendors aiding organizations in combining data from multiple sources. Vendors are categorized as Leaders (AWS, IBM, Microsoft, Oracle, Qlik), Challengers, Visionaries, and Niche Players.
Key findings:
· Data integration is crucial for enhancing operational, analytical, and AI infrastructure
· By 2027, AI assistants will cut manual intervention by 60%
· Leaders excel in supporting diverse data delivery styles and modern architectures
· Vendors must support hybrid/multicloud deployments and various user personas
· DataOps and FinOps capabilities are increasingly vital
Read the full report to find the best tools for your needs.
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A Computer Weekly buyer's guide to Business Intelligence and Analytics
By: TechTarget ComputerWeekly.com
Type: eGuide
In this 20-page buyer’s guide, Computer Weekly looks at:
- How to find a much sought-after data scientist and how every organization seems to be hunting for a data scientist, but finding the right people is a challenge
- How modern business intelligence shapes up to big data and the supplier CEO`s perspective on recent business intelligence and analytics strategies
- How DataOps can help surf the flood of data and how DataOps is helping firms to accelerate the time it takes to derive value from the data they collect
Find more content like what you just read:
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Deep Dive into DataOps
By: TechTarget ComputerWeekly.com
Type: eGuide
In this e-guide, we hear from key decision-makers from Altran, Moogsoft, Puppet, Morpheus, Talend, NetApp and PagerDuty about how and why they are adopting DataOps.
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How to build a winning digital operations framework with DevOps, GitOps & MLOps
By:
Type: Talk
With adoption of Cloud, Digital and Data growing at exponential rates, the need for mature digital operational practices continues to be a key challenge for organisations. With the growing emergence of techniques such as SRE, FinOps, GitOps, MLOps and the expectation for teams to deliver against DevOps and Agile practices, organisations need to be able pull all of these together into a cohesive framework to deliver against their digital transformation goals. Over the past 6 years, Contino has been working with the world’s leading brands to transform their Operations and ITSM functions to move towards a modern operational approach, combining the rigour and discipline of ITSM with the proven engineering principles of Cloud & DevOps. Join us on this webinar to learn more about: -What do the ITSM processes and controls translate to in a cloud-first or cloud-native environment? -How do techniques such as SRE, FinOps, GitOps, MLOps fit into your Digital Operations Framework Cloud Operations playbook? -How to move towards a DevOps & Agile operating model whilst still adhering to controls and regulatory requirements? -How to federate operational responsibilities to engineering teams by adopting modern team topologies, whilst still maintaining operational rigour within your organisation? -What role do SLIs and SLOs play in your operational metrics? -How to get started and build your own Digital Operations playbook.
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Detailed analysis of MLOps requirements, best practices, & adoption challenges
By: Red Hat
Type: Analyst Report
Open source AI platforms can help enterprises accelerate AI/ML adoption and overcome challenges like lack of MLOps tools. Learn how an open hybrid AI/ML platform like Red Hat OpenShift AI can enable flexibility, portability, and faster time to value. Read the full analyst report.
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Expert Panel: What's Ahead in Data Management in 2023
By:
Type: Video
Data management is fundamental to every application. Managing this precious asset is an essential competency in modern businesses of every sort. Innovations in data platforms are being adopted, and data management approaches are evolving rapidly to keep pace. Increasingly, enterprises are converging their data warehouse, data lake, and other data management platforms onto distributed cloud-native infrastructures. As more types of data are consolidated into their platforms, enterprises implement more scalable DataOps pipelines and more comprehensive governance practices to manage it all. Want to learn more? This webinar brings together a panel of experts, moderated by James Kobielus, TDWI’s research director for data management. The panel will discuss the hotter trends in data integration, governance, and management, including: - Increasing use of AI/ML for intelligent DataOps automation; - Growing reliance of data governance, quality, and curation on centralized data catalogs; - Adoption of cloud data marketplaces as the go-to source for reference data sets; - Emergence of synthetic data as a supplement or replacement for source data in the MLOps pipeline; - Team-oriented policy authoring and management within the data stewardship workflow; - Democratization of data engineering through visual, low-code tooling; and - Examples of what successful real-world organizations are doing today
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Decoding Data Automation With Cloud-Native Analytics Pipeline and MLOps
By:
Type: Replay
At the core, Machine Learning Operations (MLOps) turns an experimental machine learning model into a production system. MLOps is an emerging practice distinct from traditional DevOps. The ML lifecycle aggregates training data, making MLOps workflow sensitive to data changes, volumes, and quality. Additionally, matured MLOps should support monitoring both ML lifecycle activities and production model monitoring. Key Takeaways: • Build cloud-native serverless analytics pipeline. • Learning different data automation pattern including data lake, lake house and data mesh architecture. • Automate end-to-end data pipeline with MLOps.
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Decoding Data Automation With Cloud-Native Analytics Pipeline and MLOps
By:
Type: Talk
At the core, Machine Learning Operations (MLOps) takes an experimental machine learning model into a production system. MLOps is an emerging practice distinct from traditional DevOps. ML lifecycle involves using patterns from training data, making MLOps workflow sensitive to data changes, volumes, and quality. Additionally, matured MLOps should support monitoring both ML lifecycle activities and production model monitoring. Key Takeaways: • Build cloud-native serverless analytics pipeline. • Learning different data automation pattern including data lake, lake house and data mesh architecture. • Automate end-to-end data pipeline with MLOps.
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Decoding Data Automation With Cloud-Native Analytics Pipeline and MLOps
By:
Type: Replay
At the core, Machine Learning Operations (MLOps) takes an experimental machine learning model into a production system. MLOps is an emerging practice distinct from traditional DevOps. ML lifecycle involves using patterns from training data, making MLOps workflow sensitive to data changes, volumes, and quality. Additionally, matured MLOps should support monitoring both ML lifecycle activities and production model monitoring. Key Takeaways: • Build cloud-native serverless analytics pipeline. • Learning different data automation pattern including data lake, lake house and data mesh architecture. • Automate end-to-end data pipeline with MLOps.
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Succeeding with Mature MLOps feat. TDWI
By:
Type: Replay
Organizations everywhere are automating the development, deployment, monitoring, and governance of mission-critical machine learning (ML) and other artificial intelligence (AI) applications. Operational data science is a collaborative process that increasingly goes under the name of MLOps. Organizations are bringing the latest MLOps into their data science workflows to augment the productivity of data engineers, statistical modelers, and other highly skilled personnel. Mature enterprise MLOps processes leverage cloud-native infrastructure to scale the deployment, monitoring, and management of statistical models and code builds into production applications. Join Dan Darnell from Dataiku and TDWI’s senior research director James Kobielus for this webinar to learn how enterprises can succeed in using mature MLOps practices across their entire data science pipelines to speed deployment of their most sophisticated AI applications. Key topics that he will discuss include: - Business opportunities that are driving demand for MLOps - Key investments in data ingestion, cleansing, preparation, and modeling technologies that are essential for organizations to succeed with MLOps - Challenges that organizations face when implementing MLOps within their established data science processes - Principal operational metrics that organizations must monitor and track to ensure the success of their MLOps initiatives while mitigating associated operational, legal, and regulatory risks
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Why DataOps is crucial for digital transformation success
By: HighByte
Type: White Paper
How can your business simplify data contextualization to maximize the value of your technology investments? By leveraging DataOps to define standard models and establish and manage integrations, operational teams can provide data to the systems and business users who are requesting it in a more efficient and managed way. Read on to learn more.
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Modern Industrial Data Architecture Best Practices
By: HighByte
Type: White Paper
Industrial DataOps is the dominant framework for mastering 4.0 data transformation projects, and it is key for leveraging solutions that can deliver data to users for a real-time view of the enterprise. Read on to learn about a solution that can help manage data in a common format that is ready to consume, contextualize, and scale for the customer.
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Introduction to MLOps: Six proven techniques to amplify AI maturity
By:
Type: Talk
If you are working in AI or studying data science, then you might have heard about MLOps and be wondering what the buzz is all about. Data science is a rapidly evolving and maturing space and new techniques and technologies are emerging all the time to help amplify the productivity and maturity of data scientists’ work. MLOps is all about this. MLOps is the natural extension of DevOps applied to AI and Machine Learning, adapting the advanced automation techniques of the pros to the new learnings and technologies that drive artificial intelligence and machine learning systems. In this webinar, we will review how adopting a few key MLOps techniques can help you get the most out of your data science projects. We will examine some of the most popular MLOps techniques adopted by the leading industry players, and how you can adopt them too: - Machine Learning pipelines - Continuous deployment - Inference services - Blue-green deployments - Automated drift detection - Feature stores
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DataOps enhances data engineering for data science at Moneysupermarket.com
By:
Type: Talk
Data Operations is a methodology combining technological and cultural changes to improve data usage through better collaboration and automation. At Moneysupermarket.com, the DataOps approach is critical to meet the expectations of the business. Several important Vertica features facilitate the adoption of the DataOps culture and simplify the data engineer’s journey. DataOps with Vertica gets new data to data scientists promptly, so they can deliver maximum value to their organisation. Join our webinar to discover why Moneysupermarket.com adopted a DataOps approach!
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DataOps enhances data engineering for data science at Moneysupermarket.com
By:
Type: Replay
Data Operations is a methodology combining technological and cultural changes to improve data usage through better collaboration and automation. At Moneysupermarket.com, the DataOps approach is critical to meet the expectations of the business. Several important Vertica features facilitate the adoption of the DataOps culture and simplify the data engineer’s journey. DataOps with Vertica gets new data to data scientists promptly, so they can deliver maximum value to their organisation. Join our webinar to discover why Moneysupermarket.com adopted a DataOps approach!
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DataOps enhances data engineering for data science at Moneysupermarket.com
By:
Type: Replay
Data Operations is a methodology combining technological and cultural changes to improve data usage through better collaboration and automation. At Moneysupermarket.com, the DataOps approach is critical to meet the expectations of the business. Several important Vertica features facilitate the adoption of the DataOps culture and simplify the data engineer’s journey. DataOps with Vertica gets new data to data scientists promptly, so they can deliver maximum value to their organisation. Join our webinar to discover why Moneysupermarket.com adopted a DataOps approach!
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DataOps enhances data engineering for data science at Moneysupermarket.com
By:
Type: Replay
Data Operations is a methodology combining technological and cultural changes to improve data usage through better collaboration and automation. At Moneysupermarket.com, the DataOps approach is critical to meet the expectations of the business. Several important Vertica features facilitate the adoption of the DataOps culture and simplify the data engineer’s journey. DataOps with Vertica gets new data to data scientists promptly, so they can deliver maximum value to their organisation. Join our webinar to discover why Moneysupermarket.com adopted a DataOps approach!
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DataOps enhances data engineering for data science at Moneysupermarket.com
By:
Type: Replay
Data Operations is a methodology combining technological and cultural changes to improve data usage through better collaboration and automation. At Moneysupermarket.com, the DataOps approach is critical to meet the expectations of the business. Several important Vertica features facilitate the adoption of the DataOps culture and simplify the data engineer’s journey. DataOps with Vertica gets new data to data scientists promptly, so they can deliver maximum value to their organisation. Join our webinar to discover why Moneysupermarket.com adopted a DataOps approach!
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DataOps enhances data engineering for data science at Moneysupermarket.com
By:
Type: Replay
Data Operations is a methodology combining technological and cultural changes to improve data usage through better collaboration and automation. At Moneysupermarket.com, the DataOps approach is critical to meet the expectations of the business. Several important Vertica features facilitate the adoption of the DataOps culture and simplify the data engineer’s journey. DataOps with Vertica gets new data to data scientists promptly, so they can deliver maximum value to their organisation. Join our webinar to discover why Moneysupermarket.com adopted a DataOps approach!
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Just Build It! How to Make MLOps a Reality In Your Organisation
By:
Type: Talk
Orchestrating data science and machine learning in an industrial setting is difficult. The data landscape is challenging, littered with complex problems and without the necessary freedom to design experiments or create observational studies on real-world processes. On top of that, you must manage stakeholders, operate cloud technologies, write software or wrap your solution into a product required to run predictions 24/7/365 – all while supporting business operations! With all this on your plate, you will need strategies to successfully incorporate MLOps in your organisation. In this talk, we will explore: How to “bootstrap” ML Engineering (MLEng) and MLOps practices in your organisation. Different ways to organize your MLOps teams to create optimum value. Where to start on your MLOps journey. How to avoid some common “gotchas” of starting out in MLOps.
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Establishing Strong DataOps Foundations to Drive Business Value
By:
Type: Replay
With many organizations embarking or already embarked on modernization initiatives, anchored in cloud and AI capabilities, operational challenges can hinder the long run value realization of such transformations. Increasingly, organizations need to consider optimal data operations (DataOps) as part of the modernization efforts, to deliver and expand upon expected business value, and improve AI and data-driven decision making. In fact, the Enterprise Strategy Group’s April 2022 "State of DataOps" report found that out of 403 technical and business data professionals, 90% of those surveyed reported plans to make moderate to extensive investments in DataOps in the upcoming year. Data and Business leaders, need to collaboratively map out desired outcomes and prepare for challenges before implementing a DataOps strategy. Join this conversation among leaders from leading organizations, Deloitte and Enterprise Strategy Group (ESG) as they discuss the foundations of a strong DataOps strategy. This conversation will focus on: • The long-term goal of DataOps and key pillars that will drive its sustainability • How Agile and DevOps approaches and teams that have the right roles and skillsets drive effective and value-adding operations • How new technology will support DataOps • How data governance needs to be embedded in DataOps • Program management, governance and alignment • And much more Moderator: Mike Leone, Senior Analyst, ESG Panelists: Hemathri Balakrishnan, Managing Director, Head of Architecture, Product Engineering & Data, J.P Morgan Wealth Management Prakul Sharma, Managing Director, DataOps Practice Lead, Deloitte Consulting
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Establishing Strong DataOps Foundations to Drive Business Value
By:
Type: Replay
With many organizations embarking or already embarked on modernization initiatives, anchored in cloud and AI capabilities, operational challenges can hinder the long run value realization of such transformations. Increasingly, organizations need to consider optimal data operations (DataOps) as part of the modernization efforts, to deliver and expand upon expected business value, and improve AI and data-driven decision making. In fact, the Enterprise Strategy Group’s April 2022 "State of DataOps" report found that out of 403 technical and business data professionals, 90% of those surveyed reported plans to make moderate to extensive investments in DataOps in the upcoming year. Data and Business leaders, need to collaboratively map out desired outcomes and prepare for challenges before implementing a DataOps strategy. Join this conversation among leaders from leading organizations, Deloitte and Enterprise Strategy Group (ESG) as they discuss the foundations of a strong DataOps strategy. This conversation will focus on: • The long-term goal of DataOps and key pillars that will drive its sustainability • How Agile and DevOps approaches and teams that have the right roles and skillsets drive effective and value-adding operations • How new technology will support DataOps • How data governance needs to be embedded in DataOps • Program management, governance and alignment • And much more Moderator: Mike Leone, Senior Analyst, ESG Panelists: Hemathri Balakrishnan, Managing Director, Head of Architecture, Product Engineering & Data, J.P Morgan Wealth Management Prakul Sharma, Managing Director, DataOps Practice Lead, Deloitte Consulting
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Introduction to Machine Learning Operations ( MLOps )
By:
Type: Talk
Machine learning(ML) is a branch of artificial intelligence that focuses on the development of algorithms that have the ability to learn by using data. While ML gets a lot of attention, the actual implementation of ML models (their deployment and maintenance) requires much more than programming skills. Enter MLOps MLOps is the short term for machine learning operations. MLOps represents a set of practices that aim to simplify workflow processes and automate machine learning and deep learning deployments. For example, in the case of a smart city, a good use case is a model that automatically sends alerts when there are accidents. It constantly retrains, based on new data regarding the traffic and it behaves differently during bank holidays or during different seasons. MLOp accomplishes the deployment and maintenance of models reliably and efficiently for production, at a large scale. In other words, MLOps enables you to ship models faster, ensuring portability and reproducibility. Navigating the landscape of MLOps solutions can be daunting. There is no one size fits. If you are looking to understand current MLOps trends, join our webinar to find out how to choose the right solution and learn about Charmed Kubeflow. During the webinar, you will learn from Canonical’s AI experts, Maciej Mazur, Principal AI/ML Engineer, and Andreea Munteanu, MLOps Product Manager based on real customer questions and requests collected by Adrian Matei, Sales Representative.
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Optimize Your Data Fabric for Digital Innovation
By:
Type: Talk
Join us in this fireside chat with 451 Research and Hitachi Vantara on the State Of DataOps in 2022 and see how Enterprises are using intelligent DataOps to unify data, govern it, and deliver seamless customer experience. Demystify the hype around data fabrics and see how DataOps can help you build your fabrics to deliver key business outcomes. Learn how you can put data at the heart of decision-making with new AI-driven data management solutions.
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Optimize Your Data Fabric for Digital Innovation
By:
Type: Replay
Join us in this fireside chat with 451 Research and Hitachi Vantara on the State Of DataOps in 2022 and see how Enterprises are using intelligent DataOps to unify data, govern it, and deliver seamless customer experience. Demystify the hype around data fabrics and see how DataOps can help you build your fabrics to deliver key business outcomes. Learn how you can put data at the heart of decision-making with new AI-driven data management solutions.
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Optimize Your Data Fabric for Digital Innovation
By:
Type: Talk
Join us in this fireside chat with 451 Research and Hitachi Vantara on the State Of DataOps in 2022 and see how Enterprises are using intelligent DataOps to unify data, govern it, and deliver seamless customer experience. Demystify the hype around data fabrics and see how DataOps can help you build your fabrics to deliver key business outcomes. Learn how you can put data at the heart of decision-making with new AI-driven data management solutions.
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Operationalize Your AI Models: 4 key Elements
By: Red Hat
Type: White Paper
As organizations continue to adopt AI, the challenge quickly becomes operationalizing models effectively via a continuous machine learning operations (MLOps) process. How can you spin up this process in to ensure you deliver reliable AI results? Access this checklist now to learn 4 key methods of operationalizing AI successfully in the long-term.
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Applying Data to DevOps: A Comprehensive Guide to DataOps
By:
Type: Talk
The IDC estimates that the amount of digital data created over the next five years will be greater than twice the amount of data created since the advent of digital storage. It’s therefore no surprise that it’s a pressing need for organizations to become more data-driven and to be able to quickly process deluges of data to quickly derive actionable insights. It’s becoming increasingly apparent that this requires an agile data management approach that makes the most of both technology and humans. Still a relatively new term, this is termed DataOps. DataOps seeks to expand the virtues of DevOps, and advocates for clear communication and collaboration across all data stakeholders to eliminate the inefficiencies of traditional data integration, and drive efficiency. In this episode, Helen Beal and guests will be discussing how DataOps can eliminate silos and foster a Data Culture throughout your organization. Join them to hear them share ideas that will help you understand and overcome the challenges around data in the DevOps space. They’ll be discussing: - Why DataOps isn’t just DevOps for Data - How DataOps reduces complexity for organizations and shouldn’t be viewed as ‘just another thing for data professionals to worry about’ - What the importance of individuals and interactions, as well as tools and processes, are in DataOps - How to demonstrate value early and encourage executive buy-in - And more
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Applying Data to DevOps: A Comprehensive Guide to DataOps
By:
Type: Talk
The IDC estimates that the amount of digital data created over the next five years will be greater than twice the amount of data created since the advent of digital storage. It’s therefore no surprise that it’s a pressing need for organizations to become more data-driven and to be able to quickly process deluges of data to quickly derive actionable insights. It’s becoming increasingly apparent that this requires an agile data management approach that makes the most of both technology and humans. Still a relatively new term, this is termed DataOps. DataOps seeks to expand the virtues of DevOps, and advocates for clear communication and collaboration across all data stakeholders to eliminate the inefficiencies of traditional data integration, and drive efficiency. In this episode, Helen Beal and guests will be discussing how DataOps can eliminate silos and foster a Data Culture throughout your organization. Join them to hear them share ideas that will help you understand and overcome the challenges around data in the DevOps space. They’ll be discussing: - Why DataOps isn’t just DevOps for Data - How DataOps reduces complexity for organizations and shouldn’t be viewed as ‘just another thing for data professionals to worry about’ - What the importance of individuals and interactions, as well as tools and processes, are in DataOps - How to demonstrate value early and encourage executive buy-in - And more
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Harvard Business Review: Aligning Business Goals with Data Strategy
By:
Type: Replay
In this interactive panel discussion, Harvard Business Review examines the roles of leadership, technology, and culture in building an optimal environment for creating and acting on data insights. Discover how businesses can best position themselves to reap the rewards from DataOps with Alex Clemente, Managing Director, Harvard Business Review Analytic Services, Radhika Krishnan, Chief Product Officer at Hitachi Vantara, and Eric Hirschhorn, Chief Data Officer at BNY Mellon. Together, they will discuss: - Why using DataOps will alleviate pressure on IT resources - How DataOps can unlock value from existing data - Why employee buy-in is so crucial - The importance of leadership in creating a DataOps strategy
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Modernizing the Organization to Support Data and Analytics (NA)
By:
Type: Video
As organizations strive to compete in a dynamic environment, they are looking to modernize their data and analytics environment to help. This modernization includes implementing new technologies such as scalable cloud platforms and unified approaches. It includes utilizing more advanced analytics such as geospatial analytics and machine learning. It includes new paradigms such as the data fabric and the data mesh. Moreover, as part of this, modernization may include new organizational constructs such as the data office and new teams such as DataOps, MLOps, and data literacy enablement teams. Join TDWI’s VP of Research, Fern Halper, Ph.D., as she discusses the results of her latest TDWI Best Practices Report on how successful companies are organizing to execute a winning strategy with analytics. This research focuses on topics including leadership structures, organizational structures, new roles and new paradigms. It also examines new technologies and the impact of these technologies on organizations. Topics include: New leaders, new offices, and new roles for modern analytics Organizing to execute against modern analytics Enabling technologies Building the culture and skills Success factors for obtaining value with modern analytics
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Modernizing the Organization to Support Data and Analytics (APAC)
By:
Type: Replay
As organizations strive to compete in a dynamic environment, they are looking to modernize their data and analytics environment to help. This modernization includes implementing new technologies such as scalable cloud platforms and unified approaches. It includes utilizing more advanced analytics such as geospatial analytics and machine learning. It includes new paradigms such as the data fabric and the data mesh. Moreover, as part of this, modernization may include new organizational constructs such as the data office and new teams such as DataOps, MLOps, and data literacy enablement teams. Join TDWI’s VP of Research, Fern Halper, Ph.D., as she discusses the results of her latest TDWI Best Practices Report on how successful companies are organizing to execute a winning strategy with analytics. This research focuses on topics including leadership structures, organizational structures, new roles and new paradigms. It also examines new technologies and the impact of these technologies on organizations. Topics include: New leaders, new offices, and new roles for modern analytics Organizing to execute against modern analytics Enabling technologies Building the culture and skills Success factors for obtaining value with modern analytics
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Bringing sustainability to your industrial data architecture
By: HighByte
Type: Blog
While industrial processes are a big contributor to a company’s CO2 emissions, data processing and storage is also an important part of the sustainability equation. Read on to learn how manufacturers can support their sustainability goals while potentially increasing production and why DataOps is crucial for success.
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Retail at the edge with MLOps: market basket analysis
By:
Type: Talk
Retail is being reinvented by AI/ML and retailers are challenged to respond to customers’ needs and preferences. From personalised promotions to inventory management, there is a wide spectrum of use cases that are addressed by technological innovations. It is an industry that is rich in data, and more than using it to connect dots, retail needs to gather insights to offer better experience and retain clients. During the webinar, we will talk more about retailers and how MLOps enables their machine-learning initiatives. People from key positions that work in the industry can learn more about: - Digital transformation in retail driven by AI/ML - What is MLOps - Why open-source MLOps is suitable for the retail industry - Challenges and possible solutions using MLOps - A business case discussion: market basket analysis - Charmed Kubeflow: an end-to-end platform for retailers As main hosts for the webinar, there will be: - Andreea Munteanu, MLOps Product Manager - Stephen Barnes, Retail Consultant
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DataOps: Drivers, Challenges and Adoption Trends
By:
Type: Video
As enterprises continue to accumulate data at increasingly large volumes and speeds, they are looking for ways to effectively and efficiently leverage that data to drive value for the organization. This has led to the emergence of DataOps–an automated, process-oriented methodology used by analytic and data teams to improve the quality and reduce the cycle time of data analytics. Despite the obvious and growing need for a discipline like DataOps across many enterprises, a majority of companies have yet to really embrace the practice. Join Issam Hijazi to learn more about DataOps drivers, challenges and adoption trends.
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Reimagining and optimizing DataOps to drive evergreen business value
By:
Type: Talk
The growing value of data has been understood for some time, yet many organizations still struggle to build, run and evolve their data capabilities and services for full utilization. These challenges are compounded by mounting business needs and expectations, and tighter competition within industry sectors. Data operations (DataOps) is therefore becoming much more than just "lights on", it is foundational for driving evergreen business value. Reimagining your DataOps strategy is a critical component to define and deliver your organization's Enterprise Data Strategy. While companies have customized their individual operations approach, certain factors remain consistent across the most successful insight-driven organizations. Their approach to DataOps is driven by a clear and actionable mission, value-oriented governance framework, and a comprehensive suite of enabling capabilities that drive high quality and high value execution. Join Deloitte to hear how reimagined DataOps is a critical success factor to unlocking the full value of your data-driven business strategies, and how new technologies and shifting operating models are transforming the delivery of DataOps services.
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Accelerate Data-Driven Innovation With DataOps
By:
Type: Video
DataOps has created a lot of hype as a practice for agile and automated data management. See how it applies to financial services and how it can help you modernize your data fabric. Learn how DataOps makes data visible from edge to cloud, provides frictionless access to data, enhances data pipelines for faster AI, and automates and scales data governance.
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Deploying an Intelligent Cloud: 7 Strategies for Success
By: Nutanix
Type: eGuide
In this e-book, you'll discover 7 proven strategies to optimize your hybrid multicloud environment and maximize cloud productivity and cost savings. Dive in now to learn how to automate operations, control data services, and leverage AIOps.
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MLOps & ModelOps: What’s next?
By:
Type: Talk
AI and machine learning are closely linked to digital transformation, and is perhaps why 2021- a year of immense business transformation - has seen such a shift towards MLOps and ModelOps. In fact, according to a recent IBM survey, 21% of respondents said that AIOps had a “transformational” relationship between IT and other parts of the business. Some key takeaways from organisations implementing MLOps include observability and end-to-end visibility, automation of complex processes, and support on the cloud. Evidently, MLOps and ModelOps can have a real impact on the relationship between moving parts in the organisation, and done right, can have a transformative impact on your business. Join us in this session as we discuss: - How MLOps and ModelOps have impacted transformation efforts in 2021 - Best practices for implementing MLOps in your organisation - Overcoming challenges when it comes to implementation - ModelOps challenges and how to overcome them - What’s next for MLOps and ModelOps as we move into 2022
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The Data Factory: Put the data to work
By:
Type: Video
Industrial organizations are generating more data than ever before and looking to institute AI and Analytic applications to transform their organizations and give them a competitive advantage, to control costs and optimize operations. Because DataOps is a new concept to industrial, there is a chasm in understanding between IT and OT teams that must be bridged to fully realize sustainable digital transformation for an industrial business. As an Operations Leader, you understand the value of automation and operations management systems, but may struggle in the IT concepts and terminology that are more well known in commercial businesses such as banking, ad-tech, and insurance. You do, however, appreciate how a factory works and how well-designed production processes execute. Industrial DataOps is a lot like a factory production line in the way it automates, upgrades, improves, and assembles another valuable raw material which is, of course, data. In this session, we will explain Industrial DataOps in the context of the way a manufacturing plant is optimized. This will help to bridge the divide between the plant manager and the central IT manager, allowing the understanding these techniques of professional data management to drive sustainable digital transformation. And also, we will share Lumada Industrial DataOps DEMO to share how we can help putting the data to work. In this webinar, audience will learn - How to bridge the divide between the plant manager and the central IT manager - The techniques of professional Data management - Lumada industrial DataOps DEMO (Capabilities)
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See what Enterprise-grade FinOps is and why you're needed
By:
Type: Replay
If you're forming, launching, or advancing a FinOps practice, having a background in IT Asset Management (ITAM) is valuable. But is it enough to help your organization to achieve an enterprise-grade FinOps? Find out by watching this webinar, See what Enterprise-grade FinOps is and why you’re needed. Speakers from ServiceNow and Anglepoint, a leader in ITAM and FinOps implementations, will delve into the relationship and future between ITAM and FinOps, and will explain: - How far apart are ITAM and FinOps? - What does enterprise-level FinOps really mean? - Which skills are needed to transform FinOps? We’ll also cover how ServiceNow empowers enterprise-grade FinOps through Cloud Cost Management. Get started on your FinOps journey—watch now.
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Moving up to Enterprise FinOps
By:
Type: Replay
Explore this extensive ServiceNow interview with two Anglepoint FinOps experts to understand the landscape of enterprise FinOps. Topics cover organizational reports with showbacks and chargebacks, an ideal cloud cost management workspace, features in an enterprise-grade cloud cost management solution, reporting to executives using cloud cost management data integrated with other technology asset data, and suggested next steps in your FinOps journey.
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Moving up to Enterprise FinOps
By:
Type: Replay
Explore this extensive ServiceNow interview with two Anglepoint FinOps experts to understand the landscape of enterprise FinOps. Topics cover organizational reports with showbacks and chargebacks, an ideal cloud cost management workspace, features in an enterprise-grade cloud cost management solution, reporting to executives using cloud cost management data integrated with other technology asset data, and suggested next steps in your FinOps journey.
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Energy service provider cuts storage costs by 91%
By: Hitachi Pentaho EMEA
Type: Case Study
VNG Handel & Vertrieb, an energy service provider, boosted productivity and cut storage costs by 91% using Pentaho Data Integration. Read this case study now to learn how they streamlined data pipelines, handled large data volumes, and integrated systems to enhance the user experience.
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Boosting productivity and cutting costs with data integration
By: Hitachi Pentaho
Type: Case Study
VNG Handel & Vertrieb, an energy service provider, boosted productivity and cut storage costs by 91% using Pentaho Data Integration. Read this case study now to learn how they streamlined data pipelines, handled large data volumes, and integrated systems to enhance the user experience.
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Data Science in the Enterprise: Leveraging MLOps in the Enterprise
By:
Type: Replay
MLOps offers ways to optimize processes, automate workflows, detect anomalies, reduce costs, and improve business outcomes. However, implementing and applying these techniques can be a large and complex effort. In this webinar, we’ll explore how to successfully implement MLOps across the enterprise using data science platforms to achieve different business goals, like deployment and automation, reproducibility and scalability, diagnostics, governance and compliance, collaboration, and monitoring. Speaker: Chris Styduhar is the Director of Enterprise Products at Anaconda. With a background in software engineering, machine learning, and enterprise technology solutions, Chris manages the development of Anaconda’s Data Science Platform, including MLOps and AI features that help data scientists build, collaborate, and deploy more effectively.
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See what Enterprise-grade FinOps is and why you’re needed
By:
Type: Replay
If you're forming, launching, or advancing a FinOps practice, having a background in IT Asset Management (ITAM) is valuable. But is it enough to help your organization to achieve an enterprise-grade FinOps? Find out by attending our upcoming webinar, See what Enterprise-grade FinOps is and why you’re needed. Speakers from ServiceNow and Anglepoint, a leader in ITAM and FinOps implementations, will delve into the relationship and future between ITAM and FinOps, and will explain: - How far apart are ITAM and FinOps? - What does enterprise-level FinOps really mean? - Which skills are needed to transform FinOps? We’ll also cover how ServiceNow empowers enterprise-grade FinOps through Cloud Cost Management.
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MagicOrange | FinOps Advisory Services
By:
Type: Video
FinOps is an evolving cloud financial management discipline and cultural practice defined by The FinOps Foundation. FinOps brings together Finance, Business and Technology teams to manage, control and develop strategic cloud solutions. MagicOrange is a general member of The FinOps Foundation. We are passionate about the goal of The FinOps Foundation to advance people who do cloud financial management and to adopt the FinOps Framework and best practices as part of our organisation. Our customer success and FinOps Lead consultants are FinOps Certified Practitioners. As a SaaS solution, MagicOrange is also using the FinOps Framework and best practices for our own cloud financial management and strategic needs. So, we are certainly going to share all our learnings on our FinOps journey with our customer base. Visit our website to learn more: magicorange.com
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Preparing for 2025: FinOps Trends and ITAM Synergy
By:
Type: Video
Climbing Higher than ever before with FinOps in 2025! As we wrap up 2024, it’s time to take stock of the FinOps landscape and chart a course for 2025. Join expert Robbie Plourde, FinOps Certified Technology and IT Transformation Executive at USU, in this 45-minute webinar as he ropes in the major milestones that defined FinOps success stories this year and how IT Asset Management (ITAM) can anchor your FinOps journey to new heights. Gain insights that will help you ascend 2025 with a firm grip on optimizing IT and financial operations. Gear up for success and explore the future of FinOps in this on-demand webinar. Join us to explore: - The terrain of FinOps in 2024 - Current footholds in the FinOps journey - Future trends in FinOps and ITAM to help you scale