Name: Best Practices for Adopting Containers within your MLOps Process
Start: 2022-10-12T15:00:00Z
End: 2022-10-12T15:00:42.000Z
Location: BrightTALK
Rating: 0

Data Science Central is the industry's online resource for data practitioners. From Statistics to Analytics to Machine Learning to AI, Data Science Central provides a community experience that includes a rich editorial platform, social interaction, forum-based support, plus the latest information on technology, tools, trends, and careers. 

This on-demand workshop tackles the challenges of mapping and maintaining your company universe to the right internal or external identifiers. We’ll walk through a streamlined S&P Global Marketplace Blueprint, showcasing automated entity recognition powered by Kensho, S&P Global’s AI innovation arm. 

Discover how the Business Entity Cross Reference Service (BECRS) dataset automates initial linking and handles unmapped companies using Kensho Link, an advanced machine learning model. Kensho Link matches company names to unique entity identifiers with high accuracy, providing comprehensive coverage. Plus, see how it rolls entities up to their ultimate parent, offering a clear view of corporate hierarchies.

Unified Company Mapping: Leveraging Cross Reference Services and Kensho Link

Enhancing company data, even when structured and cleaned, is challenging without mapping it to industry-standard identifiers like S&P Global’s CapIQ IDs or BECRS IDs. Entity mapping is complex for data engineering and analyst teams due to limited contextual information and frequent name similarities, even within corporate hierarchies. Traditional methods like fuzzy matching and rules-based mapping often fall short because they rely heavily on semantic similarities. 

AI-powered entity linking changes the game by considering both the submitted entity and its associated data, along with the target database’s content. This advanced approach offers more precise and effective mapping. Watch this session to learn about using  AI-powered entity mapping to support initiatives such as: 

- CRM clean-up 
- Platform integrations 
- Security master file enhancement

Data Entity Mapping with AI - The Challenges and the Solutions

The seamless integration and management of vast data across companies, sectors, and instruments are essential. In this webinar, we’ll explore how to build a robust security and entity master file using S&P Global's Cross Reference Services, a solution built from the ground up and meticulously refined and enhanced over the last 30 years. 
  
Discover our extensive linking capabilities that provide clear views of corporate hierarchies and relationships, helping to identify critical counterparty exposures and ensure effective data management. Learn how Cross Reference Services can serve as a solid foundation, boosting productivity and enhancing decision-making throughout your organization.

Mastering Data Integration: The Importance of Building a Strong Security and Entity Master File

Is Your Data Ready for AI?

The quality of your data plays a critical role in adopting generative AI and achieving meaningful outcomes. However, many organizations find themselves bogged down by time-consuming and repetitive data preparation tasks, which limits their ability to fully leverage the power of generative AI.

In this podcast, we'll guide you through six essential steps to ensure your data is clean, consistent, and primed for advanced AI tools.

The Six Steps to AI-Ready Data:

- Data Exploration: Discover hidden patterns and anomalies within your dataset.
- Data Cleaning: Remove duplicates, errors, and irrelevant information.
- Data Blending: Merge multiple datasets to uncover new insights.
- Data Profiling: Detect and rectify poor-quality data before it affects your outcomes.
- ETL (Extract, Transform, Load): Efficiently aggregate data from various sources.
- Data Wrangling: Prepare and optimize your data for smooth consumption by AI tools.

6 Steps to AI-Ready Data

Traditional data providers offer limited line items and forecast periods, leaving analysts at financial firms seeking more information. But data means nothing without analysis, which is why S&P Global developed a variety of tools to help you translate data into Visible Alpha.

During this webcast, you’ll find out how you can enable data users in your organization to analyze multiple datasets together instead of a single dataset in a silo. Then, you’ll learn about a unified platform for end-to-end AI and machine learning designed to help businesses unlock the full potential of their data.

Join the session now to discover how you can empower your analysts with the tools they need to make data-driven decisions.

Innovating Asset Management in Volatile Markets

As AI continues to dominate across industries, many organizations are realizing that their tools are only as strong as the insights and data that drive them. This is especially true for large language models (LLMs).

Today, businesses are leveraging LLMs to help analysts more effectively cover thousands of reports, enhance feature extraction, and identify new patterns that the human eye might miss. However, these functions (and many others) can cause potential risks that call for further investments in both data management and your automation technology.

This webinar, featuring a panel of industry experts including Prof. Ronen Feldman, Alex Kim, Harpreet Kaur, and Kevin Zacharuk, provides you with an in-depth exploration of the current AI and LLM landscape, offering insight into the steps you can take to improve your automation and data models, as well as how you can achieve better outcomes.

Tune into the webinar to see more and discover the key ways you can build your AI initiatives for success.

AI, LLMs & more: Why and how should you invest in AI-based tech?

The latest Gartner Hype Cycle says Generative AI is just passing the peak of inflated expectations. What does that mean?  

Right now, expectations for AI are extremely high, but perhaps overinflated.  And while there is certainly value to be created from generative AI today, there is a misalignment between expectations and reality. AI is a gamechanger, but it's not a one stop solution for every ailment, and there are foundations that need to be laid down now to get the most from AI moving forward.  

In this podcast, our experts from Alteryx will analyze these trends and talk about how to build a foundation for AI that will stand the test of time. We’ll look at why data is the new oil in the age of AI, and how this should influence the way we look at the industry and inform our processes for data extraction, refinement, and delivery (in the form of business-changing use cases).

How Data is Becoming the New Oil and Why it Matters

Our organizations live and breathe based on the speed of insights. But data analysts can only work so fast. 

A large percentage of the average data analyst’s day job consists of being bogged down with repetitive reports which must be repeated on a daily or weekly basis. These not only consume lots of time, reducing the ability to do ad hoc analytics exploration, but also frequently result in lots of errors, which can be costly.

In this webinar, you’ll learn how to escape the day-to-day grind with analytics automation, including some new AI tools that help with the automation process. You’ll also learn the benefits of automation, and why it is one of the most important career-building skills for a budding analyst.

Becoming an Analytics Champion: How Anyone Can Automate Their Data Analytics – Without Writing Code

Data teams have always been asked to do more with less. “Can you make this easier? Can this run faster?” That focus on efficiency hasn’t changed. But what has changed is the macroeconomic pressure to tighten budgets, consolidate tools, and find cost optimizations wherever possible in the data development process.

As teams look back on the lessons of 2023, and look ahead at the budgets of 2024, this webinar provides a candid discussion—and tangible tips—for how to make the most of your resources.

What you’ll learn: In this webinar panel, data practitioners who optimized resources in their data workflow share exactly:
- What they did
- The impacts the initiative had
- What they learned
- The advice they have for other teams
- Plus, the panelists share their perspectives on some of the most talked-about ideas to drive efficiency with spend: AI, self-service, modernization, and SaaS consolidation.

Optimizing Costs in Your Data Workflow

Watch dbt Labs exclusive virtual launch event, where executive and product leaders reveal the latest dbt Cloud innovations designed to help you deliver Data That Works. You’ll witness detailed demos, learn about our newest releases, and get a peek into what’s coming to dbt Cloud over the next few months.

dbt Cloud Launch Showcase

Watch this on-demand webinar with experts from the analytics engineering space, Jason, Lauren, and Ian, as they discuss industry benchmarks, macro trends, and strategies for building effective data organizations. We'll discuss the results from our State of Analytics Engineering annual survey which reveals the experiences of data practitioners and leaders.

Explore the evolving landscape of data analytics
 Learn how both large and small data teams are adjusting to changes in analytics tools.
- Understand the impact of macro-environmental shifts on data teams and their leaders.
- Discover essential information about Generative AI and its relationship with data teams.
- Q&A: Share your thoughts, reactions, and questions. We're all ears!

2024 State of Analytics Engineering

Curious about how dbt Cloud can simplify your data workflows? This demo offers a hands-on look at how dbt Cloud helps you build trusted data pipelines, accelerate analytics workflows, and maintain data quality at scale. Learn how to easily develop models in the IDE, seamlessly analyze data, and efficiently schedule jobs within dbt Cloud to deliver reliable data, faster. Our product experts will showcase key dbt Cloud features in Snowflake. 

You’ll learn how dbt Cloud enables:
o Data modeling with the slick new IDE
o Job automation within a simple UI
o Lineage graphs directly integrated in your development environment

dbt Cloud Demo

Building a business case for mathematical optimization can be a daunting task, especially if you are asking for buy-in from various decision makers. Although this decision intelligence AI technology is a powerful and ubiquitous force in our modern world, some people lack an awareness of what mathematical optimization is and the value it can provide.

In this webinar, discover how to:
-Communicate the unique value of optimization in a way that decision makers will understand.
-Build a business case for the use of optimization within an organization, including overcoming organizational resistance and demonstrating how optimization provides a unique value-add that other tools are missing.
-Answer frequently asked questions from decision makers and c-suite executives.

After watching this webinar, you will have the tools needed to explain the value of optimization to internal decision makers and get the buy-in needed for optimization projects.

Join special guest Steve Sashihara, Princeton Consultants CEO and author of the book “The Optimization Edge,” and Jerry Yurchisin, Gurobi Data Science Strategist for this practical webinar.

Ready, Set, Optimize: Securing Buy-In for Optimization Projects

Data science is used to help improve decision-making, and is now applied to nearly all industries, academic research and curriculum that require enhanced decision-making with complexity. This webinar will explore how mathematical optimization is an invaluable tool to enhance decision-making and how it works seamlessly with machine learning to help problem solvers get the most out of their well-tuned predictive models. 

In this webinar, we will deliver:

• A brief introduction to math optimization and its use cases.
• A new modeling example in action that truly takes you from data to optimal decisions.  
• An example from Gurobi’s set of ready-to-solve models, where you can use optimization with no prior experience.

We’ll then wrap up by covering all the learning material Gurobi has made freely available for anyone to use. Don't miss out – let's dive into the world of data-driven decision-making together.

Jerry has over a decade of experience in operations research, data science, and visualization, specializing in enhancing decision-making with data. Before joining Gurobi, Jerry worked in consulting  at OnLocation, Inc. & Booz Allen where he supported numerous projects by building and customizing mathematical optimization models and leveraging machine learning, applied statistics, and simulation—all to support decision-making through data-driven narratives. Jerry also has a background in college-level mathematics instruction. Now, at Gurobi, Jerry aims to promote the integration of mathematical optimization into the data science and broader AI communities.

Material covered during the presentation will be made available in the attachments tab beneath the video.

Where Data Meets Decisions: Combining data science and mathematical optimization

Join us for a forward-looking webinar as we delve into the rapidly evolving data landscape. Over the past two decades, the emphasis has shifted towards accelerating insights from structured data and analytics. Yet, with the advent of deep learning and generative AI, we are witnessing an unprecedented demand for data handling and computations on an unprecedented scale.

In this engaging session, we will unveil the keys to architecting a cutting-edge data ecosystem that can meet the challenges of tomorrow. Our discussion will revolve around three crucial pillars: performance optimization, cost efficiency, and platform scalability. These are the cornerstones that make the next generation of data technology a feasible goal for any enterprise.

Take advantage of the opportunity to gain valuable insights into today's data ecosystem and its impending challenges. Join us as we explore the considerations and strategies required to build the data technology of the future.

Optimizing AI Strategies in the Modern Data Landscape

This is an interview with Dr. Jans Aasman, CEO of Franz, Inc. and designer of the Allegrograph knowledge graph engine. In this interview, we cover everything from the role of Lisp (and Lispers), the versatility of RDF hypergraphs, the value of Allegrograph, and the future of artificial intelligence, machine learning and inferential logic in the graph space.

DSC The Cagle Report Episode 2 - From Lisp to SHACL

Modernizing analytics is an opportunity for companies to reassess their needs, drive transformation and streamline existing processes and data pipelines. For customers using last-generation technology that is approaching end-of-life support, such as Oracle OBIEE, projects often contain significant complexity across three critical areas: 
 

Projects rely on legacy data architectures, with patchwork data pipelines consisting of many analytic “choke points” that can cause inefficiency in moving from raw data to insights.
 

The technology stacks themselves are last-generation, with many moving parts and heavily-specialized configuration needs, and
 

BI delivery typically follows outdated modeling and design principles, with functionality that no longer meets the needs of modern business consumers. 
 
In this webinar, learn how Incorta delivers an agile approach to modern analytics that streamlines the data architecture, unifies the analytics platform through a simplified and scalable technology stack and offers unrivaled access to operational data with faster time-to-insights. 
 
Hear from Comcast around their Oracle BI modernization journey, including how over 150 separate back-end ETL processes and legacy reports were decommissioned through a single Incorta deployment that reduced BI development cycles by over 88%.

How Comcast Modernized their legacy Oracle BI

With the release of SAS Container Runtime (SCR), organizations can execute models and decisions outside of SAS using standard technologies. Containerized deployments are lightweight to save on cloud costs, portable to enable easy movement across environments, and scalable to meet traffic needs. In this talk, we will discuss how IT and MLOps Engineering teams can create repeatable Continuous Integration/Continuous Delivery (CI/CD) processes to efficiently test and promote new models and decision flows using containers.

Speakers:
Sophia Rowland
Product Manager of MLOps/ModelOps at SAS. Previously, Sophia was a Systems Engineer focused on Data Science and ModelOps applications.

Glenn Clingroth
Principal Software Developer at SAS. Glenn focuses on MLOps and model integration technologies and he is one of the architects for SAS Container Runtime.

Alessio Tomasino 
Principal Technical consultant at SAS and specializes on complex distributed software systems. He currently focuses on technologies in container and cloud computing.

Best Practices for Adopting Containers within your MLOps Process

mlops

Cloud

CICD

containers

Scalability

Continuous Integration

Continuous Delivery

Data Science

Data Modelling

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Best Practices for Adopting Containers within your MLOps Process

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