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

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. 

Power systems all over the world are experiencing rapid and profound changes. The last decade has seen an inexorable rise in renewable generation technologies, such as wind and solar farms.

In the past, these technologies accounted for only a small fraction of total electricity supply. But now they are a critical part of our power system, and their significance will only continue to grow. These are profound changes that have consequences for every part of the energy sector.

Against this backdrop, there is now more focus than ever on how we can better understand the outlook for the power system. Despite the many detractors, wholesale market models are in widespread use across the energy sector. We seek to use these models to answer a wide variety of questions, including:

Finding the optimal combination of generation and transmission assets to invest in while maintaining low energy cost.
Defining the role of different technologies like wind, solar, storage, and gas in the future power system.
Retiring existing high-emissions generation without adversely impacting reliability, security and affordability of electricity.
Providing models to better understand, design, and conduct stress test configuration on the future power system digitally through the use of prevailing tools such as PLEXOS and Gurobi that are commonly used in the energy sector.
In this webinar, we will offer a high-level overview of how optimization can be used in the energy sector to better support decision-making.

Opening the Black Box: Understanding Optimization in the Energy Sector

Are you making the right decisions for your supply chain network?
Watch this exclusive webinar with Wendy Wang, Director of Optimization Analytics Technology (OAT), as she shares use cases on how optimization can transform supply chain network planning and support better decision making. Wendy is part of the team that was a 2024 Franz Edelman Award finalist for groundbreaking work in supply chain optimization for a major fast-food chain.

What You’ll Learn:
How to improve daily transportation scheduling and stock replenishment to reduce costs and increase efficiency.
How to adjust network coverage and Stock Keeping Unit (SKU) mapping to make better seasonal and annual decisions.
How to determine the ideal number, size, and location of facilities to maximize performance and reduce costs.
How to optimize product and material flows to improve delivery speed and service levels.

Supply Chain Network Planning Optimization

In the age of sector coupling and volatile electricity markets, energy storage is key to unlocking resilient and profitable renewable energy systems.

Join us and our guest speaker Leon Haupt, Founder & Head of Business Development at Phelas GmbH—a pioneer in innovative energy storage solutions—to discover how Gurobi’s optimization engine drives smarter decision-making in energy infrastructure design, from techno-economic feasibility to operational strategy.

Learn how advanced mathematical models simplify the complexity of hybrid power plants and help future-proof your investments against regulatory and financial risks.

Smart Design of Energy Storage Projects - From Complexity to Clarity

There is a common consensus that state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms are powerful in terms of their accuracy, but they are also perceived as opaque not being transparent about how they arrive at their decisions.  This prevents the adoption of these powerful algorithms in Data-Driven Decision-Making. Even when in place, they can have a detrimental impact on the citizen, and there are well-documented examples of discriminatory outcomes in high-stakes algorithmic decision-making. Therefore, there is an urgent need to strike a balance between three goals, namely, accuracy, explainability and fairness.

In this talk, we will navigate through some of the latest advances in Mathematical Modelling and Optimization to enhance the transparency and fairness of ML algorithms. We will first focus on the training of ML models that trade off accuracy, explainability and fairness. Then, we will focus on the task of providing explanations to an existing ML model by means of the burgeoning field of Counterfactual Analysis.

On a (Short) Optimization Tour Through Transparent and Fair ML with Prof. Dolores Morales

Join us for our Gurobi Days Digital Academic webinar series, focusing on Optimization for Good and featuring top academics changing the world with mathematical optimization. Dr. Özlem Ergun joins us to share some of the impactful research and collaborations she has led with global non-profits harnessing the power of optimization.

ABSTRACT: In this talk, Dr. Ergun will describe her efforts in optimizing global food aid operations using mathematical programming in collaboration with UN World Food Program and USAID for over 15 years.

Gurobi Academic Webinar with Dr. Ozlem Ergun: Optimizing Global Food Aid Operations

Quantum computers have received extensive publicity throughout the last 20 years, showing potential for major performance improvements over classical computers when it comes to challenging industrial problems. However, while progress is being made, questions remain about how to effectively translate the underlying theory into practical computers. The resulting debate between quantum enthusiasts and quantum skeptics can be difficult to navigate, especially for those who are new to the underlying concepts.

In Part I of this presentation, we explored the technical details of quantum computers, with a focus on quantum annealers. Now, in Part II, we discuss the logic gate architecture, revealing the underlying linear algebraic concepts that are familiar to many optimization practitioners.

The connections between classical mathematical optimization concepts and quantum computing can get lost in the collection of different concepts and notations associated with the latter. However, a closer look reveals that numerous mathematical optimization concepts can help us learn how to solve challenging problems in a quantum computing environment.

We examine how the entanglement of qubits results in significant computational power, and how that power can be harnessed into algorithms that can potentially solve complex problems much faster compared to a classical computing environment.

An Intro to Quantum Computing for Optimization Practitioners Part II: Logic Gate Quantum Computers

Quantum computers have received extensive publicity over the last 20 years, promising potentially major performance gains over classical computers for a range of industrial problems. However, while progress is being made, questions remain about how to translate the underlying theory into practical computers. The resulting debate between quantum enthusiasts and quantum skeptics can be difficult to navigate, especially for those who have little or no familiarity with the underlying concepts.

This tech talk will not take sides in the debate, nor make predictions about quantum computing capabilities of the future. Rather, it will try to present the technical details of quantum computers in a way that resonates with mathematical optimization practitioners. It’s easy to overlook the connections between classical mathematical optimization concepts and quantum computing. However, many mathematical optimization concepts can offer guidance for solving challenging problems in a quantum computing environment.

An Introduction to Quantum Computing for Optimization Practitioners, Part I: Quantum Annealers

Join us for an exclusive webinar hosted by S&P Global Market Intelligence, featuring Justine Iverson, Head of Corporates & AI, and George Sivulka, Founder and CEO of Hebbia. This session will provide a deep dive into the transformative impact of artificial intelligence on the financial services sector, focusing on its applications across private capital, investment banking, asset management, in addition to the legal sector and for corporations.  
 
Justine Iverson will share industry insights into recent trends, review the transformation we’ve seen over the past 18 months, and share recent AI solution use cases customers are exploring. George Sivulka, a pioneer in large language model (LLM) applications, will share insights from his groundbreaking work in AI. As the founder of Hebbia, a $700 million generative AI company backed by industry giants Peter Thiel and Andreessen Horowitz, George has been at the forefront of AI innovation. George and Justine will discuss the current shortcoming and opportunities of AI technologies as well as a view into the future with trends to watch over the next few years. 
 
Join us for a discussion around: 
-The transformative impact of AI, across private capital, investment banking, asset management, legal, and corporations
-Latest tech developments and common customer use cases 
-Demystifying ‘agentic AI’ for strategic planning considerations 
-The current shortcomings and opportunities of AI technologies 
-What’s next: George and Justine will give us their view into trends to watch over the 3–5-year horizon
 
Don't miss the opportunity to gain valuable insights from industry leaders and explore the future of AI with our distinguished speakers. 
 
Watch this webinar replay.

The AI Arms Race: Who Will Win?

CRM data is often messy, full of duplicates, incomplete hierarchies, and inconsistent records. These issues drain time, limit visibility, and make it harder to act on opportunities. Teams often rely on manual cleanup, but it slows down workflows and can’t keep pace with growing data volumes. In this session, we show how a global media company created a single, trusted CRM by unifying records, automating updates, and embedding intelligence directly into Salesforce with S&P Global Market Intelligence solutions including the Salesforce App, Cross Reference Services, and Kensho Link.
____________________________________________________________________________________

What you’ll take away:
• Methods for consolidating CRM records, resolving duplicates, and maintaining consistent hierarchies
• How to reduce reliance on manual cleanup with scalable automation
• Ways to ensure CRM data stays accurate and continuously updated for downstream analytics
• A live demo of streamlined CRM data pipelines directly in Salesforce

Winning the Ad Dollar: Data-Driven Sales Enablement

In this webinar, Gurobi experts Silke Horn and Robert Luce dive into advanced portfolio optimization and backtesting with discrete constraints using Gurobi's MIP technology. Learn how to incorporate complex real-world factors like fixed costs, transaction limits, and cardinality constraints to optimize investment strategies. Watch to discover best practices, insights, and practical tips for unlocking alpha in your portfolio.

Unlocking Alpha with Gurobi

Unstructured text, from earnings calls to regulatory filings, contains market-moving signals, but extracting those signals reliably is a challenge for quant teams. Traditional NLP methods often miss financial nuance, limiting their usefulness in alpha generation. In this webinar, we introduce ProntoNLP, a finance-trained NLP framework designed to structure unstructured text at scale. With sentence-level labeling, sentiment scoring, and context linking, ProntoNLP helps investment teams uncover signals, validate hypotheses, and integrate textual insights directly into quantitative workflows.

What you’ll take away:
• How textual data can be transformed into structured features for quantitative research
• Methods for extracting sentiment, entity-level signals, and strategic themes from transcripts
• How signals from earnings calls extend to filings and other unstructured sources
• Practical applications for integrating textual analytics into systematic investment strategies

Quantext: Investment Trends in Textual and Alternative Quant Data

With Gurobi 12.0, users can leverage enhanced capabilities for solving mixed-integer nonlinear programs (MINLPs) to conveniently model nonlinear constraints.

Curious to see these enhancements in action? Walk through real examples, get expert insights, and discover practical strategies for modeling and solving MINLPs seamlessly using Python and Gurobi. Whether you’re tackling challenges in energy, supply chain, finance, or beyond, this session will equip you with the tools and knowledge to unlock new possibilities in optimization.

Solving MINLPs with gurobipy

Explore how Gurobi AI Modeling makes optimization more accessible than ever! In this webinar, Jerry Yurchisin demonstrates how generative AI can streamline the modeling process using Prompt Engineer and Modeling Assistant. Whether you're new to optimization or looking to enhance your expertise, this session provides practical insights, best practices, and hands-on examples.

Simplifying Optimization: Learn, Build, and Solve with Gurobi AI Modeling

Integrated data management, customer service and support, and creating personalized marketing content continue to rank as high-impact generative AI (GenAI) use cases, according to S&P Global Market Intelligence. Yet only 18% of people polled by S&P Global indicate GenAI tools are fully integrated across their organization. How can you expect GenAI advancements to impact your company, alongside a host of other evolving economic trends?

In this webinar, our panel of experts from S&P Global examine the high-traction trends shaping business and economic environments throughout 2025, including:
• Economic fluctuations impacting private investments, capital markets, and M&As
• How to prepare a GenAI-ready infrastructure
• Accounting for extreme climate events within the supply chain 
• Commercial real estate loan delinquency at U.S. banks 

Watch this session to stay apace with the global trends that will influence your company’s technology, economic, and business strategies.

Outlook for 2025: Critical Trends Shaping Business & Economic Environments

S&P Global Market Intelligence hypothesized firms that remain on-topic when answering questions during earnings call Q&As have superior stock performance compared to those who pivot to adjacent or unrelated topics. Relatedly, S&P Global proposed that firms proactively addressing key issues in their prepared remarks in earnings calls, before analysts ask about them, have better stock performance compared to those that respond reactively.

To fully test out these hypotheses, S&P Global processed the text of several earnings calls through Snowflake Cortex LLM Summarization. The results?

Watch this webinar as S&P Global experts review the results of using LLMs to score executive performance on earnings calls to help investors make better, data-informed decisions. We’ll talk through how vector embedding the earnings calls’ text enabled us to identify answers on- or off-topic, and showcase this real-world application of LLMs in the field.

Questioning the Answers of AI in Action: LLMs Enter the Boardroom

Enterprises are under pressure to use AI and advanced analytics; however, their infrastructure is full of data silos that span across on-premises, multiple clouds, and at the edge. This leads to inconsistent data management, increased security risks, and complex governance policies, introducing bottlenecks that prevent enterprises from scaling AI effectively.

Watch this webinar to learn how a hybrid platform can unify data across all environments (on-premises, in the cloud, and at the edge), eliminating data silos and enabling consistent secure data management. By adopting a hybrid platform, enterprises can maintain control over data governance while ensuring access to trusted data for AI and analytics initiatives.

Hybrid Data Platforms: The Backbone for Enterprise AI

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

Entity resolution isn’t just a data management chore—it’s a core technical challenge for data engineering and analytics teams. Even “clean” company data becomes unreliable without consistent mapping to industry-standard identifiers. But traditional approaches like fuzzy matching and rules-based mapping often break down in the face of ambiguous names, multilingual variants, and complex corporate hierarchies.

In this webinar, S&P Global explores how AI-powered entity linking goes beyond string similarity by leveraging contextual signals from both the source dataset and the target reference database. You’ll see how this approach enables more accurate and scalable entity mapping across massive datasets.

Key takeaways:
• Why fuzzy matching and deterministic rules fall short at scale
• How AI models incorporate context and metadata to improve precision
• Practical applications: CRM cleanup, security master file enhancement, and platform integrations

Data Entity Mapping with AI - The Challenges and the Solutions

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

Welcome to the big data and data management community on BrightTALK. Join thousands of data quality engineers, data scientists, database administrators and other professionals to find more information about the hottest topics affecting your data. Subscribe now to learn about efficiently storing, optimizing a complex infrastructure, developing governing policies, ensuring data quality and analyzing data to make better informed decisions. Join the conversation by watching live and on-demand webinars and take the opportunity to interact with top experts and thought leaders in the field.

Big Data and Data Management

As an IT professional, many of the problems you face are multifaceted, complex and don’t lend themselves to simple solutions. The information technology community features useful and free information technology resources. Join to browse thousands of videos and webinars on ITIL best practices, IT security strategy and more presented by leading CTOs, CIOs and other technology experts.

Best Practices for Adopting Containers within your MLOps Process

Presented by

About this talk

Data Science Central