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

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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

An interview with Brendon Bartholomew, President of Vector Aerial, on the use of drones in both military and civilian contexts, as well as a discussion about how Drone AI works and where it's heading

The Cagle Report - Episode 1 - Send in the Drones

The amount of customer data available to brands has absolutely exploded in the past decade, giving brands an opportunity to create personalized experiences.

But with the introduction of privacy regulations, thousands of new marketing tools, new customer touchpoints, and more, this data can simply cause chaos.

It's hard to measure and manage, but most importantly, blocks brands from delivering winning customer experiences. The fun stuff! Come to this session to learn how brands like Airbnb, Venmo, and Burger King solve data chaos.

In this webinar you’ll learn:
Why collecting, organizing, and activating customer data becomes more difficult as you scale
How to implement a customer data infrastructure that allows you to execute on your data strategy over time
How to leverage your customer data to deliver better personalization across channels

It's Time to Turn Data Chaos into Order

Data scientists' time is valuable. Computing resources are expensive. With only 87% of projects ever making it to production (Source: VentureBeat), organizations often overcommit to costly projects that bear little fruit. Data science teams need a way to assess project feasibility without diving head first.


Join us to learn how you can create a rapid baseline assessment to determine project feasibility using numerical, categorical, and image data. Help your team prioritize work and allocate resources accordingly.


You will learn how to:

Prioritize research, assess multi-modal data sources and optimize DS time 

Use rapid prototyping for quick feasibility assessment

Incorporate output from multiple images into a single prediction

How to Gauge Project Feasibility with Rapid Prototyping using Code-First Data

Ready. Set. Cortex Recording - How an analytics simulation game fuels learning | Cortex isn’t just a simulation. It’s a competitive simulation, complete with a leaderboard to compare results with other competitors. Hear how the games benefit both students and teachers as they adopt new analytical skills.

In our latest study, The impact of increased digitization on the data science field, the results show that data science professionals spend most of their time gathering, exploring, managing, and cleaning data. While individual data science contributors, as well as managers, recognize the importance of working with clean data, the fun part comes with building models to provide strategic business recommendations.

Cortex, an analytics simulation game, by SAS and in partnership with HEC Montreal, allows data scientists to skip to the fun part and spend more time building models while adding a competitive element to it. Cortex has become a worldwide phenomenon, with competitions as far afield as the U.S., Thailand, Malaysia, Finland and Australia.

Join us to hear from Dr. Jean-François Plante, Professor at HEC Montreal who will give an overview on Cortex's inception and students: Varuni Ratnasiri, and Mitra Farokhnia from the B412 Analytics Post-Grad program at George Brown College who will share their thoughts and experience from their latest Cortex game.

Ready. Set. Cortex – How a SAS Analytics Simulation game fuels learning

There’s a vibrant ecosystem of choices available for data scientists to perform their job. This spans programming languages – such as Python, R and Java – as well as integrated development environments, deployment technologies, virtual machines, Kubernetes and more.

While these choices create a lot of opportunities, they also can lead to option fatigue, resulting in an overcrowded, uneven landscape that makes it difficult for data practitioners and companies to pursue the real priorities to generate business values.

In this talk, data scientist Marinela Profi will explain how ModelOps and MLOps can help you streamline and simplify the process and achieve Responsible AI. She’ll discuss the difference between the two approaches and the important role they play in solving common challenges with the ML lifecycle.

Taking it a step further, she will introduce the concept of an analytics platform to develop, deploy and monitor any type of model to adopt a full life cycle approach. She’ll also discuss how to integrate different open source packages and ensure that proper model governance and audibility best practices remain in place.

Developing, Deploying and Managing Models at Scale

Depending on your point of view, Disaster Recovery can either seem overly complicated or unnecessary, but both of those attitudes can be problematic. The fact is that in today's world, where it is so easy to become the target (or unintended victim) of a criminal or state-sponsored attack, having a Disaster Recovery plan is absolutely crucial. Even a simple ransomware attack can turn into a disaster if all countermeasures ultimately fail.
 
In this webinar, Mirantis Solution Architect Ben Dorman gives you the foundation you need to understand what you need to begin planning your own Disaster Recovery plan including:
 
What constitutes a disaster for you
What your Disaster Recovery plan needs to accomplish
The concept of Application Data Mobility
How cloud native technologies affect your Disaster Recovery plan
 
Join us to begin protecting your infrastructure and start sleeping better at night.

Now's not the time to ignore Disaster Recovery. Learn what you need to know.

By 2030, oil & gas plays across the United States are expected to produce over 60 million barrels of
water every day. Most of this water is characterized by high Total Dissolved Solids (TDS) concentrations,
exceeding 300,000 mg/L in some plays. Even though many upstream operators strive to reuse produced
water for hydraulic fracturing, disposal continues to be the leading water management practice across
the United States. This talk will focus on the development, demonstration, and deployment of a novel
optimization framework for produced water management and beneficial reuse.

The framework is designed to identify cost-effective and environmentally sustainable produced water management, treatment, and reuse solutions. Specifically, the proposed platform will support decision-makers with the: 

(1) coordination of produced water deliveries, 
(2) buildout of the produced water infrastructure in
terms of pipelines and storage facilities, 
(3) the selection of effective treatment technologies, 
(4) the placement and sizing of treatment facilities, 
(5) the identification of beneficial water reuse options, and
(6) the distribution of treated produced water and/or concentrated brine for beneficial reuse. 

This talk will also discuss opportunities and challenges associated with the recovery of critical minerals (e.g., Lithium) from oil & gas produced water.

Project PARETO – DOE’s Produced Water Optimization Initiative

Machine learning and deep learning have become standard tools in a data scientist’s toolbox, applied to generate insights into large amounts of data. But organizations are realizing that these technologies can’t do it all, and when decision making needs to be fast, reliable, and defendable, mathematical optimization is perfect complimentary tool to machine learning. 

Predictive models help answer the question of “What is likely to happen?” but also answering “What actions should be taken?” will allow organizations to add depth and value to their analytics projects where others can’t.

Why and How to Partner Machine Learning with Math Optimization

With great power comes even greater responsibility: AI responsibility to be specific. Organizations, regulators, and data practitioners can all get behind the concept of Responsible AI. It’s more than just understanding how a model is built; It’s about addressing ethics, regulations and law, and human rights to build an inclusive world. So if this is so universal and important, why aren’t we seeing more prioritize this framework? Join us to learn what organizations are doing today to tackle bias and AI risk, plus how data professionals can start a positive step forward with AI, in a more responsible way.

Responsible AI Frameworks: An inclusive future for all

As business decisions are becoming more and more complicated, Decision Analytics are becoming increasingly important and a way analytics teams can differentiate themselves from the competition. When these decisions become extremely complex and it seems the trade-offs are too numerous and intricate to work through, mathematical optimization lends itself as great tool for this job. 

Mathematical optimization is a versatile tool that is easy to start using and adds huge value for any data scientist by addressing the part of a problem that is usually overlooked – the decision.

Navigating Decision Trade-offs: Scratching the Surface with Math Optimization

Predictive performance and resiliency are equally important requirements for model operationalization. Users would rather have resilient models with “good” performance than models that show high predictivity at training and testing, but underperform when deployed, and require frequent tuning. Of the multiple impactors of performance and resilience, it is well known that training data has an outsize impact on predictivity, and data drift is a major influencer of resilience. In this webinar we will discuss two strategies to improve accuracy and resiliency of models: 
- Enriching first party data to create effective training data sets that possess adequate breadth, depth and scale 
- Using the concept of data stability to reduce the impact of data drift and produce resilient models from scratch
 

In discussing these strategies, we will address the following questions:

- Why machine learning models often underperform in production
- How data enrichment can improve model performance
- The concept of data stability, and how it can be used proactively
- Discuss how feature transformation and selection is so critical to model resilience\

Building data pipelines that drive highly predictive, resilient models

Solving Complex Challenges with Gurobi:

Data science and analytics teams are doing more with data than ever before, but more is being expected from such teams to differentiate themselves from their competition. All too often these analytics teams are ending their projects too soon – failing to consider the decisions their analyses inform. That’s where Decision Analytics comes in, and for the most complex problems, Mathematical Optimization. 

Mathematical optimization and Gurobi are fantastic partners to machine learning and can help data scientists get the most out of their predictive models by working where they work – in popular programming languages like Python and R.

Key topics: 
What are Decision Analytics, really?
What are some examples and common approaches used in Decision Analytics?
What is mathematical optimization? What are the problems it can address?
What tools can I use?
How can I get started using optimization and Gurobi?
Who else uses optimization and Gurobi?

The Data Science Key to an Unpredictable Future

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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