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: 5

In today's organizations, you need to get relevant data quickly to drive faster business decisions. With big data, sometimes that's easier said than done.
 
SAS® Business Intelligence offers predictive insights with the ability to understand the past, monitor the present and predict outcomes, no matter the size or complexity of your data. In fact, SAS helps you deliver accurate, valuable information – from Hadoop or any other big data source.
 
Plus, SAS offers an integrated, flexible presentation layer for the full breadth of SAS Analytics capabilities: data and text mining, statistics, predictive analytics, forecasting and optimization.
 
One of the key components of SAS Business Intelligence, SAS Visual Analytics, offers self-service data discovery, enabling even nontechnical business users to explore billions of rows of data in seconds. With this tool, you can discover more opportunities and make more precise decisions, easily publishing reports to the Web and mobile devices.

The power of large language models has revolutionized the infusion of AI into many aspects of everyday life. As organizations exploit this technology, we will undoubtedly see and feel an impact on the quality of services they provide. In this talk we will share compelling use cases demonstrating significant productivity leaps in critical sectors that require scrutiny, like law enforcement and MarTech, where the application of LLMs is not just beneficial but transformative. Furthermore, we delve into how marketers can utilize LLM-based technologies in a safe and reliable manner as creative and efficient aids for crafting compelling narratives and strategies

• Hear an AI and Industry leader’s perspective on responsible delivery and application of this technology
• Witness immense productivity gains realized in high-impact use cases such as law enforcement
• See how LLM-based MarTech technology can be used to enhance marketers' creativity and efficiency

Speakers:
Marinela Profi leads Market Strategy for Generative AI at SAS. With her background as Data Scientist, she 'and has transitioned brings a unique perspective bridging the realms of technology and marketing. She leads the strategy and positioning of AI solutions at SAS for organizations in leading industries such as banking, manufacturing, insurance, government and energy. Profi has a Bachelor's in Econometrics, a Master of Science in Statistics and Machine Learning and Master's in Business Administration (MBA). On her LinkedIn and YouTube channels, she shares her journey to inspire others who have interest in the data and tech world.

Mac Carlton spearheads the advancement of generative AI strategy and capabilities at SAS, focusing on responsibly deploying generative AI to modernize marketing approaches with customers across different industries. Committed to putting customers at the forefront, he collaborates with organizations in healthcare, financial services, and retail sectors.

Making Generative AI Real through Industry-focused Digital Assistants

Those in data-driven roles often struggle to facilitate effective communication with non-technical
stakeholders and persuade them to make business decisions using insights. Data storytelling is a critical
skill that helps data scientists and analysts give key decision-makers the ability to make data driven
decisions. Using augmented analytics and business intelligence to influence others not only makes
others’ lives easier but helps you make an impact with your work.

In this webinar, we’ll break down different communication styles, how they show up in the workplace,
and how each can use augmented analytics and business intelligence to easily share insights. No matter
how you prefer to communicate, you’ll gain tips and tricks on how to best share information that you
can act on in your daily role.

Presented by: 
Briana Ullman 
Global AI Product Marketing Manager
Briana Ullman is a Product Marketing Manager at SAS with a focus on the intersection of AI and
business intelligence. She is passionate about bridging the divide between audiences of any
background, whether they’re analytics pros or newcomers, to give everyone the ability to make
more insights-driven decisions. Briana is an alum of North Carolina State University and lives in
Denver, CO.

Anna Prioletti 
Global AI Product Marketing Manager
Anna Prioletti is a Product Marketing Manager at SAS, focusing on business intelligence and SAS
Viya. With experience in DX, marketing sciences, and communications, she loves combining data
with consumer behavior. Anna received a marketing and psychology degree from the University
of Central Florida and loves being a continuous learner.

Influence Data-Driven Decisions Based On Your Communication Style

Many industries are turning to cameras and computer vision to solve novel and non-traditional challenges that may be hard to accomplish with traditional data sets. Example use-cases include biomedical imaging in Healthcare, smart cities for governments, and automated defect detection in manufacturing. However, just having a computer vision model is not enough to generate the necessary insight to drive action. There is a need to have a complete analytic pipeline that has all the necessary components to transform input data sets into actionable insights. This analytic pipeline must have sufficient accuracy to have confidence in predictions, minimal latency to provide these decisions in a timely manner, and have the ability to deploy and scale to multiple cameras and locations. Come and see how this analytic pipeline for a manufacturing use-case can be built and operationalized using the SAS Platform. We will show how the analytic pipeline consumes multiple data sources, contains multiple post-processing analytic techniques to interpret and generate actionable information from the computer vision model outputs, leveraging technology optimized for compute, and the intermixing of both SAS and Open Source technology in the same analytic pipeline.

How to Operationalize & Scale Pipelines with Computer Vision Models at the Edge

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

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

Often the hardest part of a project is the “final mile” - getting deep learning models into production and yielding results. In order to accelerate this process, you need an effective approach that supports ONNX (open neural network exchange) format models for inferencing -- allowing you to leverage trained ONNX format models to implement streaming analytics in the cloud or at the edge.

You can focus on training the most effective model for the problem at hand by simplifying the task of deployment on various supported GPU and CPU hardware with capabilities that remove a lot of the model deployment complexity, 

In this webinar, you will learn: 
·        How to inference models like neural nets for computer vision use cases, including object detection and classification. 
·        How the integration of ONNX Runtime with SAS Event Stream Processing combines streaming analytics with simplified testing and deployment on different GPU and CPU accelerated hardware. 
·        How you can deploy ONNX format models to gain maximum benefit using sample projects provided through GitHub examples.

Implementing Deep Learning Models in Streaming Analytics Projects

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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