Name: The Battle for Top AI Talent
Start: 2022-04-29T15:00:00Z
End: 2022-04-29T15:45:14.000Z
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. 

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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

Today's enterprises struggle to unlock real value out of data that is stuck in legacy relational databases,
data warehouses and data lakes. Modernizing disparate data silos to a single data fabric is key to
managing change over time and creating data-driven innovation and value. Join this session to learn
how you can build a modern data fabric leveraging the Lumada DataOps Suite from Hitachi Vantara
along with MongoDB Atlas. In addition, hear from a utility customer that is reaping the benefits of the
data fabric and see a demo of how you can create a modern data pipeline to drive great enterprise data
value.

Improving Your DataOps Practices with Data Fabric

Relational databases in the 1980s were typically designed using the Codd-Date rules for data normalization. It was the most efficient way to store data used in operations. As BI and multi-dimensional analysis became popular, the relational databases began to have performance issues when multiple joins were requested. The development of the star schema was a clever way to get around performance issues and ensure that multi-dimensional queries could be resolved quickly. But this design came with its own set of problems.

Unfortunately, the analytic process is never simple. Business users always think up unimaginable ways to query the data. And the data itself often changes in unpredictable ways. These result in the need for new dimensions, new and mostly redundant star schemas and their indexes, maintenance difficulties in handling slowly changing dimensions, and other problems causing the analytical environment to become overly complex, very difficult to maintain, long delays in new capabilities, resulting in an unsatisfactory environment for both the users and those maintaining it.

There must be a better way!

Attend this webinar to learn:

- The three technological advances in data storage that eliminate star schemas
- How these innovation benefit analytical environments
- The steps you will need to take to reap the benefits of being star schema-free!

Death of the Star Schema

In this latest Data Science Central webinar, meet Shadow IT’s younger sibling. Shadow AI is the result of one-off AI initiatives inside organizations, where siloed AI teams buy their own infrastructure or use cloud compute resources. While no organization wants to stifle the ambition of its data science teams, this decentralized approach results in many AI initiatives never making it to production. Learn from Gijsbert Janssen van Doorn at Run:ai how to centralize AI so that it is accessible and productive across an entire organization.

You’ll learn:
- Why Shadow AI exists, and how to prevent it at your organization
- The benefits of centralizing your AI compute resources
- What a best-practice centralized AI infrastructure looks like

Shadow AI, the Silent Killer of Deep Learning Productivity

KEH is a ReCommerce camera specialist in finding and providing the best high-end camera gear at an attractive price. To better serve a growing demand for used material via their multi-channel pipelines, KEH needed to rethink entirely its digital model and bring data to the heart of its business.
 
Switching from a manual approach (Excel and Legacy Applications) to the Google Cloud analytics suite (including Dataprep by Trifacta, BigQuery, Cloud Functions, and DataStudio) has been transformative to automate their process to sourcing the best gear, optimizing price/quotes (based on the supply, demand, and competitors), capturing omnichannel customer signals, and drive segmented campaigns.
 
In this webinar, Aftab Alam, Sr Director of Technology @ KEH will unveil his automation of the product lifecycle management platform. What will you learn?

- Understanding KEH specific data challenges to automate their analytics (silo data sources, data quality, and data profiling, variable data format from search engine, data quality issues)
- How to bring numerous data channels (search engine, transaction, customer engagement, ERP, warehouse, competitive web crawl) into a centralized cloud BigQuery data warehouse
- How to clean, unstructured ( web scraping data) and structure (JSON, CSV & XML), combine and derive trustworthy insight with Dataprep by Trifacta 
- The dos and don’ts in transforming your eCommerce data journey

See the solution end-to-end live!

Boosting Your eCommerce Business with Marketing Analytics

In this latest Data Science Central podcast, Dan Chaney, VP of Enterprise AI & Data Science Solutions, will talk about some key best practices for recruiting and retaining to AI & Data Science talent. Dan will be joined by other industry experts, including representatives from Dell Technologies, to talk about ways that companies are implementing successful data science programs, how organizations are trying to drive alignment between the AI & Data Science function with various business units and C-level stakeholders, and what today’s top data science talent is looking for from their employers.

The Battle for Top AI Talent

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