Name: Code-First Data Science Using DataRobot’s AI Cloud Platform
Start: 2022-07-21T16:00:00Z
End: 2022-07-21T16:31:34.000Z
Location: BrightTALK
Rating: 0

DataRobot powers the AI-driven enterprise, enabling users throughout the organization to make business decisions unmatched in simplicity, speed, and accuracy. Its revolutionary approach to automated machine learning harnesses hundreds of cutting-edge algorithms to discover and deploy the best predictive models for every situation and delivers accurate business predictions at scale.

For many Data Scientists, writing code to explore, transform, and visualize data is an essential part of getting value from data. 

Hear more about DataRobot’s AI Cloud tools for code-first Data Scientists and developers from Chief AI Strategist, Ben Taylor and watch how you can put it into practice with some short demos. 

You’ll learn about:
Companies using code-based tools to get more value from data and models
Leveraging unlimited compute power and storage with a notebook platform
Streamlining data exploration and visualization with DataRobot AI Cloud

Don't miss out on hearing our experts talk about DataRobot’s AI Cloud, a unified environment optimized across the entire AI lifecycle from data to value.

Code-First Data Science Using DataRobot’s AI Cloud Platform

During this webinar, Paul Winsor from Snowflake and Lisa Aguilar from DataRobot will explore how AI Cloud can open the door to innovation and growth in the retail industry. You’ll learn how to exceed customer expectations by delivering the right amount of goods, at the right time, while maintaining high operational efficiency and ROI. 

Join us to start leveraging AI Cloud as your greatest strength with the combined experience of Snowflake and DataRobot. They’ll help you use your own data to better understand customer needs, and find unique opportunities for innovation and growth into the future.

Retail Unleashed: Foresee the Future Using AI

Financial services organizations are at a unique place and time in their evolution, as the potential for AI grows by the day. Across the highly regulated industry, banking is facing major challenges and must orchestrate a trusted view of data for compliance and operationalization to enhance performance, lower costs, improve scalability and reliability - to ultimately drive business transformation, growth, and a competitive edge.

We’ll share how banking organizations can use AI to help tackle some of the greatest challenges like credit losses and fraud, and unlock opportunities like greater customer retention and leveraging non-standard data sources. 

Join us to find out how Snowflake's Cloud Data Platform works together with DataRobot’s AI Cloud platform to uniquely enable financial services organizations to evolve their businesses with data at their core.

Financial Services Unleashed: How Banks are Winning with AI

In this segment, Patrick Kovalik from Snowflake and Sally Embrey from DataRobot will dig deeper into data and AI solutions that address today’s critical healthcare challenges like labor shortages and algorithmic bias. They’ll also cover how to close the communication gap between data scientists and healthcare executives.

Join us to see how Snowflake and DataRobot’s end-to-end data solutions can address today’s challenges in the healthcare industry, transform your business for the better and ultimately save lives through data and AI.

Healthcare Unleashed: How to Unlock Healthcare Data Insights

O machine learning está mudando a forma como organizações de todos os setores lidam com análises preditivas. Mesmo com toda a movimentação e jargões de mercado — machine learning, inteligência artificial, deep learning e modelagem estatística — é cada vez mais difícil separar o fato da ficção à medida que você avalia soluções de machine learning.

Em meio ao desafio de contratar e reter de talentos — cientistas de dados, engenheiros, analistas, profissionais de TI — torna-se um pesadelo decidir quem pode alavancar e implementar tecnologias de análises preditivas e adotar com sucesso o machine learning.

Pontos chave para o sucesso do Machine Learning na sua organização

Companies are spending more than ever on marketing. According to the Harvard Business Review, it is estimated that global spending on media is expected to reach $2.1 trillion by 2019. But is all that money effective in producing improved ROI? Without knowing which channels are driving sales, or more specifically, which individual marketing activities are working, marketing spending is a black box.

On this webinar, Data Scientist Colin Priest of DataRobot explains the importance of multichannel, multi-touch attribution to accurately measure the success of your marketing efforts – and how automated machine learning offers the shortest path to success.

Multichannel Marketing Attribution with Automated Machine Learning

Within the federal sector, machine learning and artificial intelligence are treated like a distant possibility rather than an immediate reality. Considerable confusion exists about how the government can implement technology to automate predictive analytics and free data scientists to make educated decisions in real time, rather than crunch algorithms.

 

The objective of this webinar is to help educate the federal community about the realities, and real possibilities, data science can provide. See how agencies can start implementing machine learning for mission success.

Machine Learning for Mission Success: Data Science for the Federal Executive

There is no doubt about the impact of predictive analytics in any insurance company – large, medium, or small. The question that everyone is asking: how can we bring a sustainable, cost-effective, and consumer centric predictive analytics strategy within the organization? Machine Learning Automation is the answer to that question.

This 60 minute on-demand webinar provides an overview of predictive modeling, machine learning, and Artificial Intelligence (AI) in the insurance industry, offering insights into how these technologies will impact growth and profitability. You’ll also learn the three main aspects of a sustainable and cost-effective predictive analytics strategy in an insurance company:

Analytics Strategy of a 21st Century Insurance Company

Don’t be naive when it comes to automated machine learning. Despite the unprecedented speed and ease of creating automated predictive models today, the human mind is still essential for generating good models.

 

From selecting the right problem to solve to preventing algorithm bias, machine learning is still an art and a science. To reap the benefits of automated machine learning, Jen Underwood, Founder of Impact Analytix, will share the most common mistakes – and battle-proven practices –  to help you build better models.

How to Avoid Building Bad Models

Machine Learning has become a competitive differentiator in a big data world. Vast amounts of data are already overwhelming existing BI tools and analytics processes. When faced with hundreds of variables, a human’s ability to efficiently identify new insights or detect changing patterns manually has also been exceeded. To address these challenges, BI and analytics professionals are adopting user-friendly, automated machine learning solutions.

 

In this on-demand webinar, recognized analytics industry expert Jen Underwood discusses how BI and analytics professionals can get started with automated machine learning.

Moving from BI to Automated Machine Learning

In this webinar, Greg Michaelson, PhD, and Head of DataRobot Labs, reviews the practical first steps an organization takes towards becoming an AI-driven enterprise and remaining competitive in the coming years.

The Fast Path to Success with AI

Predicting customer behavior is quite challenging. However, knowledge of a customer at an individual level offers enormous benefits. A company with such knowledge can provide better customer experience and retention, targeted marketing, increase sales, and proactive care. But many existing customer models are very macro in nature and fail to deliver at an individual level.

Machine Learning allows us to build such micro level models by taking into account all digital touch points of a customer with the company. These models will help predict what a customer is going to do next, what their near-future behavior will be and what response to anticipate from an action.

On this webinar, you’ll hear the current state of machine learning in Customer 360 – and then learn how you can stay one step ahead in building highly customer-centric models for your business.

Enhancing Customer 360 Models with Automated Machine Learning

In order to run successful machine learning projects, and create highly-accurate predictive models for your business, you need effective data preparation. Although machine learning automation provides safeguards to prevent common mistakes, you’ll still want to correctly prepare, shape and format your data to generate optimal models.

 

In this on-demand webinar, Jen Underwood, Founder of Impact Analytix reviews how to organize data in a machine learning-friendly format that accurately reflects the business process and outcomes. She shares basic guidelines, practical tips, and additional resources to help get you started mastering the essence of predictive model data preparation.

Data Preparation Essentials for Automated Machine Learning

Compliance organizations within banks and other financial institutions are turning to machine learning for improving their AML compliance programs.  Today, the systems that aim to detect potentially suspicious activity are commonly rule-based, and suffer from ultra-high false positive rates.  Automated machine learning provides a solution to address this challenge.

 

In this webinar, Dan Yelle, a Customer-Facing Data Scientist for DataRobot will show how automated machine learning can be used to reduce false positive rates, thereby improving the efficiency of AML transaction monitoring and reducing costs.

Enhancing Anti-Money Laundering (AML) Programs with Automated Machine Learning

Preventing fraud is a mission-critical objective of every financial institution, including fintechs. But those committing fraud continue to evolve their tactics to evade detection by even the best prepared organizations.

 

On this on-demand webinar, you’ll get an overview of the current state of machine learning in fraud detection – and learn how you can stay one step ahead of those looking to harm your business.

Advances in Fraud Detection with Automated Machine Learning

Machine learning has quickly become the tool of choice for pricing a variety of financial products. Instead of utilizing legacy rules-based matrices for pricing, companies have turned to predictive modeling to understand the likelihood of default and overall borrower repayment performance. This has enabled companies to move from older pricing schemes to dynamic risk-based pricing.

Justin Dickerson, General Manager of Global Fintech for DataRobot and Igor Veksler, a leading Customer-Facing Data Scientist for DataRobot have both worked in the alternative finance industry as data scientists and led this transition to risk-based pricing for their respective organizations. As leaders at DataRobot, they currently share their expertise with clients and potential customers looking to leverage machine learning to make the transition to dynamic risk-based pricing.

In this on-demand webinar, Justin and Igor describe how DataRobot can help enable your enterprise to leverage automated machine learning to become a leader in risk-based pricing.

Dynamic Risk-Based Pricing in Fintech

Understanding buzzwords, avoiding the hype, and capitalizing on AI.

Is Artificial Intelligence Worth It for Me?

Cloud

Data Science

Data

Data Analytics

Storage

Compute

Visualization

Visual Data

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Enterprise Applications

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Business Intelligence and Analytics

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Code-First Data Science Using DataRobot’s AI Cloud Platform

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