Name: How to Avoid Building Bad Models
Start: 2018-02-21T20:25:00Z
End: 2018-02-21T20:25:58.000Z
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.

The rapid growth of Generative AI assets introduces new risks and complexity for organizations. This session explores AI observability and model evaluation techniques for Generative AI Large Language Models (LLMs) in production.

We will cover strategies to ensure performance, accuracy, and reliability, through evaluation metrics, guardrails, and user feedback. Participants will learn to implement observability across environments, drawing insights from real-world use cases.

AI Observability: How to Evaluate and Improve LLM Performance

It’s been over a year since GenAI hit the market, and Gartner has estimated that only 10% of generative AI solutions have made it to production. Such roadblocks range from difficulty testing to poor observability, to knowing which LLM is the best to use to the development and management process presenting new challenges for time-crunched data science teams. 

Join VP of Field CTOs Lisa Aguilar, and Jess Lin Lead GenAI Data Scientist, as they explore the two biggest decisions facing aspiring AI teams: how to choose the right LLM and how to compare and optimize GenAI performance. She’ll share:

-How to evaluate commercial versus open-source models
-How to balance cost, performance, and risk management factors
-How to benchmark your GenAI experiments against performance and quality metrics
-How to build effective (and sustainable) intervention and moderation strategies
 
Attendees will also see the real-world impact of AI with a behind-the-scenes look at digital consultancy for sustainable growth BSI and how they reduced model development time by 67%.

Choosing the Right LLM and Optimizing GenAI Performance

As a data leader, you must consider the evolving landscape of AI to deliver value to your organization. In this session, we'll cover pragmatic advice for improving your predictive AI workflows and how to tackle the new complexities of generative AI in the enterprise. Walk away with common best practices for "traditional ML" and how to take advantage and accelerate generative AI adoption leading to transformative changes in your organization, along with governance and oversight to de-risk all your AI workloads.

CAA North & East Ontario + DataRobot: Implementing Generative & Predictive AI

As a data leader, you must consider the evolving landscape of AI to deliver value to your organization. In this fireside chat, we'll cover pragmatic advice on what to consider and how to tackle generative AI in the enterprise. Walk away with best practices for "traditional ML" and how to take advantage of and accelerate generative AI.

SAG + ESG + DataRobot: The Considerations for Generative and Predictive AI

Is your team developing AI solutions at pace with business demand? Modern cloud architectures have enabled a scale and flexibility for production AI not possible less than a decade ago. However, data science teams are seeing increased demand for new models often exceeding their capacity. 

In this session, we'll share why Agile AI is necessary for modern data science teams and practical learnings on how to leverage the cloud to:
- Increase time to value
- Decrease time to production
- Ensure the success of your models over the long term

Plus, we'll share multiple DataRobot customer stories and examples of Agile AI across the AI lifecycle.

Leveraging the Cloud for Agile AI Development

AI is reshaping every organization. As it continues to grow, solutions built on machine learning (ML) will become the new norm. In this podcast, TechTarget and DataRobot’s Brett Olmstead, Director of Presales Data Science, will discuss the importance of speed, time to market, and experimentation during the ML modeling process. 

You’ll learn:
- How to accelerate time to deployment for your ML projects
- How DataRobot’s AutoML solution can empower you to deliver best-in-class models without sacrificing time and trust

Don’t miss this opportunity to get exclusive insights from ML experts.

How to Deploy Machine Learning Projects in Record Time

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

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.

How to Gauge Project Feasibility using Code-First Data Science

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

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

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

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