Name: How to Successfully Deploy Data Science Projects in Production?
Start: 2020-09-24T06:00:00Z
End: 2020-09-24T06:00:47.000Z
Location: BrightTALK
Rating: 4.5

Dataiku is the world’s leading platform for Everyday AI, systemizing the use of data for exceptional business results. Organizations that use Dataiku elevate their people (whether technical and working in code or on the business side and low- or no-code) to extraordinary, arming them with the ability to make better day-to-day decisions with data.

More than 450 companies worldwide use Dataiku to systemize their use of data and AI, driving diverse use cases from fraud detection to customer churn prevention, predictive maintenance to supply chain optimization, and everything in between. 


In this exclusive, invite-only webinar with Dataiku featuring Forrester, see why we're past the stage of experimentation and POCs with AI — today, ROI is a must. But how can you guarantee business value from AI initiatives? 

Agenda:
- Mike Gualtieri, VP & Principal Analyst at Forrester, will discuss the state of the market and trends in how today's businesses are driving value.
- Asha Dinesh, Consultant at Forrester, will dive into the results of The Total Economic Impact™ Of Dataiku study.
- Claire Gubian will uncover why Everyday AI is the path forward to ROI from AI and unpack some examples of businesses that have been successful with their AI initiatives. 
- 15-minute Q&A with the experts from Dataiku & Forrester.

The Future of AI and ROI for the Enterprise, featuring Forrester

ChatGPTをはじめとする生成AIの台頭から1年が経ち、2024年においても、ビジネス現場での生成AIの活用がさらに広がると予測されています。ビジネスの現場では、急速に進化する生成AIをどのように業務の効率化とビジネス価値の向上に活用すべきでしょうか。本セッションでは、生成AIの中でも企業が最も関心を寄せているLLMに注目し、LLMが業務プロセスの効率化にもたらすメリット、そしてをLLMアプリケーションを安全に構築するための方法、海外の先進事例、そして生成AIを用いた口コミデータの分析やクイックに使えるプロンプトエンジニアリング機能を実践デモを交えてご紹介します。

海外の先進事例で学ぶ生成AI活用のすすめ

Organizations recognize the potential of AI to revolutionize their operations and accelerate their digital transformation. Whether it's enhancing customer experience, reducing churn, driving innovation, or optimizing productivity, AI presents numerous opportunities for businesses.

Despite investing resources in developing their AI capabilities, companies face significant challenges in scaling and deploying their models at a wider scale to generate returns on investment. This process is complex and requires careful management to ensure success.

To address these challenges and accelerate your AI lifecycle, join our upcoming webcast where we will discuss:

- Key ingredients for addressing these limitations and successfully launching and scaling AI initiatives across your organization.
- Strategies for creating resilient AI-driven assets that can maximize their impact and value.

Scaling your AI without Crashing

Join Convex Insurance for an enlightening webinar sharing their firsthand experience with Dataiku and Snowflake. 

John McCambridge (Global Director at Dataiku), Steve Perry (Head of Data Delivery at Convex Insurance), and Sully McConnell (Head of Insurance at Snowflake) will uncover the art of managing data democratization at scale in the financial services domain. Discover actionable insights on building collaborative cultures, ensuring trust, and maximizing value generation in the data-driven business landscape. Gain insights into fostering collaboration, building trust, and unlocking maximum value in the financial data ecosystem.

Tune in on 18th of January 2024!

Webinar: How to Manage Data Democratization in the Insurance Sector

What exactly do we mean by “AI project failure?” It’s indeed a fine line, because data practitioners should never feel discouraged from exploring the unknown. In that vein, failure is a critical element of advanced development and good data science. In the industry, viewing “failure” as a fundamental flaw in doing data science could create the wrong expectations for practitioners. 

In this fireside chat featuring Will Benton, Principal Product Architect at NVIDIA, uncover how we can encourage this type of experimentation while also empowering practitioners (on both technical and business teams) with the tools needed to avoid AI project failure at large (i.e., sound and reliable data, AI Governance and MLOps, collaboration with IT).

Why Do AI Projects Fail? Dataiku x NVIDIA Fireside Chat

AIモデルを本番稼働するにあたり、不可欠となるのがMLOpsです。先行企業の多くはさまざまなツールを駆使してMLOpsの仕組みを作り上げています。
しかし本番稼働するモデルが増えていく中で、モデルのパフォーマンスを監視し、さらに精度維持のために再学習とその評価を継続して行うことが、そのような仕組みでサポートできるでしょうか。
前回のMLOps構築編に続く本Webinarでは、継続運用のためのモデルの比較と評価についてお話しします。シングルプラットフォームであるDataikuならではの利便性をぜひご覧ください。

MLOps前編：https://www.brighttalk.com/webcast/17108/596043?
■ 講師プロフィール
木邑 文彦
Sales Engineerとして、AI・機械学習の民主化を目指し、あらゆるお客様に課題解決に向けたソリューション提供・技術支援を実施。
Dataiku参画前はOracle Japanにて主に重要アカウントに対するセールスコンサルタントとして従事し、Accenture参画後はビジネス戦略策定から実行支援・大規模SIなどの多岐に渡るプロジェクトをマネージャーとして推進。



Dataikuについてお問い合わせはこちらよりお願いいたします。
https://www.dataiku.com/ja/お問い合わせ/

AIモデルの継続的な運用と拡大のためのMLOps 【モデル比較/評価編】

Join Patrick Hall, one of the authors of the O’Reilly book “Machine Learning for High-Risk Applications: Approaches to Responsible AI” and principal scientist at BNH.AI, and Triveni Gandhi, Responsible AI Lead at Dataiku, for a riveting discussion on AI risk management and Responsible AI for data practitioners. The conversation will touch on concepts from the O’Reilly book, notably a snapshot of the legal and regulatory landscape, the NIST AI risk management framework, and model governance. Plus, get some practical takeaways on how to implement the NIST framework using Dataiku. Be sure to get a complimentary of the full O’Reilly ebook in the "Attachments" section below!

Modern ML Risk Management Strategies

The future looks bright and exciting for data scientists of tomorrow. As the interface between technology and industry, data scientists will play a pivotal role in helping businesses grow. Activities like collaborating with the business to identify the right problem, creatively leveraging solutions from cross domains, and creating powerful, innovative ML systems will keep data scientists busy for many years. But are the data scientists of today ready for the data-driven businesses of tomorrow? In this session, we discuss the challenges associated with the evolving role of the data scientist and suggest strategic ideas to help upgrade the role on domain / industry-specific skills and also how to evolve in specialized roles.

The Evolution of the Data Scientist in the Enterprise

Join Aviva's Chief Audit Officer, Chris Butler and Senior Manager of Audit Data Analytics, Francisco Pavão Martins as they discuss their process mining use case.

Process Mining At Aviva Internal Audit | Everyday AI London Roadshow 2023

In this talk Anders Dræge AI/ML Team Lead, will explore how Frende Forsikring, through combining AI and robotic processes with Dataiku, have successfully automated their email claims reporting process. Using a trained BERT model and Dataiku’s platform they have increased workload efficiency, saved costs and have opened up new areas for automation and AI acceleration

Hyper-Automation With Dataiku And RPA | Everyday AI London Roadshow 2023

In this session from the Everyday AI London Roadshow, explore the present and future of Artificial Intelligence for predictive and population health in the NHS.

Scaling Artificial Intelligence For Population Health In The NHS

Discover how Snowflake and Dataiku supercharge Everyday AI together in this record session from the Everyday AI London Roadshow.

How Snowflake And Dataiku Supercharge Everyday AI Together

Join Rek Chong and Howard Chu as they describe Element's path to modernisation with Dataiku following our Everyday AI London Roadshow.

Element: On Path to Modernisation

Over the past 15 years, ESG evaluation, has shifted from a niche activity to a significant trend, influencing banks, asset managers, insurers, and other key players. However, successfully integrating ESG into financial processes remains a complex task, requiring the right analytics, models, and strategies.

This webcast will shed light on the challenges organisations face in embedding ESG into their operations and how some organisations have successfully navigated these hurdles to drive high-value transformation across the financial services value chain.

Join our experts, Anne-Sophie Roessler, Account Executive for FSI and Vincent De Stoecklin, RVP Customer Success APJ, as they discuss;

- Complexities you may face incorporating ESG criteria into the financial decision-making processes. 
- Best practices and strategies to implement and continuously evolve. 
- ESG integration goes beyond meeting compliance requirements; how you can transform through AI, Machine learning and Analytics.

Accelerating ESG Embedding for Sustainable Transition in Financial Services

Effectively managing and scaling multiple Large Language Model (LLM) applications demands more than a mere notebook approach. Join us as we navigate through the often-overlooked facets of operationalizing LLMs. This session will guide you through the complete journey, spanning from the initial stages to deployment, while emphasizing the crucial aspects of ongoing maintenance and monitoring.

Operationalizing Large Language Models

When it comes to industrializing ML models, challenges include lengthy project cycles and a lack of streamlined processes, which can leave teams struggling to keep up with business demands. In this session, attendees will gain insights into best practices and strategies for efficiently productionizing and monitoring ML models, including the latest features from Dataiku.

Improving With MLOps: Three Steps to Operationalize at Scale

Optimizing and automating data pipelines to transform, prepare, and analyze data on an ongoing basis is critical for production-ready AI projects. In this session, learn how Dataiku supports the maintenance and operations of data pipelines through metrics, checks, and data drift notifications for clean, reliable, and timely data across the enterprise.

A Well-Oiled Machine: Create Sustainable Data Pipelines With Dataiku

In this session from Everyday AI New York Tech Day 2023, hear from Dan Darnell, our VP of Product Marketing, and Elise Salamanca, our Senior Director of Product Management, on how to build Generative AI Apps with New Features in Dataiku.

New Generative AI Features In Dataiku

Building a data science (DS) project and training a model is only the first step. Getting that model to run in the production environment is where companies often fail: according to McKinsey, less than 10% of DS projects are deployed into production with an average deployment time of 9 to 12 months.

Vincent De Stoecklin, Customer Success Director at Dataiku, will talk about best practices and key learnings to make this crucial step of the data science process easier.

During the session, we will cover:
1) How organisations deploy AI, ML, DS projects in production
2) What are some key challenges and learnings
3) Real-life examples of companies who have successfully deployed data science products.

How to Successfully Deploy Data Science Projects in Production?

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How to Successfully Deploy Data Science Projects in Production?

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