Name: Data Science in the Enterprise: Leveraging MLOps in the Enterprise
Start: 2023-10-10T18:00:00Z
End: 2023-10-10T18:00:32.000Z
Location: BrightTALK
Rating: 2

With more than 35 million users, Anaconda is the world’s most popular data science platform and the foundation of modern machine learning. We pioneered the use of Python for data science, champion its vibrant community, and continue to steward open-source projects that make tomorrow’s innovations possible. Our enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness the power of open-source for competitive advantage, groundbreaking research, and a better world.

Financial institutions face an unprecedented challenge: deploying AI at startup speed while
maintaining bank-grade security and regulatory compliance. The native integration between
Anaconda and Databricks is designed to address the trade-off between innovation velocity and
enterprise security. Join Databricks, Anaconda, and ESG as we review the current state of AI
deployment in financial services and share examples of the challenges and how this unified
platform scales from secure development environments to enterprise production, enabling
financial services to innovate quickly and confidently without compromising regulatory
standards.

From Pilot to Production: AI Governance Strategies for Financial Services

While enterprise AI offers tremendous potential for organizations, implementation challenges include rapid technological evolution and governance concerns that undermine innovation confidence. This session focuses specifically on AI governance and security, featuring Anaconda's practical advice and best practices for establishing trust controls over AI models and procedures to help organizations succeed with their enterprise AI initiatives.

Practical Open Source AI: Moving Fast Without Breaking Things

Enterprise AI projects are failing at alarming rates. Despite massive investments, most organizations struggle to move from proof-of-concept to production-scale success.

The problem isn’t the technology—it’s the approach.

Join Forrester Principal Analyst Mike Gualtieri, Forrester Senior Consultant, Luca Son, and Anaconda Field CTO Steve Croce as they reveal why some enterprises are thriving with AI while others remain stuck. You’ll discover the real barriers blocking AI success and the practical strategies that are delivering measurable results.

Key Takeaways:
The 3 critical mistakes that doom AI projects before they start
How to balance innovation speed with enterprise security requirements
The unified platform approach that eliminates AI toolchain fragmentation
Why curated open source beats DIY for production-ready AI at scale
 

Whether you’re a data leader frustrated by slow progress, an IT executive balancing innovation with security, or a business leader demanding AI results, this session provides the clarity you need to move forward with confidence.

Making Enterprise AI Work

Register to access this free on-demand webinar for an exclusive look at the latest enhancements to the Anaconda AI Platform—the only unified platform designed to streamline AI development with open source. In this session, Saundra Monroe will demonstrate how our unified platform empowers technical teams to accelerate innovation, reduce operational costs, and gain competitive advantage—all while maintaining enterprise-grade security and compliance.

Discover how the Anaconda AI Platform now delivers:

3X more compatible artifacts to launch AI initiatives faster
Simplified enterprise adoption with guided onboarding and smarter navigation
Curated environments tailored to specific use cases and industries
Comprehensive security and governance across your entire AI development lifecycle

Accelerate Innovation with the Anaconda AI Platform

The growth of open-source AI in the enterprise has been nothing short of phenomenal. Enterprise Strategy Group (ESG) research found that 99% of organizations are actively using, exploring or experimenting with AI in their environments.

As organizations accelerate their AI initiatives, the adoption of open-source tools has become essential. But there isn’t a commonly known playbook for how to leverage open-source AI, and rapid changes in AI models, tools, and frameworks means there is currently a steep learning curve. 

In this exclusive webinar, open-source visionary and co-founder of Anaconda, Peter Wang, shares insights about the growth and challenges of the open-source AI phenomena with Mark Beccue, ESG Principal Analyst. Tune in as the pair unpack:
• How open-source tools provide unparalleled flexibility, cost-effectiveness and access to cutting-edge tech
• Best practices for operationalizing AI with open-source tools
• How to overcome the AI readiness gap 
• Where & how Anaconda assists customers in their AI journeys

Understanding the State of Enterprise Open-Source AI & Adoption Challenges

Are you ready to revolutionize your data analysis workflow? Join us for an insightful webinar where we’ll explore how to streamline your analytical tasks using Snowflake Notebooks and Anaconda packages. Whether you’re a data scientist, analyst, or engineer, this session will equip you with the tools and techniques to automate repetitive processes, increase productivity, and gain deeper insights faster.

Automate Your Analysis Tasks with Snowflake Notebooks & Anaconda Packages

Join this webinar for an introduction to Python in Excel including taking your first steps, understanding data types and output modes. A brief introduction to Pandas DataFrames, custom functions and a simplified Python charting experience using the Anaconda Toolbox for Excel.

Introduction to Python in Excel

Experience our exclusive 2025 AI Predictions Webinar where top experts from Anaconda and the industry explore the trends, innovations, and opportunities shaping the future of AI.

What You'll Learn:
AI Agents: How autonomous agents will drive automation and business operations
AI Trends for 2025: What cutting-edge advancements to prepare for
Real-World Applications: How organizations are turning AI insights into action
Practical Advice: How businesses can align their AI strategy for the coming year

Moderator: Kodie Dower, Sr. Marketing Communications Manager, Anaconda

Panelists:
Peter Wang, CAIO, Anaconda
Greg Jennings, Head of Engineering - AI, Anaconda
Priyanka Kulkarni, founder and CEO of Casium

2025 AI Predictions: Uncover the Future of AI with Industry Experts

Join us for an insightful fireside chat as we peer into the future of artificial intelligence (AI) in 2024. In this dynamic session, industry experts will share their predictions and unveil the vast opportunities ahead in the rapidly evolving landscape of AI. Our speakers will delve into key areas shaping the AI ecosystem, exploring recent advancements in machine learning, large language models (LLMs), and beyond. Studying transformative breakthroughs to emerging trends, we will analyze the forces propelling AI innovation and their potential impact on industries ranging from healthcare and finance to manufacturing and entertainment.

In this webinar, you will learn:

What could be next, after this year’s explosive growth of large language models (LLMs)
Strategic considerations for organizations and data professionals navigating the AI landscape in 2024
How open-source AI and closed-source AI will compete in the coming year and the potential outcomes of the race to build marketable AI systems

AI in 2024: Unveiling Predictions and Seizing Opportunities

MLOps offers ways to optimize processes, automate workflows, detect anomalies, reduce costs, and improve business outcomes. However, implementing and applying these techniques can be a large and complex effort. In this webinar, we’ll explore how to successfully implement MLOps across the enterprise using data science platforms to achieve different business goals, like deployment and automation, reproducibility and scalability, diagnostics, governance and compliance, collaboration, and monitoring.

Speaker: 
Chris Styduhar is the Director of Enterprise Products at Anaconda. With a background in software engineering, machine learning, and enterprise technology solutions, Chris manages the development of Anaconda’s Data Science Platform, including MLOps and AI features that help data scientists build, collaborate, and deploy more effectively.

Data Science in the Enterprise: Leveraging MLOps in the Enterprise

Open-source software (OSS) is at the heart of many of the innovations in machine learning (ML) due to its accessibility, flexibility, and active community support. As a result, many enterprise organizations are adopting or seeking to adopt OSS into their operations, product development, and marketable solutions. However, as with any software, OSS is not immune to vulnerabilities and requires an actively managed governance program.

In this webinar, we describe some of the major advantages of open-source software, along with common risks. We also describe key features of an effective OSS governance program with a focus on challenges for enterprise in the areas of software supply-chain security, license management, and support.

Building an OSS Governance Program for ML in the Enterprise

As data continues to grow at an exponential rate, businesses must find ways to leverage it for competitive advantage.

In today's fast-paced environment, organizations need a solution that provides the resources, horsepower and scalability needed to create and execute complex, powerful AI / ML solutions.

An enterprise data science platform is the solution to enable organizations to efficiently manage their data and transform it into actionable insights. 

Join Anaconda's Director of Enterprise Product, Chris Styduhar, on Thursday, June 8 to learn:

The most compelling use cases for your data

Why your organization could benefit from a full-featured Data Science Platform

The unique capabilities provided by Anaconda's Data Science Platform and Enterprise products

Why You Need a Data Science Platform

Recent events have highlighted the importance of software supply chain trustworthiness and security.

In this session, we discuss the content trust features in conda and the Anaconda Professional Repository. We’ll describe the design of conda's package signature verification functionality and its trust architecture, and identify the threats they protect against. Finally, we’ll highlight what’s next for Anaconda content trust as we continue to work to better secure the conda package ecosystem.

How Conda Signature Verification Secures Your Software Pipeline from the Start

AI techniques like machine learning (ML) and deep learning (DL) offer new ways to optimize processes, automate workflows, detect anomalies, and reduce costs in manufacturing industries, with a potential value estimated at $2 trillion. However, these techniques can be tricky to implement and apply, with many potential pitfalls for the unwary.

In this webinar, we’ll explore some use cases for AI in manufacturing and highlight what data science and machine learning practitioners need to consider when applying AI to this field.

Data-Driven Manufacturing: AI, ML, and Data Science

The innovation, community, and agility of open source software are unmatched, but Python ecosystems are not without risk. Security vulnerabilities are a reality in open-source code just as they are with proprietary software. It's a high-stakes game that depends on the robustness of your supply chain security. Scanning for security vulnerabilities is not enough; you must trust where your open-source software packages, libraries, and databases originate. We’re here to help.


In this webinar, you will learn:

Where, why, and how security issues might creep into your Python and R open-source ecosystem
How a single compromised open-source component can lead to a cascade of security breaches
Key steps for securing your open-source supply chain and a roadmap of risks you can reference with your teams to build security awareness

How to Secure Your Python Open-Source Supply Chain from Hidden Threats

Open-source software (OSS) offers free access to powerful tools created by the innovation of the community. But how you use those tools in an enterprise context can have significant effects on your organization’s data, collaboration, and security. Choosing the right OSS respositories for your use cases and requirements can make a difference in how your team builds, deploys, and secures your intellectual property and data. Anaconda provides secure open-source repositories to 90% of Fortune 500 companies. Join our Director of Engineering Kyle Leaders for a webinar about choosing OSS repositories with enterprise-level build, deployment, and security capabilities.


In this webinar, you will learn:
3 types of repositories commonly used in enterprise
5 considerations for choosing OSS repositories for enterprise
How to right-size and right-fit your OSS enterprise platform and tools

Considerations for Choosing OSS Repositories for Enterprise Use

As the adoption of artificial intelligence, machine learning, and data science (AI/ML/DS) continues to grow, the importance of open source security has never been more critical.

Today, upwards of 95% of enterprises rely on open source for rapid innovation making them attractive targets for threat actors who may attempt to compromise popular packages or distribute malicious ones.

Join Anaconda’s Principal Solutions Architect, Frank Yang, to learn how to secure your open source supply chain. This session will:

Delve into the challenges and risks associated with open-source

Demystify software supply chain security

Share best practices for mitigating open source risks

You will gain key insights on how to assess and secure your software supply chain and leverage open source innovations safely in your enterprise.

Securing Your AI/ML & Data Science Supply Chain

While ChatGPT took the world by storm, we also saw the darker side of new GPT and GenerativeAI solutions. There are so many emerging use cases around data-driven AI solutions. And many of these evolve a legacy approach to AI. While many of these new initiatives aim to make life easier for users and the organization, there is a darker side to data. 

With over 90% of threats coming in directly through an inbox, how do data centers ensure they're protected against some of the most advanced threats in our industry? All-new findings and security research points to an entirely new era for security and the enterprise data center. Get ready to hear some of the newest findings. 

This session will explore the meteoric rise of LLMs and GPT solutions. We'll also dive into the dark web to see how malicious actors are using these technologies to target the data center. Most of all, we'll explore how leaders protect their clients, data, and digital infrastructure. 

This session will cover:
- Enterprise data centers and the evolution of AI.
- Updating the data center: Preparing for weaponized AI
- A dive into the dark web: Learn what your Data is worth 
- Understanding AI threats impacting enterprise data centers.
- Tools and strategies to fight back.

Weaponized AI and GPT: Updating data center security practices

Data centers are not dead, and enterprises need new and refurbished data centers to be up to demands of the work to be deployed in them. Focusing on power in all its facets—energy, cooling, computing, storage, networking—to support work at very high densities is crucial. Building from the ground up to maximize automation at every level above the physical is equally important: the need to manage more capacity and high-importance infrastructure with fewer staff to hand will only become more urgent and more widespread. 

Join Nemertes as we tie together the threads and point you at the right considerations, questions, goals, technologies and techniques to take into account in your planning.

Power, Density, Automation: Keys to a Next Generation Data Center Approach

In recent years, data centers have shifted from being perceived as a clean industry to being a focal point in achieving the EU's 2030 climate protection targets. Despite the inherent drive for efficiency and sustainability in data center operations, realization of these goals has been inadequate.

The increasing energy demands of data centers and the stalling progress in improving their efficiency conflict with the EU's sustainability goals. In turn, data centers have been brought under the scope of the European Corporate Sustainability Reporting Directive.

In response to this, the Data Center Maturity Model (TS 50600-5-1) has been developed to assess a data center's sustainability within the framework of Environmental, Social, and Governance (ESG) considerations. This model allows for an EU taxonomy-compliant evaluation of energy management and environmental impact, enabling the establishment of a pathway for continuous improvement and the achievement of higher maturity levels.

Join Fridtjof Chwoyka (Senior Data Center Consultant, Data Center Excellence GmbH) for this session to get to grips with the TS 50600-5-1 Model. 
Topics for discussion include:
- Understand the need for a maturity model in the assessment process and discover how key metrics like Power Usage Effectiveness shed light on data center efficiency.
- Explore how the maturity model facilitates the assessment process and understand how its structures and methodologies can be used to evaluate sustainability within data centers.
- Discover how to leverage the model to align with shifting regulatory requirements and industry standards. 
- And more..

Enhance Data Center Sustainability With Data Center Maturity Model TS 50600-5-1

Data centres played an unquestionably key role in addressing several key challenges during the pandemic – from enabling remote work, to facilitating online education and accelerating medical innovation to speed up vaccine roll outs.  

With demand for digital services higher than ever, digitisation projects are driving the development of new trends and services, such as smart cities, Internet of Things, AI and 5G amongst others. This skyrocketing demand is matched by a growing demand for equipment and energy to power the rapidly expanding data centre industry. This increasing demand for IT equipment presents serious global, environmental challenges.  

In this session join Dr. Umaima Haider, Lecturer in Computer Science and Digital Technologies at the University of East London, to explore the opportunities and challenges that exist with respect to the environmental impact of digital infrastructure. 

Topics for discussion include: 
- Quantifying the environmental impact of data centres.  
- Reducing data centre cost and consumption. 
- Environmentally sustainable approaches to data centre growth.
- And more...

Embracing the Digital Era: Unlocking Data Centre Environmental Sustainability

There has been much talk about cloud repatriation as many organisations reverse their precipitate rush to the public cloud. But simply bringing it home isn’t the answer either. Hybrid cloud has been around for many years and the emerging trend for multi-cloud strategies is driving organisations to take a long hard look at their own data centres.

Building a truly cloud experience in an on-premises data centre is a challenge. Managing the resulting hybrid infrastructure with potential multi-clouds is a serious challenge. Join this webinar to look at options for building, managing, orchestrating and automating true hybrid clouds so they become an effective addition to an organisational strategy rather than the anchor that slows growth.

Find out:
- Why the modern enterprise data centre is still a good investment.
- How to manage across data centres and environments.
- How to move services between data centres quickly and easily.

About the speaker
An established thought leader and innovator, Simon serves as a Virtual CIO, CIO Advisor and Leadership Mentor for a wide variety of organisations.  Simon works with organisations to build high performing IT teams, develop structured training and development plans and to foster a culture of innovation and change.  Simon holds a Masters in Psychology and is a trained accountant and former finance director as well as being an RFU Licensed Rugby Coach.

The Enterprise Data Center: Reports of My Death Are Greatly Exaggerated

Updating the Enterprise Data Center

MLOps

Data Science

AI/ML

Enterprise Applications

Python

Machine Learning

Scalability

Governance and Compliance

workflow automation

data science

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Data Science in the Enterprise: Leveraging MLOps in the Enterprise

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