Name: Building an OSS Governance Program for ML in the Enterprise
Start: 2023-11-27T16:00:00Z
End: 2023-11-27T16:00:31.000Z
Location: BrightTALK
Rating: 4

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.

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

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

Using Python to build data applications for an enterprise organization brings many benefits, including leveraging the innovation of the community to harness the latest and best technologies for your use case.

However, developing with Python for the enterprise is complex, and one of the most common challenges we hear is that deployment takes too long.

Join Anaconda Solutions Engineer Cody Foxwell to learn:

The five benefits of open source for the enterprise, and the challenges of deployment

Steps you can automate to secure and centralize workflows for Python development

Three steps you can take to improve workflows and accelerate deployments

Developing with Python and Open Source in the Enterprise

Leaders across industries are applying Python and other open-source software to establish an edge in competitive markets. Python users want to move fast and iterate on their work often, which puts them at odds with security teams, who need to control access and ensure data protection. How can you leverage the innovation of open-source software and ensure the security of your software supply chain?

Join us for a discussion with open-source expert Hassam Mian, who will share how a centralized approach to your open-source software stack is paramount to unlocking the power of open-source innovation. Learn about how:

Open-source packages and repositories give Python users superpowers—but when they’re downloaded to laptops and connected to business data, they can cause headaches for both users and IT administrators.

You can eliminate common issues such as: difficulty collaborating with team members, no path to deployment.

Establishing one-stop access can be established to provide the best open-source tools for your business requirements, use cases, deployment approaches, and security requirements.

Bridging the gap between Python and IT: Faster Builds, Deployment, and Security

Enterprise use of open-source software (OSS) has seen continued growth in recent years, powering rapid innovation and solution development. Unfortunately, as OSS use has increased, so too have software supply chain attacks. In the battle against software supply chain risk, common vulnerabilities and exposures (CVEs) serve as critical tools. In this session, we'll dive into CVEs and how to handle them. Questions we'll cover include:

Where does CVE data come from?
What goes into a CVE score?
What are the benefits and limitations of public CVE data?
How can enterprises use CVEs to secure their OSS pipelines?

Know Your Enemy: Vulnerability Data And What To Do With It

The use of open-source software (OSS) in the enterprise has seen continued growth in recent years, enabling rapid innovation and solution development. Unfortunately, as OSS use has increased, so too have software supply chain attacks. From Kaseya to Log4j, attacks have made headlines and made software supply chain security top-of-mind for developers, administrators, and business leaders alike. OSS security presents unique challenges that are not typically well-served by traditional IT management and security tools. In this session, we will discuss lessons learned from helping secure the open-source pipelines of organizations across several industries, including best practices and common pitfalls to avoid. Additionally, we will discuss the implications of recent regulatory changes related to software supply chain security, and what you can do to prepare for the future.

Rising Threat: Securing Your Open-Source Software Pipeline

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 and Data Science Supply Chain

Using Python to build data applications for an enterprise organization brings many benefits, including leveraging the innovation of the community to harness the latest and best technologies for your use case.

However, developing with Python for the enterprise is complex, and one of the most common challenges we hear is that deployment takes too long.

Join Anaconda Solutions Engineer Cody Foxwell to learn:
- The five benefits of open source for the enterprise, and the challenges of deployment
- Steps you can automate to secure and centralize workflows for Python development
- Three steps you can take to improve workflows and accelerate deployments

Analyzing data using conventional statistical methods involves looking at tabular data where data points are independent of each other, e.g. a person’s age is independent of any other person’s age. These approaches limit the insight that can be gained as there’s often knowledge hidden in how one data point relates to another. For example, two people can be deemed similar if they have entirely different purchase histories but each have purchase histories similar to a third user.

This talk will go over how graph analytics can gain these sorts of insights not easily achievable through conventional data analysis performed on tabular data.

Graph Analytics for Data Scientist

When exploring and visualizing large datasets, you are usually faced with a tough choice between sparsely sampling your data and aggregation. In this talk, we'll show how to use Anaconda technologies to show all of your data, revealing both trends and outliers, and letting you explore even the largest datasets interactively in a web browser.

We'll discuss:
- How Datashader can render your entire dataset faithfully in milliseconds or seconds, with both trends and outliers easily visible. 
- How pairing Datashader with the Python-based interactive tools Bokeh, Panel, and HoloViews lets you work interactively with your data, selecting individual datapoints and local groups for further inspection and exploration
- How to make sense of incoming datasets and make them actionable without getting distracted by tuning plotting parameters or missing out on important insights.

Seeing the Forest and the Trees: Data Discovery in Billion Row datasets

Successful machine learning initiatives depend on so much more than leveraging the right tools and building the right models. To get the most value out of your ML investment, you need to align the right people and processes to drive toward your organization’s strategic goals. But where do you start, and how do you measure success?

Join us for a webinar on Tuesday, December 10, to learn how to jump start your machine learning initiatives and realize the most value from your investment. We’ll discuss:

- The challenges of driving machine learning in the enterprise
- The roadmap to success - who to get involved, when and how to involve them
- The role of a data science champion in ML implementation
- What machine learning maturity looks like
- How to realize value from your ML investment

Fueling Your Machine Learning Investment for Success

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

Open Source

Supply Chain

Machine Learning

Big Data

Data Analysis

open source

supply chain security

Data Governance

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Building an OSS Governance Program for ML in the Enterprise

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