Sustainability and ESG

Minimizing environmental impact with a culture of sustainable innovation

Why Sustainability and ESG?

Sustainability leadership is crucial for managing environmental risks and promoting sustainable practices. By switching to the cloud and utilizing sustainable energy sources, ESG leaders can significantly reduce greenhouse gas emissions, water usage, and waste. Learn how to embrace business sustainability trends and strategic leadership toward sustainability to advance environmental goals and drive impactful change within your organization.

Why Sustainability and ESG?

Practical Sustainability for Business

Sustainability is a business imperative. Organizations can’t succeed when ecologies are failing or societies are degrading; in order for business to thrive, so too must the planet. Business success is inextricably tied to sustainable practices, but becoming a sustainable business doesn’t happen overnight. In this ebook, learn practical, realistic ways you can implement sustainability with impact. Get the tools for strategic leadership towards sustainability.

Download the eBook

How companies can take immediate action to reduce their carbon footprint

Companies across all industries have set ambitious goals to reduce the environmental impact of their operations. Achieving these goals requires collaboration across an organization, from senior executives to leaders who execute sustainability initiatives relevant to their area of the business. To accelerate progress, leaders need to align business and technical teams and build scalable solutions that can accurately measure sustainability programs and continuously improve performance.

How Machine Learning Helps Small Holder Farmers Connect, Share and Thrive

Wefarm powers the world’s largest farmer to farmer digital network. See how they are using machine learning on AWS to enable knowledge sharing among small holder farmers. With over a billion small holder farms contributing more than 70% of the world’s food – Wefarm plays an important role in using technology to develop the food supply and build resilience for small holder farmers.

The Climate Pledge

All companies have a role to play in investing in solutions to protect the planet and the economy. And all companies – including Amazon – have work to do to further reduce their operations’ carbon footprint and carbon intensity. In addition to being the right thing to do, taking ambitious climate action can spur innovation that helps a business and its customers. 

The Climate Pledge is a commitment to reach net-zero carbon emissions by 2040—10 years ahead of the Paris Agreement. Amazon co-founded The Climate Pledge in 2019 to build a cross-sector community of companies, organizations, individuals, and partners working together to address the climate crisis and solve the challenges of decarbonizing our economy.

Read more on The Climate Pledge

lake

Refine your search:

Format
22-24 (37)
Showing results: 22-24
Total results: 37
  • Publication Date
  • Alphabetical (A-Z)
  • Alphabetical (Z-A)
 We could not find any results that match your search. Please try a different search.
  • Article

    Machine learning, concluded: Did the “no-code” tools beat manual analysis?

    Ars Technica set out to discover whether no-code-required tools could outperform a code-based approach. In the finale of the experiment, find out how the no-code tools performed.

    To see how much machine learning tools for the rest of us had advanced—and to redeem myself for the unwinnable task I had been assigned with machine learning last year—I took a well-worn heart attack data set from an archive at the University of California-Irvine and tried to outperform data science students' results using the "easy button" of Amazon Web Services' low-code and no-code tools.

  • Article

    Setting our heart-attack-predicting AI loose with “no-code” tools

    Ars Technica set out to discover whether no-code-required tools could outperform a code-based approach. In the second part of this three-part series, the heart attack predictions take flight.

    This is the second episode in our exploration of "no-code" machine learning. In our first article, we laid out our problem set and discussed the data we would use to test whether a highly automated ML tool designed for business analysts could return cost-effective results near the quality of more code-intensive methods involving a bit more human-driven data science.

  • Article

    No code, no problem—we try to beat an AI at its own game with new tools

    Ars Technica set out to discover whether no-code-required tools could outperform a code-based approach. In part one of three, they give the cloud a new problem to (heart) attack.

    Over the past year, machine learning and artificial intelligence technology have made significant strides. Specialized algorithms, including OpenAI's DALL-E, have demonstrated the ability to generate images based on text prompts with increasing canniness. Natural language processing (NLP) systems have grown closer to approximating human writing and text. And some people even think that an AI has attained sentience. (Spoiler alert: It has not.)

1 13

Frequently Asked Questions