-
In this AI leaders’ roundtable discussion, participants delved into various facets of AI applied research, encompassing topics such as scaling AI within organizations, deploying deep learning models, data-centric AI, the pros and cons of open source, and model plus data version control. The insights shared during this dialogue offer valuable perspectives on these key aspects…
-
In your opinion, what is the greatest performance challenge for the current state of ML? by Guillermo Navarro, HP Right now ML models are growing in complexity faster than we can improve hardware so a big challenge is trying to create new computer architectures specifically optimized for ML so that we can keep fueling the…
-
In the dynamic landscape of business, fostering diversity is not just an idealistic goal; it’s a strategic necessity. Companies with limited diversity face formidable challenges that impede their innovation, talent acquisition, and overall workplace culture. The Dilemma of Limited Diversity 1. Limited Innovation and Adaptability: Homogeneous teams often result in a lack of diverse perspectives,…
-
Cultural movements such as MeToo and Black Lives Matter highlighted some of the inequalities and injustices our society faces. This helped diversity and inclusion in the workplace get the attention it deserves. Companies could no longer simply have diversity and inclusion policies in place to tick the right boxes; they needed to readdress how to…
-
In this roundtable, engineers from the world of machine learning and MLOps came together to discuss the evolving landscape of AI development and deployment. This insightful conversation covered a range of key topics that shed light on the complexities and possibilities in the field. Here’s a summary of the main discussion points: MLOps: Automating AI…
-
This roundtable conversation delved into the complexities of deploying machine learning models in production, a critical juncture where the rubber meets the road in AI development. Here’s a comprehensive summary of the key insights and highlights from this engaging dialogue: The Gap Between Concept and Reality The heart of the conversation revolved around the enigmatic…
-
In a recent AI Talent Roundtable, Engineers in the field of machine learning and ML operations (MLOps) gathered to share their insights and experiences. The conversation touched on various crucial aspects of AI development and deployment, shedding light on the challenges and best practices in the industry. Here’s a summary of the main points discussed:…
-
This roundtable brought together AI talent from diverse companies and backgrounds. Their objective? To explore the intricacies of MLOps and chart a course toward success in this dynamic field. Diverse Perspectives Converge The discussion commenced with introductions, allowing each participant to showcase their wealth of experience and perspectives within the MLOps landscape. Adrian, a machine…
-
During a roundtable discussion with AI leaders Alex and Farshad, we gained valuable insights into the challenges and best practices of conducting research at startups, recruiting AI talent, and designing effective interviews. In this blog post, we will highlight the key points discussed by these two experts, shedding light on the nuances and complexities of…
-
In today’s competitive job market, hiring the right candidates for data science and machine learning roles is crucial for the success of any organization. To identify the best fit for these positions, companies need to design effective interview questions that evaluate not only technical skills but also problem-solving abilities, learning aptitude, and cultural fit. The…
-
In this roundtable discussion, Yong and George shared valuable insights on the data science life cycle. This conversation shed light on various stages of the life cycle, challenges faced, and strategies adopted to generate value in their specific use cases. The discussion also touched on resource allocation, the role of platforms, and the potential for…
