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It has become a more or less conventional opinion that most machine learning projects do not make it to production, and of those that do, many do not live up to their promise.
We should always take sweeping claims like these with a grain of salt, since accurate statistics are difficult to collect (and interpret), and some of the organizations that circulate them have an interest in convincing professionals that his The solution is the key to all the ai integration challenges you face. Still, it's hard to ignore so many voices (from different corners of our community) who recognize that reaping the benefits of this emerging technology is more difficult than it first seems.
Our weekly highlights focus on the practical aspects of choosing, adopting, and making the most of ai-powered products and workflows. There will never be a single solution to the problem of integrating promising but complex tools into an enterprise, but we believe that exploring these articles can frame the conversation in more useful and pragmatic terms. Let's get to it.
- Gain your competitive advantage with ai
What benefits can companies have? in fact Harvest using ai? Dr. Janna Lipenkova expands the mental model you can adopt to make smarter design and product decisions that will allow you to find the “sweet spot” for ai in your organization, moving beyond automation to open the space for greater creativity and innovation. - Integrating multimodal data into a large language model
Umair Ali Khan presents a detailed and practical introduction to a cutting-edge approach that builds on recent work on contextual retrieval and makes it possible to include not only textual data in your RAG channels, but also visual media. From receipts to graphs and charts, machine learning workflows can now become more robust with the use of richer multimodal data.
- How to Choose the Best ML Deployment Strategy: Cloud vs. Edge
“As machine learning adoption grows, there is increasing demand for scalable and efficient deployment methods, but the details are often unclear.” Vincent Vandenbussche patiently guides us through the various factors that ML engineers must consider when deciding which is the best option for their specific projects and use cases. - A tour of Nvidia's latest multi-modal LLM family
Staying up to date with new developments in ai is certainly important, but the rapid pace at which new models and tools arrive on the scene often makes it difficult for busy data professionals to keep up. Mengliu ZhaoThe recent summary of is a useful overview of the new set of multimodal LLMs released by NVIDIA and compares their performance with other models (both commercial and open source).