Palantir Technologies Inc., a leading builder of operating systems for the modern enterprise, announced it had been recognized as a Leader in artificial intelligence and machine learning (AI/ML) software platforms by renowned research and advisory firm Forrester. Palantir was among the select companies that Forrester invited to participate in "The Forrester Wave: AI/ML Platforms, Q3 2022" report. Palantir was cited as a Leader in this research. Palantir's Foundry operating system received the highest possible scores in the product vision, performance, market approach, and applications criteria. As stated in the report, "Palantir Technologies forges a resilient platform for complex, critical AI use cases…. Reference customers appreciate the breadth of capabilities within the platform, particularly for the security and governance of ML which is critical in many environments. Palantir is a solid choice for companies who have heavy data requirements and want to mix classical ML techniques with deep learning ML techniques to build complex AI solutions."

AI/ML platforms, trade secrets can include the structure of the AI/ML model, proprietary training data, a particular method of using the AI/ML model, any output calculated by the AI/ML model that is converted into an end product for a customer, and aspects of the platform.

When I reviewed the Google Cloud AI and Machine Learning Platform last November, I noted a few gaps despite Google having one of the largest machine learning stacks in the industry, and mentioned that too many of the services offered were still in beta test. I went on to say that nobody ever gets fired for choosing Google AI. This May, Google shook up its AI/ML platform by introducing Vertex AI, which it says unifies and streamlines its AI and ML offerings. Specifically, Vertex AI is supposed to simplify the process of building and deploying machine learning models at scale and require fewer lines of code to train a model than other systems. Google's summary is that Vertex AI brings Google Cloud AutoML and Google Cloud AI and Machine Learning Platform together into a unified API, client library, and user interface.

We previously discussed which portions of an artificial intelligence/machine-learning ("AI/ML") platform can be patented. Under what circumstances, however, is it best to keep at least a portion of the platform a trade secret? And what are some best practices for protecting trade secrets? In this post, we explore important considerations and essential business practices to keep in mind when working to protect the value of trade secrets specific to AI/ML platforms, as well as the pros and cons of trade secret versus patent protection. What qualifies as a "trade secret" can be extraordinarily broad, depending on the relevant jurisdiction, as, generally speaking, a trade secret is information that is kept confidential and derives value from being kept confidential. This can potentially include anything from customer lists to algorithms.

We previously discussed which portions of an artificial intelligence/machine-learning ("AI/ML") platform can be patented. Under what circumstances, however, is it best to keep at least a portion of the platform a trade secret? And what are some best practices for protecting trade secrets? In this post, we explore important considerations and essential business practices to keep in mind when working to protect the value of trade secrets specific to AI/ML platforms, as well as the pros and cons of trade secret versus patent protection. What qualifies as a "trade secret" can be extraordinarily broad, depending on the relevant jurisdiction, as, generally speaking, a trade secret is information that is kept confidential and derives value from being kept confidential.

We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production. The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems. Remember when our biggest problem was getting our models off of data science platforms and into production. Well the market is nothing if not efficient and hundreds of platform companies have been laboring away to help solve your pain point. The problem arising for the CDO, CAO or any other CXX is trying to decide which and how many of these you need.

The following is a contributed article by Peter Kirk, Business Operations Executive at GE Power Digital Solutions. With all of the press that neural networks have been getting recently, you may be asking yourself, "What is a neural network, and should I be intrigued or scared?" A neural network is a form of artificial intelligence (AI) that is loosely modeled after the human brain, and it can help solve real-world problems in the energy sector and beyond. Whether it's a threat or salvation depends on how it's used. In the 1990s, after the last AI hype cycle, a popular way to thumb one's nose at AI was to point out that neural models could generate a 24-hour weather forecast that is more accurate than a meteorologist -- it only takes 48 hours on a supercomputer to do so.

Data scientists are constantly searching for newer techniques and methodologies that can unlock the value of big data and distill this data further to identify additional insights which could transform productivity and provide business differentiation. One such area is Artificial Intelligence/Machine Learning (AI/ML), which has seen tremendous development with bringing in new frameworks and new forms of compute (CPU, GPU and FPGA) to work on data to provide key insights. While data lakes have historically been data intensive workloads, these advancements in technologies have led to a new growing demand of compute intensive workloads to operate on the same data. While data scientists want to be able to use the latest and greatest advancements in AI/ML software and hardware technologies on their datasets, the IT team is also constantly looking at enabling these data scientists to be able to provide such a platform to a data lake. This has led to architecturally siloed implementations. When data, which is ingested, worked, and processed in a data lake, needs to be further operated by AI/ML frameworks, it often leaves the platform and has to be on-boarded to a different platform to be processed.

Recently a consulting firm reached out to me for advice about building and scaling Artificial Intelligence (AI) and Machine Learning (ML) platforms for their customers. I have some experience in this space at the infrastructure level working with Intel and NVIDIA, and at the software and services level working with Amazon, IBM and a few others so I decided to help them out. This post covers some key focus areas when it comes to scaling AI/ML platforms for their customers. I figure it is best to approach this topic through household names with products and services that most understand and use on a daily basis so I chose Facebook and Uber. For Facebook, I'll focus on the software and hardware considerations they made to successfully scale AI/ML infrastructure per an excellent talk given by Yangqing Jia, Facebook's Director of AI Infrastructure, at the Scaled Machine Learning Conference.