A Technology Broker and Management Company Specializing in KM Systems
Favorite Middle management can form an almost impenetrable layer to knowledge management in many organisations. Image from wikimedia commons The two main stakeholder groupings for KM are the senior managers and the knowledge workers. The value proposition for the senior managers is that KM will deliver greater efficiency, greater effectiveness, faster
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Shared by Nick Milton September 6, 2021
Favorite You can use various tools to define and run machine learning (ML) pipelines or DAGs (Directed Acyclic Graphs). Some popular options include AWS Step Functions, Apache Airflow, KubeFlow Pipelines (KFP), TensorFlow Extended (TFX), Argo, Luigi, and Amazon SageMaker Pipelines. All these tools help you compose pipelines in various languages
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Shared by AWS Machine Learning September 4, 2021
Favorite The Intel Olympic Technology Group (OTG), a division within Intel focused on bringing cutting-edge technology to Olympic athletes, collaborated with AWS Machine Learning Professional Services (MLPS) to build a smart coaching software as a service (SaaS) application using computer vision (CV)-based pose estimation models. Pose estimation is a class
Read More Shared by AWS Machine Learning September 3, 2021
Favorite Data scientists can spend up to 80% of their time preparing data for machine learning (ML) projects. This preparation process is largely undifferentiated and tedious work, and can involve multiple programming APIs and custom libraries. Announced at AWS re:Invent 2020, Amazon SageMaker Data Wrangler reduces the time it takes
Read More Shared by AWS Machine Learning September 3, 2021
Favorite Running machine learning (ML) models at the edge can be a powerful enhancement for Internet of Things (IoT) solutions that must perform inference without a constant connection back to the cloud. Although there are numerous ways to train ML models for countless applications, effectively optimizing and deploying these models
Read More Shared by AWS Machine Learning September 3, 2021
Favorite There’s a lot to love about online shopping: It’s fast, it’s easy and there are a ton of options to choose from. But there’s one obvious challenge — you can’t try anything on. This is something Google product manager Debbie Biswas noticed, as a tech industry veteran and startup
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Shared by Google AI Technology September 1, 2021
Favorite Machine learning (ML) is highly iterative and complex in nature, and requires data scientists to explore multiple ways in which a business problem can be solved. Data scientists have to use tools that support interactive experimentation so you can run code, review its outputs, and annotate it, which makes
Read More Shared by AWS Machine Learning September 1, 2021
Favorite The last few years have seen the rise of transformer deep learning architectures to build natural language processing (NLP) model families. The adaptations of the transformer architecture in models such as BERT, RoBERTa, T5, GPT-2, and DistilBERT outperform previous NLP models on a wide range of tasks, such as
Read More Shared by AWS Machine Learning September 1, 2021
Favorite This is a guest post from Mikael Graindorge, Sales Operations Leader at Thermo Fisher Scientific. In the life sciences industry, data is growing in abundance and is getting increasingly complex, which makes it challenging to use traditional analytics methodologies. At Thermo Fisher Scientific, our mission is to make the
Read More Shared by AWS Machine Learning September 1, 2021
Favorite I have long argued that the human brain is a poor long term store for Knowledge. Here are the three cases where it’s the best store there is. Ebbinghaus’ forgetting curve, from wikimedia commons The poor human brain gets a bit of a bad press at times. The cognitive
Read More Shared by Nick Milton September 1, 2021