Why ML Models Fail in Business and What You Can Do to Prevent it from Happening
There are three critically flawed assumptions many businesses make about AI and ML models. These businesses are often completely unaware they are making these assumptions, and yet act on them on a daily basis. To the degree and extent, your company acts on these assumptions can cause irreparable damage sometimes to the collapse of the […]
Modeling Risk for Subsiding Houses
Summary: By using algorithms that continually learn from data, machine learning ensures that new trends, patterns and insights can be uncovered that may not have been identified by manual analysis methods alone. The resulting foundation repair model was then used to categorize the foundations of every building in Zaanstad, even though measurements or expert estimates […]
Which solution is financially better for heavy data science tasks? (Public Cloud -vs- HP Z8 G4 + NVIDIA GPUs)
Summary: Companies taking data science seriously, shouldn’t be discouraged by the initial investment in a dedicated workstation. As this experiment shows, you’ll break even investing in an HP Z8 G4 compared to the cloud after only 7 months. In two years, the total cost of the cloud will reach almost 120.000 dollar. For that amount, […]
Train your own NER tagger using transformer language models
Natural language processing is among the fields which are highly impacted by breakthroughs in the Deep Learning field. Hence, it is no surprise that multiple state-of-the-art techniques are pushing boundaries almost every day, and keeping up with the latest achievements, testing their capabilities has become increasingly difficult. In this post, we will focus on Named […]
Evaluating MLOps Tools
Machine learning lifecycle This section describes a generic pipeline, which is a common use case for real-world modeling initiatives. We also rely on this pipeline when testing existing tools and conducting our own experiments. It is based on Hapke H., Nelson, C. Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow (O’Reilly Media, Inc., […]
Selecting your optimal MLOps stack: advantages and challenges
MLOps Principles In 2015, Google released an influential paper Hidden Technical Debt in Machine Learning Systems. This paper described most of the problems associated with developing, deploying, producing, and monitoring machine learning-driven systems. The paper revealed that ML is no longer a discipline for data scientists. It is also relevant for any software engineering practitioner […]
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Whitepaper: LAUNCH YOUR AI MISSION WITH DATA SCIENCE – Prologue
Do you want to know more about Data Science and AI? We wrote a whitepaper for those who want to embark on a Data Science journey. It will help you understand Data Science better and, ultimately, help you make better business decisions. Whitepaper preview: UNDERSTANDING DATA What is a data-driven organization? Are all companies notdata […]
