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    Lightning fast CPU-based image captioning pipelines with Deep Learning and Ray

    Translation between Skeletal formulae and InChI labels is a challenging image captioning problem, especially when it involves large amounts of noisy data. In this blog post...

    Data Science

    Choosing a Data-Governance Framework for Your Organization

    What is Data Governance? Data governance refers to the process of managing enterprise data with the aim of making data more accessible, reliable,...

    Polars - A lightning fast DataFrames library

    We have previously talked about the challenges that the latest SOTA models present in terms of computational complexity. We've also talked about...

    The Importance of Machine Learning Model Validation and How It Works

    Model validation is a core component of developing machine learning or artificial intelligence (ML/AI). While it’s separate from training and...

    MATLAB for Data Science and Machine Learning

    The opportunities to solve problems with the use of data are greater than ever, and as different industries embrace them, the available data has been...
    Code

    Getting Started with OpenCV

    In this article we talk about the foundations of Computer vision, the history and capabilities of the OpenCV framework, and how to make your first...

    Reinforcement Learning: The K-armed bandit problem

    In a previous blog post we talked about the foundations of reinforcement learning. We covered classical and operant conditioning, rewards, states,...
    Machine Learning

    Everything You Need to Know about Feature Stores

    Features are input for machine learning models.  The most efficient way to use them across an organization is in a feature store that automates the...

    5 MLOps Best Practices for Large Organizations

    Machine learning operations (MLOps) is more than just the latest buzzword in the artificial intelligence (AI) and machine learning (ML) community....
    Practical Techniques

    Data Exploration with Pandas Profiler and D-Tale

    We all have heard how data is the new oil. I always say that if that is the case, we need to go through some refinement process before that raw oil...

    Building a Speaker Recognition Model

    The ability of a system to recognize a person by their voice is a non-intrusive way to collect their biometric information. Unlike fingerprint...
    Leaders at Work

    Fireside Chat: Stig Pedersen from Topdanmark

    "In having one or two very successful algorithmic deployments, the business then begins coming to you to ask for assistance. It becomes a mutual...

    Defining Metrics to Drive Machine Learning Model Adoption & Value

    One of the biggest ironies of enterprise data science is that although data science teams are masters at using probabilistic models and diagnostic...
    Model Management

    A Guide to Machine Learning Model Deployment

    Machine-learning (ML) deployment involves placing a working ML model into an environment where it can do the work it was designed to do. The process...

    Machine Learning Modeling: How It Works and Why It’s Important

    Models are the central output of data science, and they have tremendous power to transform companies, industries, and society.  At the center of...

    The Role of Containers on MLOps and Model Production

    Container technology has changed the way data science gets done. The original container use case for data science focused on what I call,...
    Engineering

    Tensorflow, PyTorch or Keras for Deep Learning

    Machine learning provides us with ways to create data-powered systems that learn and enhance themselves, without being specifically programmed for...

    Getting Started with Ray

    In this blog post we give a quick introduction to Ray. We talk about the architecture and execution model, and present some of Ray's core paradigms...

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