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 is converted into useful products. For data, this refinement includes doing some cleaning and manipulations that provide a better understanding of the information that we are dealing with. In a previous blog, we have covered how Pandas Profiling can supercharge the data exploration required to bring our data into a predictive modelling phase. We covered the importance of Exploratory Analytics including observing the frequency of missing data and co ..read more

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society.  At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. Even though models look like software and involve data, models have different input materials, different development processes, and different behaviors. The process of creating models is called modeling. What Is Modeling? A model is a special type of algorithm. In software, an algorithm is a hard-coded set of instructions ..read more

For a model-driven enterprise, having access to the appropriate tools can mean the difference between operating at a loss with a string of late projects lingering ahead of you or exceeding productivity and profitability forecasts. This is no exaggeration by any means. With the right tools, your data science teams can focus on what they do best – testing, developing and deploying new models while driving forward-thinking innovation. What Are Modeling Tools? In general terms, a model is a series of algorithms that can solve problems when given appropriate data. Just as the human brain can solve ..read more

In the world of machine learning (ML) and artificial intelligence (AI), governance is a lifelong pursuit. All models require testing and auditing throughout their deployment and, because models are continually learning, there is always an element of risk that they will drift from their original standards. As such, model governance needs to be applied to each model for as long as it’s being used.  What Is Model Governance? Model governance is a framework that determines how a company implements policies, controls access to models and tracks their activity. It’s similar to corporate governa ..read more

Data science is an incredibly complex field. When you factor in the requirements of a business-critical machine learning model in a working enterprise environment, the old cat-herding meme won’t even get a smile.  Framing data science projects within the four steps of the data science lifecycle (DSLC) makes it much easier to manage limited resources and control timelines, while ensuring projects meet or exceed the business requirements they were designed for.  4 Steps in the DSLC The DSLC categorizes all of the tasks within any given project within four broad steps. This not only hel ..read more

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management. With a framework and Enterprise MLOps, organizations can manage data science at scale and realize the benefits of Model Risk Management ..read more

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. It is not possible to fully understand the inferential process of a deep neural network and to prove that it would generalise as exp ..read more

The data science lifecycle (DLSC) has been defined as an iterative process that leads from problem formulation to exploration, algorithmic analysis and data cleaning to obtaining a verifiable solution that can be used for decision making. For companies creating models to scale, an enterprise Machine Learning Operation (MLOps) platform not only needs to support enterprise-grade development and production, it needs to follow the same standard process that data scientists use. How Enterprise MLOps Integrates into the DSLC DSLC can be divided into four steps or stages: Manage, Develop, Deploy and ..read more

Data science is an exciting, interdisciplinary field that is revolutionizing the way companies approach every facet of their business. Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication. In this article, we will provide an overview of the three overlapping components of data science, the importance of communication and collaboration, and how the Domino Data Lab MLOp ..read more

Data science is a field at the convergence of statistics, computer science and business. It is highly valued by organizations as they strive to remain competitive, increase revenues and delight customers because data scientists are able to coax insight on how to improve decision making by the business out of the vast stores of data created by the business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. In this article, take a deep dive into data science and how ..read more