McKinsey & Company, through its advanced analytics firm QuantumBlack, has released several data science packages and open-source projects. These tools are designed to help data scientists and engineers create data pipelines, visualize data, and build machine learning models.
- Kedro: This is an open-source tool for creating data pipelines, which are the building blocks of many machine learning projects. Kedro structures analytics code so that data flows seamlessly through all stages of an analytics project. It was released in early June and quickly became the number-one trending product on GitHub[4]. In 2022, Kedro was donated to the Linux Foundation's AI & Data incubator to evolve as an open standard[2][8].
- Vizro: This is a component of the QuantumBlack Horizon suite that helps users visualize data from their AI models. It is part of McKinsey's open-source ecosystem[2].
- CausalNex: This is a tool for building cause-and-effect models. It has been available to the public since 2020 through QuantumBlack Labs’ GitHub organization[2].
- MLRun: This is an open MLOps platform for quickly building and managing continuous ML applications across their lifecycle. It integrates into the development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications[6].
- Nuclio: This is a high-performance "serverless" framework focused on data, I/O, and compute-intensive workloads. It integrates with popular data science tools, such as Jupyter and Kubeflow, supports a variety of data and streaming sources, and can be executed over CPUs and GPUs[6].
- QBStyles: This is a Python package with light and dark Matplotlib styles[6].
In addition to these tools, McKinsey has also developed a machine learning algorithm for in silico screening, which is used in the pharmaceutical industry to expedite discovery timelines for small molecules[1].
McKinsey's open-source ecosystem can be accessed at GitHub, where they host products from across the firm, including leading-edge technologies and IP in AI, digital, and cloud[2].
McKinsey & Co and its subsidiaries have released several data science and AI tools to help clients scale their AI initiatives and realize value from AI.
One of the key tools is the QuantumBlack Horizon suite, a set of AI development tools from QuantumBlack, AI by McKinsey. This suite was built within QuantumBlack Labs, an AI and machine learning innovation hub. The QuantumBlack Horizon suite integrates AI development tools and is designed to be flexible, interoperable, and compatible with all key technology platforms and modern tech-stack components clients already have in place. The suite helps tech leaders achieve four key objectives in their AI initiatives: clean, organized, and accurate data across internal and external sources; scalable, repeatable AI models that build on each other; a factory-like approach to model development and monitoring; and performance transparency that enables quick, reliable decision making[4].
In addition to the QuantumBlack Horizon suite, McKinsey has also been involved in the development and deployment of generative AI (gen AI) tools. The company's research has shown that gen AI tools have the potential to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences[3].
Furthermore, McKinsey's research has found that gen AI could substantially increase labor productivity across the economy. For example, when McKinsey had 40 of its own developers test generative AI–based tools, they found impressive speed gains for many common developer tasks[5].
In summary, McKinsey & Co and its subsidiaries have released a range of data science and AI tools, with a particular focus on gen AI tools and the QuantumBlack Horizon suite, to help clients scale their AI initiatives and realize significant value from AI.