machineByte is a platform dedicated to growing education and information exchange in machine learning and alternative data in investment management through news, commentary, research, data, academic journals, industry ThinkTanks and global industry events.
From our offices in the Americas, EMEA & Asia Pacific, we bring together the leading innovators and thought leaders in quantitative finance, data science, machine learning and blockchain to grow education among financial and tech professionals at the centre of the growth in AI in finance.
Commentary & News
Members of machineByte receive daily news & commentary, authored by the machineByte editorial team, on the latest trends, innovations and issues in machine learning, data science, blockchain and related markets.
Data & Research
Our unique research and data provides analysis in to how institutional investors are applying machine learning across markets, asset classes and products.
Join senior machine learning and data science professionals at machineByte conferences around the globe. Our educational seminars provide training on python for finance, natural language processing for portfolio construction and reinforcement learning for investment management, among other topics.
machineByte’s webinars will feature distinguished machine learning and data science professionals presenting on areas across programming, types of learning and generating alpha through machine learning across markets, among other areas.
machineByte’s industry & academic ThinkTanks in Europe, the US and Asia bring together the leading quantitative, data science and machine learning professionals in finance and tech to discuss the latest trends and innovations in machine learning in investment management.
The Journal of Machine Learning in Finance
machineByte’s journal delivers academic and practitioner insights from quantitative finance, data science and tech innovators with one clear goal: providing a platform that fosters education and innovation in machine learning and alternative data through independent research.