In my previous articles, I talked about the similarities between operationalisation of open source analytics, and working in a restaurant. I set out all the challenges in the process, and I am now (finally) going to talk about how we can solve them. This should help you to achieve open source operationalisation worthy of three stars! I suggest that it is important to work on three aspects: analytics heterogeneity, treating models as corporate assets and not just focusing on technology.
In my previous articles, I talked about comparing operationalising open source to the difference between your kitchen at home and a restaurant kitchen. In this article, I am going to talk about the challenges of operationalisation. In my next article, I will move on to how to overcome them, so that you can achieve three-star worthy open source operationalisation.
I’ve talked about four categories of similarities between cooking and open source operationalisation: people, ingredients (or data), equipment (or technology) and processes. I am going to look at the challenges in each of these categories.
First, people. In a restaurant kitchen…
In my previous article, I suggested that operationalising an analytical model, including one built with open source, was a bit like the difference between cooking in your kitchen at home, and in a restaurant kitchen. I said that there were four categories of similarities:
2. Ingredients and Data
3. Equipment and Technology
The most obvious difference is the number of people involved. When you cook at home, it is probably just you, or perhaps you and your partner. In a restaurant kitchen, each dish may have two or three people involved. To serve an entire meal…
I am going to make a bold statement: without operationalisation, analytical modelling is just an intellectual activity.
The process of operationalising a model is what allows us to gain insight from data, and unlock business value through data-driven decisioning. To get value, we need to move the model from where it was built, tested and validated, and put it into production, where it can access new data.
What does that have to do with open source? In 2019, Kaggle reported that 87% of AI developers depended on open source technologies. RedHat’s 2020 state of enterprise open source report showed that…
My previous articles talked about why organisations should choose ModelOps, and how to implement ModelOps using a holistic approach covering process, people and technology. This article explains the levels of sophistication available when implementing ModelOps, and how to choose the right level.
ModelOps is NOT a one-size-fits-all approach. It is important to identify the appropriate level of sophistication based on both the organisation’s current readiness, and its current and future business needs.
We have identified three main levels of ModelOps sophistication. However, in practice there is a fourth: many organisations may not yet even be at Level 1, especially if…
In my previous article, I noted that introducing ModelOps requires a holistic approach combining process, people and technology. This article provides more detail about how to do this.
The analytics lifecycle provides a good ModelOps process for almost any organisation. It does not cover the technical detail needed once conversations become more advanced. However, it can be used very effectively to understand an organisation’s current readiness and the benefits they could achieve from a ModelOps approach. The general principle is to use the individual elements of the lifecycle, combined with a red, amber and green (RAG) scale to determine the…
Industry analysts including Gartner and Forrester have long noted that many organisations are failing to capitalise on their investment in analytics. Generally speaking, this results from a focus on model development and data science, however this then results in a struggle to integrate the models into business operations — the action that actually unlocks the value from analytics.
ModelOps is a framework or practice that has emerged to address this challenge and is inspired by the success of DevOps. Its focus is operationalising analytics, i.e. taking models from development to production, and therefore transforming modelling from an academic exercise to…
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