Blog on AI, ML, Strategy

Effective communication between scientists and non-scientists

5/14/2022

0 Comments

Read the full article here:
https://www.colabra.app/blog/effective-communication-between-scientists-and-non-scientists

0 Comments

Data Labeling and Relabeling in Data Science

5/6/2022

0 Comments

Read the full article here:
https://www.unbox.ai/blog/post/data-labeling-and-relabeling

0 Comments

Understanding and Measuring Data Quality

4/28/2022

0 Comments

Read the full article here:
https://www.unbox.ai/blog/post/understanding-and-measuring-data-quality

0 Comments

The Metric Layer & how it fits into the Modern Data Stack

4/25/2022

0 Comments

Read the full article here:
https://blog.transform.co/data-talks/the-metric-layer-why-you-need-it-examples-and-how-it-fits-into-your-modern-data-stack/

0 Comments

Team Development Tips for Engineering and Product Leaders

4/18/2022

0 Comments

Read the full article here:
https://statushero.com/blog/team-development-tips-for-engineering-and-product-leaders

0 Comments

Five 5-minutes Team-Building Activities for Remote Teams

3/19/2022

0 Comments

Read the full article here:
https://statushero.com/blog/five-5-minute-team-building-activities-for-remote-teams/

0 Comments

Benefits of FAANG companies for Data Science & Machine learning roles

3/7/2022

0 Comments

Read the full article here -
https://becominghuman.ai/benefits-of-faang-for-data-science-machine-learning-roles-639c8b10f8d6

0 Comments

Why Data Democratization is important to your business?

3/1/2022

0 Comments

Read the full article here:
blog.transform.co/why-data-democratization-is-important-to-your-business/

0 Comments

When is the right time to migrate to Kubernetes?

2/7/2022

0 Comments

Read the full article here:
https://www.containiq.com/post/migrate-to-kubernetes

0 Comments

How to ensure Data Quality through Governance

12/29/2021

0 Comments

Read the full article here:
blog.transform.co/how-to-ensure-data-quality-through-governance/

0 Comments

Data Labeling: The Unsung Hero Combating Data Drift

10/27/2021

0 Comments

Read the full article here:
labelstud.io/blog/Data-Labeling-The-Unsung-Hero-Combating-Data-Drift.html

0 Comments

ML Engineer vs Data Scientist

10/21/2021

0 Comments

Read the full article here:
https://neptune.ai/blog/ml-engineer-vs-data-scientist

0 Comments

Knowledge Distillation: Principles, Algorithms, Applications

10/7/2021

0 Comments

Published by Neptune.ai

Read the full article here:
https://neptune.ai/blog/knowledge-distillation

0 Comments

Best Practices for Improving Machine Learning and Deep Learning Models

8/19/2021

0 Comments

Published by Neptune.ai

Read the full article here:
https://neptune.ai/blog/improving-machine-learning-deep-learning-models

0 Comments

Developing AI/ML Projects for Business - Best Practices

5/7/2021

10 Comments

Published by Neptune.ai

Read the full article at:
https://neptune.ai/blog/building-ai-ml-projects-for-business-best-practices

10 Comments

How to build Machine Learning Teams that Deliver

4/18/2021

2 Comments

Published by Neptune.ai

Read the full article here:
http://neptune.ai/blog/how-to-build-machine-learning-teams-that-deliver

2 Comments

Why Corporate AI Projects fail? Part 2/4

4/11/2021

0 Comments

Published in BecomingHuman.ai

tldr: Poor processes and culture can derail the success of many an exceptional AI team

In part 1, I introduced a four-pronged framework for analysing the principal factors underlying the failure of corporate AI projects:

Intuition (Why)
Process/Culture (How)
People (Who)
Systems (What)

and addressed in detail how a lack of organizational intuition for AI can undermine any efforts to embrace and adopt AI successfully.

In the second part of the blog series, I will focus on core aspects of organizational processes and culture that companies should inculcate to ensure that their AI teams are successful and deliver significant business impact.

Figure 1. The Facade Culture (source: Dilbert.com)

Culture

Organizational culture is the foundation on which a company is built and shapes its future outcomes related to commercial impact and success, hiring and retention, as well as the spirit of innovation and creativity. Whilst organizational behaviour and culture have been studied for decades, it needs to be relooked in the context of new-age tech startups and enterprises. The success of such cutting-edge AI-first companies is highly correlated with the scale of innovation through new products and technology, which necessitates an open and progressive work culture.

Typically, new startups on the block, especially those building a core AI product or service, are quick to adopt and foster a culture that promotes creativity, rapid experimentation and calculated risk-taking. Being lean and not burdened by any legacy, most tech startups are quick to shape the company culture in the image of the founders’ vision and philosophy (for better or worse). However, the number of tech companies that have become infamous for the lack of an inclusive and meritocratic culture are far too many.

There are innumerable examples, from prominent tech startups like Theranos, Uber to big tech companies like Google and Facebook, where an open and progressive culture has at times taken a back seat. However, with the increasing focus on sustainability, diversity and inclusion, and ESG including better corporate governance, it is imperative for tech companies to improve organizational culture and not erode employee, consumer or shareholder trust or face real risks to the business from financial as well as regulatory authorities as recently experienced by BlackRock and Deliveroo.

Here is a ready reckoner of some of the ways AI companies tend to lose sight of culture:

Leadership is not clear that the goal of AI is to augment human productivity, reduce costs and improve efficiencies, and not to replace employees. Without human partners in the loop, AI-based products and services cannot succeed
Hiring non-technical leaders with little intuition of AI, and/or technical leaders with poor product or business mindset to lead AI teams or orgs
Poor organization alignment about the long-term vision and purpose of AI across key stakeholders in multiple business verticals
Lack of investment in organization-wide training in AI especially for business teams resulting in poor intuition about the commercial potential of AI
Lack of allocation of budget for investing in AI, or investing too late whereby early movers and competitors have already embraced and adopted AI across the org
Poor management of AI teams with no roadmap, vision, or hiring strategy
Talking up the promise of AI with no well-defined use cases and projects in sight
Treating AI like software and expecting things to work the same way as for building and delivering software products
No structured processes and mechanisms for embracing AI: from defining business-relevant AI use cases to deploying them in the real world

Figure 2. How to scope AI projects (source: DeepLearning.ai)

Process
There are several processes that are integral for ensuring a successful AI outcome across the entire lifecycle from conception to production. However, from first-principles, the primary process that needs to be streamlined and managed well is identifying the right use cases for AI that have the potential to create significant commercial impact. In this blog, I will focus only on this particular aspect and expound on the other processes in separate blogs.

What can go wrong in identifying the right set of AI use cases?

Focus on the technical aspects of AI instead of the business problem
Defining a use case without checking availability of data for the problem
Poor intuition for the feasibility of the AI use case without diving deep into the data and consulting business or domain experts
No clearly defined acceptance criteria, success metrics or KPIs for the use case
No shared alignment across cross-functional teams for the proposed use cases
Poor project management, and misallocation of time and bandwidth across AI and cross-functional teams
Poor prioritization and ranking of proposed use cases
No clearly defined project milestones and timelines to validate the use case once the project has started
Poor brainstorming of potential AI solutions for the use case

So, having listed a variety of issues that can go wrong in identifying an AI use case, how should one ideally go about scoping AI projects systematically? As per Figure 2, the strategy to scope an AI use case involves 5 steps: from identifying a business problem to brainstorming AI solutions to assessing feasibility and value to determining milestones and finally budgeting for resources.

The scoping process starts with a careful dissection of business, not AI problems, that need to be solved for creating commercial value. As discussed above, if not done right, the rest of the AI journey in an organization is bound to fail.

Secondly, it is important to brainstorm potential AI solutions across AI, engineering and product teams to shortlist a set of approaches and techniques that are practically feasible instead of going with the latest or most sophisticated AI model or algorithm.

Thirdly, AI teams should assess the feasibility of shortlisted methods by creating a quick prototype, validating the approach based on literature survey or discussions with domain experts within the company or partner with external collaborators accordingly. If a particular method does not appear to be feasible, then teams should consider the alternative approaches until they are ruled out.

Once the initial efforts have validated the use case, its feasibility and potential approaches, it is critical to define key business metrics, KPIs, acceptance or success criteria. These are not composed of the typical AI model metrics like precision, accuracy of F-1 score, but KPIs need to be defined that are directly correlated with the impact of the AI models on business goals e.g. retention, NPS, customer satisfaction amongst others.

The final step involves program management of the entire project from allocating time, bandwidth of individual contributors in the AI as well as partner teams, budget for collecting or labeling data, hiring data scientists or buying software or infrastructure to setup and streamline the entire AI lifecycle.

Tldr part 2:
Before you head out to build AI, first ask what are the business problems that are big enough and suitable for an AI-based solution? What business metrics and objectives ought to be targeted? Scope out the problem systematically to ensure the best chance of success.

Build on the initial successes of AI and foster a meritocratic and open culture of innovation and cross-functional collaboration to build AI that solves a variety of business use cases.

0 Comments

Why Corporate AI projects fail? Part 1/4

3/2/2021

0 Comments

Published in BecomingHuman.ai

Tldr: Corporate AI failures can be ascribed to poor Intuition, Process, Systems, People

The promise of AI is real. We are at the crossroads of the next industrial revolution where AI is automating industrial processes and technologies that were hitherto considered state-of-the-art. AI is expected to create global commercial value of nearly USD 13 Trillion by 2030 (McKinsey Global Institute). Given the immense commercial value that AI can unlock, it is no surprise that businesses of all kinds and sizes have jumped on the AI bandwagon and are repositioning themselves as ‘AI-first’ or ‘AI-enabled.

Source: Dilbert (https://dilbert.com)

However, the groundbreaking progress and transformation that AI has brought across industry belies the stark reality of an increasing number of failed AI projects, products and companies (e.g. IBM Watson, and many more).

How can startups and large enterprises battle these tough odds to drive innovation and digital transformation across the organization? In this blog, I will examine from first principles common themes that typically underlie failed AI projects in corporations, and questions business leaders and teams should address when embarking on AI projects.

I have classified these under four broad areas and will tackle each of these themes individually in future blog posts:

Intuition (Why)
Process/Culture (How)
People (Who)
Systems (What)

Part 1: Intuition (Why)
Commercial AI projects often fail due to a lack of organizational understanding of the utility of AI vis-a-vis the business problem(s) to be solved. More often than not, throwing a complex AI-based solution at a problem is not the right approach, where a simpler analytical or rule-based solution is sufficient to have things up and running. It is therefore paramount to decode the business problem first and ask whether an AI approach is the only and best way forward.

Unlike software engineering projects, the fundamental unit of AI is not lines of code, but code and data. In an enterprise, data typically belongs to a particular business domain, and is generated by the interaction of customers with specific business products or services.

Here, a customer-centric approach is critical to understand the context in which this data is generated so that AI models may be developed to predict or influence user behavior to meet well-defined business objectives with clear success criteria. Wherever possible, the data scientists should themselves use and experiment with their company’s products/services by donning a ‘customer’s hat’ to decode the customer mindset. It’s hard to understand the nuances of training data if you don’t intimately understand the customer ‘persona’ to begin with.

Data reflects more than just mere numbers. Making sense of data requires a holistic cross-functional understanding from a business, product, customer as well as technical perspective. Typically, these functional roles are played by different teams within a company, necessitating a strong collaborative effort to demystify the business problem, question the existing solutions and come up with new hypotheses, test and prove or disprove these hypotheses quickly via iterative experiments to hone in on a feasible solution and strategy.

Here, the importance of domain knowledge or subject matter expertise cannot be stressed enough. It takes years to gain deep domain expertise which enables practitioners to develop better intuition for the business problem and the underlying data to propose feasible solutions or strategies.

As data scientists typically lack expertise in business domains, it is imperative they complement their algorithmic data science skills with expert knowledge from those who work closely with the customer and understand the business problem intimately.

Tldr (Part 1/4):
Ask why is AI needed for your business problem? Is it the only way to solve the problem? And if yes, build and test hypotheses by leveraging the collective organizational knowledge and intuition across cross-functional teams that specialize in data science, business, product, operations.

0 Comments