Data science teams are an integral part of early-stage or growth-stage start-ups as midlevel and enterprise companies. A data science team can include a wide range of roles that take care of the end-to-end machine learning lifecycle from project conceptualization to execution, delivery, and monitoring:
The manager of a data science team in an enterprise organization has multiple responsibilities, including the following:
As the data science manager, it’s critical to have a structured, efficient hiring process, especially in a highly competitive job market where the demand outstrips the supply of data science and machine learning talent. A transparent, thoughtful, and open hiring process sends a strong signal to prospective candidates about the intent and culture of both the data science team and the company, and can make your company a stronger choice when the candidates are selecting an offer.
In this blog, you’ll learn about key aspects of the process of hiring a top-class data science team. You’ll dive into the process of recruitment, interviewing, and evaluating candidates to learn how to find the ones who can help your business improve its data science capabilities.
Benefits of an Efficient Hiring Process
Recent events have accelerated organizations’ focus on digital and AI transformation, resulting in a very tight labor market when you’re looking for data sciencedigital skills, like machinelike data science and machine learning, statistics, and programming.
A structured, efficient hiring process enables teams to move faster, make better decisions, and ensure a good experience for the candidates. Even if candidates don’t get an offer, a positive experience interacting with the data science and the recruitment teams makes them more likely to share good feedback on platforms like Glassdoor, which might encourage others to interview at the company.
Hiring Data Science Teams
A good hiring process is a multistep process, and in this section, you’ll look at every step of the process in detail.
Building a Funnel for Talent
Depending on the size of the data science team, the hiring manager may have to assume the responsibility of reaching out to candidates and building a pipeline of talent. In larger organizations, managers can work with in-house recruiters or even third-party recruitment agencies to source talent.
It’s important for the data science managers to clearly convey the requirements for the recruited candidates, such as the number of candidates desired and the profiles of those candidates. Candidate profiles might include things like previous experience, education or certifications, skill set or tech stack, and experience with specific use cases. Using these details, recruiters can then start their marketing, advertising, and outreach campaigns on platforms, like LinkedIn, Glassdoor, Twitter, HackerRank, and LeetCode.
In several cases, recruiters may identify candidates who are a strong fit but who may not be on the job market or are not actively looking for new roles. A database of all such candidates ought to be maintained so that recruiters can proactively reach out to them at a more suitable time and reengage the candidates.
Another trusted source of identifying good candidates is through employee referrals. An in-house employee referral program that incentivizes current employees to refer candidates from their network is often an effective way to attract the specific types of talent you’re looking for.
The data science leader should also publicize their team’s work through channels, like conferences or workshops, company blogs, podcasts, media, and social media. By investing dedicated time and energy in building up the profile of the data science team, it’s more likely that candidates will reach out to your company seeking data science opportunities.
When looking for a diverse set of talent, the search an be difficult as data science is a male dominated field. As a result, traditional recruiting paths will continue to reflect this bias. Reaching out and building relationships with groups such as Women in Data Science, can help broad the pipeline of talent you attract.
Defining Roles and Responsibilities
Good candidates are more likely to apply for roles that have a clear job description, including a list of potential data science use cases, a list of required skills and tech stack, and a summary of the day-to-day work, as well as insights into the interviewing process and time lines. Crafting specific, accurate job descriptions is a critical—if often overlooked—aspect of attracting candidates. The more information and clarity you provide up front, the more likely it is that candidates have sufficient information to decide if it’s a suitable role for them and if they should go ahead with the application or not. If you’re struggling with creating this, you can start with an existing job description template and then customize it in accordance with the needs of the team and company.
It's also critical to not over populate a job description with every possible skill or experience you hope a candidate brings. That will narrow your potential applicant pool. Instead focus on those skills and experiences that are absolutely critical. The right candidate will be able to pick up other skills on the job.
It can be useful for the job description to include links to any recent publications, blogs, or interviews by members of the data science team. These links provide additional details about the type of work your team does and also offer candidates a glimpse of other team members.
Here are some job description templates for the different roles in a data science team:
When compared to software engineering interviews, the interview process for data science roles is still very unstructured, and data science candidates are often uncertain about what the interview process involves. The professional position of data scientist has only existed for a little over a decade, and in that time, the role has evolved and transformed, resulting in even newer, more specialized roles, such as data engineer, machine learning engineer, applied scientist, research scientist, and product data scientist.
Because of the diversity of roles that could be considered data science, it’s important for a data science manager to customize the interviewing process depending on the specific profile they’re seeking. Data scientists need to have expertise in multiple domains, and one or more second-round interviews can be tailored around these core skills:
Given how tight the job market is for data science talent, it’s important to not over complicate the process. The more steps in the process, the longer it will take and the higher the likelihood you will lose viable candidates to other offers. So be thoughtful in your approach and evaluate it periodically to align with the market.
Types of Data Science Interviews
Interviews are often a multistep process and can involve multiple steps of assessments.
To save time, one or more screening rounds can be conducted before inviting candidates for second-round interviews. These screening interviews can take place virtually and involve an assessment of essential skills, like programming and machine learning, along with a deep dive into the candidate’s experience, projects, career trajectory, and motivation to join the company. These screening rounds can be conducted by the data science team itself or outsourced to other companies, like HackerRank, HackerEarth, Triplebyte, or Karat.
Once candidates have passed the screening interviews, the top candidates will be invited to a second interview, either virtually or in person. The data science manager has to take the lead in terms of coordinating with internal interviewers to confirm the schedule for the series of interviews that will assess the candidate’s skills, as described earlier. On the day of the second-round interviews, the hiring manager needs to help the candidate feel welcome and explain how the day will proceed. Some companies like to invite candidates to lunch with other team members, which breaks the ice by allowing the candidate to interact with potential team members in a social setting.
Each interview in the series should start by having the interviewer introduce themself and provide a brief summary of the kind of work they do. Depending on the types of interviews and assessments the candidate has already been through, the rest of the interview could focus on the core skill set to be evaluated or other critical considerations. Wherever possible, interviewers should offer the candidate hints if they get stuck and otherwise try to make them feel comfortable with the process. The last five to ten minutes of each interview should be reserved for the candidate to ask questions to the interviewer. This is a critical component of second-round interviews, as the types of questions a candidate asks offer a great deal of information about how carefully they’ve considered the role.
Before the candidate leaves, it’s important for the recruiter and hiring manager to touch base with the candidate again, inquire about their interview experience, and share time lines for the final decision.
It is common for there to be some sort of case study or technical assessment to get a better understanding of a candidate’s approach to problem solving, dealing with ambiguity and practical skills. This provides the company with good information about how the candidate may perform in the role It also is an opportunity to show the candidate what type of data and problems they may work on when working for you.
After the second-round interviews and technical assessment, the hiring manager needs to coordinate a debrief session. In this meeting, every interviewer shares their views based on their experience with the candidate and offers a recommendation if the candidate should be hired or not.
After obtaining the feedback from each member of the interview panel, the hiring manager also shares their opinion. If the candidate unanimously receives a strong hire or a strong no-hire signal, then the hiring manager’s decision is simple.
However, there may be candidates who perform well in some interviews but not so well in others, and who elicit mixed feedback from the interview panel. In cases like this, the hiring manager has to make a judgment call on whether that particular candidate should be hired or not. In some cases, an offer may be extended if a candidate didn’t do well in one or more interviews but the panel is confident that the candidate can learn and upskill on the job, and is a good fit for the team and the company.
If multiple candidates have interviewed for the same role, then a relative assessment of the different candidates should be considered, and the strongest candidate or candidates, depending on the number of roles to be filled, should be considered.
While most of the interviews focus on technical data science skills, it’s also important for interviewers to use their time with the candidate to assess soft skills, like communication, clarity of thought, problem-solving ability, business sense, and leadership values. Many large companies place a very strong emphasis on behavioral interviews, and poor performance in this interview can lead to a rejection, even if the candidate did well on the technical assessments.
After the debrief session, the data science manager needs to make their final decision and share the outcome, along with a compensation budget, with the recruiter. If there’s no recruiter involved, the manager can move directly to making the candidate an offer.
It’s important to move quickly when it comes to making and conveying the decision, especially if candidates are interviewing at multiple companies. Being fast and flexible in the hiring process gives companies an edge that candidates appreciate and take into consideration in their decision-making process.
Once the offer and details of compensation have been sent to the candidate, it’s essential to close the offer quickly to prevent candidates from using your offer as leverage at other companies. Including a deadline for the offer can sometimes work to the company’s advantage by incentivizing candidates to make their decision faster. If negotiations stretch and the candidate seems to lose interest in the process, the hiring manager should assess whether the candidate is really motivated to be part of the team. Sometimes, it may move things along if the hiring manager steps in and has another brief call with the candidate to help remove any doubts about the type of work and projects. However, additional pressure on the candidates can often work to your disadvantage and may put off a skilled and motivated candidate in whom the company has already invested a lot of time and money.
In this article, you’ve looked at an overview of the process of hiring a data science team, including the roles and skills you might be hiring for, the interview process, and how to evaluate and make decisions about candidates. In a highly competitive data science job market, having a robust pipeline of talent, and a fast, fair, and structured hiring process can give companies a competitive edge.
Published by Domino Data Lab
Reproducibility is a cornerstone of the scientific method and ensures that tests and experiments can be reproduced by different teams using the same method. In the context of data science, reproducibility means that everything needed to recreate the model and its results such as data, tools, libraries, frameworks, programming languages and operating systems, have been captured, so with little effort the identical results are produced regardless of how much time has passed since the original project.
Reproducibility is critical for many aspects of data science including regulatory compliance, auditing, and validation. It also helps data science teams be more productive, collaborate better with nontechnical stakeholders, and promote transparency and trust in machine learning products and services.
In this article, you’ll learn about the benefits of reproducible data science and how to ingrain reproducibility in every data science project. You’ll also learn how to cultivate an organizational culture that promotes greater reproducibility, accountability, and scalability.
Here is the full article.
Machine learning models, especially deep neural networks, are trained using large amounts of data. However, for many machine learning use cases, real-world data sets do not exist or are prohibitively costly to buy and label. In such scenarios, synthetic data represents an appealing, less expensive, and scalable solution.
Additionally, several real-world machine learning problems suffer from class imbalance—that is, where the distribution of the categories of data is skewed, resulting in disproportionately fewer observations for one or more categories. Synthetic data can be used in such situations to balance out the underrepresented data and train models that generalize well in real-world settings.
Synthetic data is now increasingly used for various applications, such as computer vision, image recognition, speech recognition, and time-series data, among others. In this article, you will learn about synthetic data, its benefits, and how it is generated for different use cases.
👉 Here is the full article
Data drift refers to the phenomenon where the distribution of live, real-world data differs or “drifts” from the distribution of data used to train a machine learning model. When data drift occurs, the performance of machine learning models in production degrades, resulting in inaccurate predictions. This reduction in the model’s predictive power can adversely impact the expected business value from the investment in training. If data drift is not identified in time, the machine learning model may become stale and eventually useless.
In this article, you’ll learn more about data drift, exploring why and in what ways it occurs, its impact, and how it can be mitigated and prevented.
👉 Here is the full article
Supervised machine learning models are trained using data and their associated labels. For example, to discriminate between a cat and a dog present in an image, the model is fed images of cats or dogs and a corresponding label of “cat” or “dog” for each image. Assigning a category to each data sample is referred to as data labeling.
Data labeling is essential to imparting machines with knowledge of the world that is relevant for the particular machine learning use case. Without labels, models do not have any explicit understanding of the information in a given data set. A popular example that demonstrates the value of data labeling is the ImageNet data set. More than a million images were labeled with hundreds of object categories to create this pioneering data set that heralded the deep-learning era.
In this article, you’ll learn more about data labeling and its use cases, processes, and best practices.
👉 Here is the full article
Modern companies now unanimously recognize the value of data for driving business growth. However, high-quality data is much more valuable than data assets of poor quality. As companies accumulate petabytes of data from various sources, it becomes imperative to focus on the quality of data and filter out bad data.
Data is the fundamental building block for predictive machine learning models. Although having access to greater amounts of data is beneficial, it doesn’t always translate to better-performing machine learning models. Sampling training data that passes quality checks and meets certain acceptance criteria can significantly boost the accuracy of the model predictions.
In this article, you’ll learn more about why high-quality data is essential for building robust machine learning models, expanding on the various parameters that define data quality: accuracy, completeness, consistency, timeliness, uniqueness, and validity. You’ll also explore a few mechanisms you can implement to measure and improve the quality of your data.
👉 Here is the full article
I receive several messages about the benefits of joining FAANG and similar companies and startups in the context of Data Science, Machine Learning & AI roles.
Here’s my take, in no particular order:
1. 𝐁𝐫𝐚𝐧𝐝. FAANG+ are not only the top technology companies but also the biggest companies by market cap -> great brand to add to your profile, top compensation and benefits.
2. 𝐒𝐜𝐨𝐩𝐞. The scope of AI/ML applications in these companies is tremendous as they have tons of data. You can get to work on multiple use cases, driven by statistics, machine learning, deep learning, unsupervised / semi-supervised / self-supervised, reinforcement learning etc. Internal team transfers facilitate expanding your breadth of ML experience.
3. 𝐁𝐚𝐫. The AI/ML work is cutting edge, as most of these companies invest heavily in R&D and create game-changing techniques and models. They also invest heavily in platform, cloud, services etc. that make it easier to build and deploy ML products.
4. 𝐑&𝐃. You can do both research on moon-shot projects if that’s your cup of tea, as well as more immediate business-driven data science projects with monthly or quarterly deliverables.
5. 𝐏𝐞𝐨𝐩𝐥𝐞. You get to work with the creme-de-al-creme in terms of talent, ideas, vision, and execution. Your own level will rise if you are surrounded by some of the brightest folks, and also get to collaborate with their clients and collaborators from academia, startups as well.
6. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤. After FAANG, people go on to do many diverse things — from building a startup to doing cutting-edge research to non-profits to venture capital amongst others. You can find quality partners for the next steps of your career journey.
7. 𝐒𝐲𝐬𝐭𝐞𝐦𝐬. Processes and systems for AI/ML/Data are more mature and streamlined than smaller/newer companies which can facilitate your speed and execution of your projects.
8. 𝐂𝐮𝐥𝐭𝐮𝐫𝐞. The culture, on average, is more professional as these companies invest heavily in their employees and regularly come up with new employee-friendly policies to make it a great place to work.
9. 𝐅𝐫𝐞𝐞𝐝𝐨𝐦. After FAANG, you will be in demand and recruiters and hiring managers will seek you out if you’ve proved your chops whilst at the company. You will have more opportunities to sample from and greater freedom in terms of deciding your career and life trajectory, as you can also move internally to different countries.
10. 𝐈𝐦𝐩𝐚𝐜𝐭. Given the scale at which these companies operate, the scope for real-world measurable impact is enormous.
There are some downsides, caveats and exceptions as well, but on average these factors make FAANG and similar tech companies a very attractive proposition to launch, build and grow your career in data science and machine learning.
"Data democratization" has become a buzzword for a reason. Modern organizations rely extensively on data to make informed decisions about their customers, products, strategy, and to assess the health of the business. But even with an abundance of data, if your business can’t access or leverage this data to make decisions, it’s not useful.
To that end, data democratization, or the process of making data accessible to everyone, is quintessential to data-driven organizations.
Providing data access to everyone also implies that there are few if any roadblocks or gatekeepers who control this access. When stakeholders from different departments—like sales, marketing, operations, and finance—are permitted and incentivized to use this data to better understand and improve their business function, the whole organization benefits.
Successful data democratization requires constant effort and discipline. It’s founded on an organization-wide cultural shift that embraces a data-first approach and empowers every stakeholder to comfortably use data and make better data-driven decisions. As Transform co-founder James Mayfield put it, organizations should think about "democratizing insights, not data."
In this article, I will provide a detailed overview of data democratization, why organizations should invest in it, and how to actually implement it in practice.
👉 Here is the full article
Data governance is a fundamental pillar of modern digital businesses. It refers to a framework of processes and guidelines that companies use to ensure all enterprise data assets are managed and utilized appropriately.
Even if an organization has large investments in data infrastructure and teams, without a structured data governance framework, organizations will struggle to harness the full value of their data.
A strong framework provides a clear set of guidelines for all employees who access and consume data in downstream applications. It also contributes to greater trust in the authenticity and quality of data and allows data stakeholders to focus on core data tasks instead of worrying about whether the data was created, processed, stored accurately, and in compliance with national or domain-related legislations like GDPR, HIPAA, CCPA, and data localization laws. Given recent data breaches, the importance of a structured data governance framework cannot be emphasized enough.
In this article, you’ll learn how to ensure data quality through better data governance mechanisms, leading to an increase in data informed decision-making. You’ll also learn how a clear data governance framework contributes to improved data quality and value creation across the entire organization.
👉 Here is the full article
Data drift is a common problem for production machine learning systems. It occurs when the statistical characteristics of the training (source) and test (target) data begin to differ significantly. As illustrated in the image below, the orange curve depicting the original data distribution shifts to the purple curve, representing a change in statistical properties like the mean and variance.
Understanding data drift is fundamental to maintaining the predictive power of your production machine learning systems. For instance, a data science team may have started working on a machine learning use case in 2019, using training data from 2018, but by the time the model is ready to go into production, it’s 2020. There could be a huge change in the distribution between the source data from 2018 and the live data coming from 2020.
Any time a machine learning model is ready to be shipped, it needs to be rigorously tested on live data. It’s critical that you detect data drift before deploying a model to production.
In this article, I’ll illustrate the various types of data drift and how data drift impacts model performance along with several examples. I’ll also address data labeling, one of the popular ways to tackle data drift, and how to perform data labeling efficiently.
👉 Here is the full article
In 2010, DJ Patil and Thomas Davenport famously proclaimed Data Scientist (DS) to be the “Sexiest Job of the 21st century”. The progress in data science and machine learning over the last decade has been monumental. Data science has successfully empowered global businesses and organizations with predictive intelligence and data-driven decision-making to the extent that data science is no longer considered a fringe topic. Data science is now a mainstream profession and data science professionals are in high demand a cross all kinds of organizations from big tech companies to more traditional businesses.
A decade earlier the focus of data science was more on algorithmic development and modeling to extract robust insights from data. However, as data science has evolved over the decade, it has become clearer that data science involves more than just modeling. The machine learning lifecycle, from raw data through to deployment, now relies on specialized experts including data engineers, data scientists, machine learning engineers along with product and business managers.
The role of a machine learning engineer is gaining prominence across companies as they realized that the value of data science cannot be realized until a model is successfully deployed to production. Whilst a lot of tools and technologies such as Cloud APIs, AutoML, and a number of Python-based libraries have made the job of a data scientist easier, the MLOps of putting models into production and monitoring their performance is still quite unstructured.
👉 Here is the full article
Large-scale machine learning and deep learning models are increasingly common. For instance, GPT-3 is trained on 570 GB of text and consists of 175 billion parameters. However, whilst training large models helps improve state-of-the-art performance, deploying such cumbersome models especially on edge devices is not straightforward.
Additionally, the majority of data science modeling work focuses on training a single large model or an ensemble of different models to perform well on a hold-out validation set which is often not representative of the real-world data.
This discord between training and test objectives leads to the development of machine learning models that yield good accuracy on curated validation datasets but often fail to meet performance, latency, and throughput benchmarks at the time of inference on real-world test data.
Knowledge distillation helps overcome these challenges by capturing and “distilling” the knowledge in a complex machine learning model or an ensemble of models into a smaller single model that is much easier to deploy without significant loss in performance.
In this blog, I will:
👉 Here is the full article
Only 10% of AI/ML projects have created positive financial impact according to a recent survey of 3,000 executives.
Given these odds, it seems that building a profit generating ML project requires a lot of work across the entire organization, from planning to production.
In this article, I’ll share best practices for businesses to ensure that their investments in Machine Learning and Artificial Intelligence are actually profitable, and create significant value for the entire organization.
👉 Here is the full article
In this article, I have documented the best practices and approaches to build a productive Machine Learning team that creates positive business impact and generates economic value within corporate entities, be it startup or enterprise.
If you do Machine Learning, either as an individual contributor or team manager, I’ll help you understand your current team structure and how to improve internal processes, systems and culture. We’ll explore how to build truly disruptive ML teams that drive successful outcomes.
👉 Here is the full article
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:
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.
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:
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?
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.
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.
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:
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.
TLDR (or TL;DR) is a common internet acronym for “Too Long; Didn’t Read.” It likely originated on the comedy forum Something Awful around 2002 (source) and then became more popular in online forums like Reddit. It is often used in social media where the author or commenters summarise lengthy posts and provide a TLDR summary of one or two lines as a courtesy to other readers. TLDRs help readers get the gist of the information and enable quick informed decisions on whether to invest the time in reading the full post.
With Natural language processing (NLP) and automatic text summarization systems, TLDR generation can be automated. Automatic text summarization is a challenging problem of generating a shorter summary of a long document while preserving its essence. It has wide practical applications in multiple domains such as legal contract analysis, search (summarising use information in websites, entity-centric summarization from Wikipedia articles), question answering systems, media (generating news headlines, summarising articles in newsletters), marketing (generating copy, slogans) among others. Automatically generated text summaries help reduce reading time, are non-biased compared to human authored summaries, and could also be beneficial for a lot of personal day to day applications like email summarization, TLDR generation for posting on social media sites like Twitter, and more.
👉 Here is the full article
Published in KDNuggets
Coughing and sneezing were believed to be symptoms of the bubonic plague pandemic that ravaged Rome in the late sixth century. The origins of the benevolent phrase, “God bless you,” after a person coughs or sneezes is often attributed to Pope Gregory I, who hoped that this prayer would offer protection from certain death. The flu-like symptoms associated with the plague co-occur during the current Covid-19 pandemic as well, to the extent where “normal” coughs draw immediate alarm and concern. However, in the present technologically advanced times, we need not resort only to prayers. We can now build sophisticated AI models that learn complex acoustic features to distinguish between cough sounds from Covid-19 positive and otherwise healthy patients.
Since the start of the Covid-19 pandemic, multiple AI research teams have been working towards leveraging AI to improve screening, contact tracing, and diagnosis. Most of the preliminary work involved CT or X-ray scans [1,2,3,4] to diagnose Covid-19 faster and, in some cases, with better accuracy than the RT-PCR test. Recently, AI researchers have started testing cough sounds for preliminary diagnosis or a prescreening technique for Covid-19 detection in asymptomatic individuals. This is beneficial because, while someone may not have noticeable symptoms, the virus may still cause subtle changes in their body that may be detected by specific algorithms combining audio signal processing and machine learning. Cough-based audio diagnosis is non-invasive, cost-effective, scalable, and, if approved, could be a potential game-changer in our fight against Covid-19. This technology might also prove to have better efficacy than the standard strategy of prescreening for Covid-19 on the basis of temperature, especially for asymptomatic patients.
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Published in BusinessWorld
The promise of AI is real. Research from Accenture posits that AI could add $ 957 billion to the Indian economy and raise India’s income by 15 percent in 2035. Globally, the economic value that AI is expected to create close to $ 13 trillion by 2030. However, the stark reality is that India has close to 100,000 vacant data scientist jobs as of today, with the demand for AI-centric roles set to increase exponentially. How can India possibly unlock this massive economic potential of AI, without an established talent pipeline?
The lack of an established AI talent pipeline for a rapidly modernizing economy like India is alarming. While India has a working age population of close to 589 million, only 49 percent are said to possess digital skills, with the proportion of those able to understand and build AI products is far lower (World Economic Forum). Although the supply of engineering talent is steady, the nature of the rapidly changing jobs landscape means that core engineering jobs are transforming into digital roles that require strong software engineering and programming skills.
Not only Indian universities have failed to keep pace with adapting the course curricula to the skills requirements of the modern data-driven industries but the consequences of not training candidates in fundamental data skills and leadership skills to build collaborative AI projects can be even more damaging to the economy in the long run. Academia suffers from an acute shortage of expert faculty to train students in state-of-the-art AI theory and practical knowledge at scale. This burden of nurturing and creating AI talent does not rest solely with educational institutions. Industry needs to step up and actively contribute by sharing business data, a critical ingredient for building data-hungry supervised AI systems, and foster a vibrant and collaborative ecosystem by partnering with both academia and startups to raise awareness of the kind of challenging business problems that only AI can solve effectively.
To bridge the gap between industry requirements of AI talent and lack of industry- oriented AI education at universities, a number of edtech startups have stepped up. The majority of online edtech platforms focus on programming and coding skills, a key foundational skill to building AI systems. However, the pedagogical methods practised by most suffer from lack of imagination and creativity and do not innovate beyond offering the age-old offline classroom content via online platforms - the adage ‘old wine in a new bottle’ comes to mind.
AI is a multidisciplinary field that requires strong creative, scientific and problem solving abilities to come up with novel solutions to pressing business problems. The ability to innovate beyond open-source models and solutions is fundamental to building tailored customer-centric AI solutions that incorporate the unique business and cultural context of India.
If India is not able to keep pace with AI global superpowers like the USA and China, then not only is she at risk of lagging behind in the battle for tech supremacy but also faces the dire prospect of losing its emerging tech talent to countries that offer better opportunities to work at the cutting edge of AI. India is set to become the world’s youngest country with 64 percent of its population in the working age group, while western countries, China and Japan have an aging demographic. India must therefore implement policy changes, state-wide reskilling initiatives in cooperation with industry, academia and startups to reskill the nation’s youth in the latest digital and AI-first skills to steer India into the next decade as a leading digital economy.
Electronic means of communication have helped to eliminate time and distance barriers to sharing and broadcasting information. However, despite all its advantages, faster means of communication have also resulted in the extensive spread of misinformation. The world is currently going through the deadly COVID-19 pandemic and fake news regarding the disease, its cures, its prevention, and causes have been broadcast widely to millions of people. The spread of fake news and misinformation during such precarious times can have grave consequences leading to widespread panic and amplification of the threat of the pandemic itself.
As per a recent BBC report from August 2020, at least 800 people may have died around the world because of coronavirus-related misinformation in the first three months of this year. It is therefore of paramount importance to limit the spread of fake news and ensure that accurate knowledge is disseminated to the public.
In this blog, we explore the problem of fake news detection related to COVID-19 and describe our approach to tackle it using Natural Language Processing. This is based on our recent paper — ‘Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking’, accepted at the NLP for Internet Freedom Workshop, co-located with COLING2020.
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In recent years, the amount of data powering different industries, and their systems has been increasing exponentially. Majority of business information is stored in the form of relational databases that store, process, and retrieve data. Databases power information systems across multiple industries, for instance, consumer tech (e.g. orders, cancellations, refunds), supply chain (e.g. raw materials, stocks, vendors), healthcare (e.g. medical records), finance (e.g. financial business metrics), customer support, search engines, and much more.
It is imperative for modern data-driven companies to track the real-time state of its business in order to quickly understand and diagnose any emerging issues, trends, or anomalies in the data and take immediate corrective actions. This work is usually performed manually by business analysts who compose complex queries in declarative query languages like SQL to derive business insights stored in multiple tables. These results are typically processed in the form of charts or graphs to enable leadership teams to quickly visualize the results and facilitate data-driven decision making.
Although the most common SQL queries that address fundamental business metrics are predefined and incorporated in commercial products like PowerBi that power insights into business metrics, any new or follow-up business queries still need to be manually coded by the analysts. Such static interactions between database queries and consumption of the corresponding results require time-consuming manual intervention and result in slow feedback cycles. It is vastly more efficient to have non-technical business leaders directly interact with the analytics tables via natural language queries that abstract away the underlying SQL code.
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Copyright © 2022, Sundeep Teki
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