Tag Archives: faced


There were a few notable challenges my team and I faced during this project.

The first was securing buy-in across various stakeholder groups. As you can imagine, a project of this scope touched on nearly every department within the organization. We needed participation, collaboration, and compromise from people who didn’t initially see the value of this investment or understand how it would impact their day-to-day work. Gaining support took patience, empathy, and more than a few long meetings to discuss priorities, trade-offs, and potential benefits.

Another hurdle was managing expectations as requirements and timelines inevitably shifted. When working with new technologies, integrating complex systems, and coordinating among large teams, things rarely go exactly as planned. We had to balance the need for transparency when issues arose with preventing delays from spiraling out of control. Over-promising risked damaging credibility, but too many missed deadlines threatened support. Communications was key, as was accountability in putting fixes in place.

Data migration presented unique problems as well. Extracting, transforming, and transferring huge volumes of information from legacy databases while minimizing disruption to operations was a massive technical and logistical feat. We discovered numerous cases of corrupt, incomplete, or incorrectly structured records that required extensive preprocessing work. The amount of testing and retesting before “flipping the switch” on the new system was immense. Even with contingency plans, unplanned maintenance windows and bug fixes post-launch were to be expected.

Organizing and leading a distributed team across different regions and time zones also posed its own coordination difficulties. While cloud collaboration tools helped facilitate communication and project management, the lack of in-person interaction meant certain discussions were harder and delays more likely. Keeping everyone on the same page as tasks were handed off between locations took extra effort. Cultural differences in working styles and communication norms had to be understood and accommodated for productivity and morale.

Ensuring the reliability, performance, and cybersecurity of cloud services and infrastructure exceeded our expectations and industry standards was of paramount importance. We had stringent standards to meet, and anything less than perfect at go-live carried risks of a major credibility blow. Extensive load testing under real-world usage scenarios, third-party security audits, regular penetration testing, and simulated disaster recovery scenarios were all required. Even with diligent preparation, we knew post-launch support would need to be very robust.

Change management across boundaries, expectation management, successful data migration at scale, distributed team alignment, and guaranteed platform quality assurance were the primary challenges we had to solve iteratively throughout the project. It required meticulous planning, communication, testing, and the full commitment of every team member to get through each hurdle and progress towards our goals. With the right approaches and continued diligence, I believe we were able to overcome significant barriers and deliver value to the business in a secure, scalable way.


One of the major challenges faced during the implementation of food waste reduction strategies was changing public behavior and mindsets around food. For many years, most people have viewed excess food as unimportant and not given much thought to wasting it. Things like clearing one’s plate, over-ordering at restaurants, or throwing out old leftovers and expired foods were ingrained habits. Shifting such habitual behaviors requires a significant mindset change, which can be difficult to achieve. It requires sustained education campaigns to raise awareness of the issue and its impacts, as well as motivation for people to adjust their daily food-related routines and habits.

Another behavioral challenge is that reducing food waste often requires more planning and coordination within households. Things like meticulously planning out meals, sticking to grocery lists, adjusting portion sizes, and making better use of leftovers necessitates more effort and time compared to past habits. While improving skills like meal planning, it is an adjustment that not everyone finds easy to make. For families with both parents working long hours, seeking convenience is also an understandable priority, leaving little time or energy for meticulous waste-reduction efforts.

From a business and operations perspective, one challenge is the lack of reliable data on food waste amounts. Most organizations, whether food manufacturers, grocery retailers or food service companies, have historically not tracked the scale of food that gets wasted within their facilities and supply chains. Without robust baseline data, it is difficult to analyze root causes, identify priorities and set meaningful targets for improvement. Some have also been hesitant to publicly share waste data for risk of reputational damage. The lack of common measurement standards has made industry-wide benchmarking and goal setting a challenge.

On the policy front, the mixed competencies shared between different levels and departments of government have made coordinated action difficult. Food waste touches on the responsibilities of agriculture, environment, waste collection, business regulations, public awareness campaigns and more. There is sometimes lack of clarity on who should take the lead, and duplication or gaps can occur between different actors. The complexity with multiple stakeholders across many domains further impedes swift, aligned policy progress to drive systemic changes.

Even when strategies are set, enforcement is a big challenge especially related to food date labeling policies. Standardizing and simplifying date labels to distinguish between ‘Best Before’ – indicating quality rather than safety – and ‘Use By’ date is an important intervention. Inconsistent application of new labeling rules by some in the vast food industry has undermined the effectiveness of this policy change to reduce consumer confusion and subsequent waste. Stronger compliance mechanisms are needed.

From a technological standpoint, while innovative solutions are emerging, scaling these up to have meaningful impact requires extensive investments of time and capital. Food redistribution through apps needs to overcome challenges like adequate coverage, logistical issues in arranging pick ups, necessity of refrigerated transportation, and standardizing quality parameters of donor and recipient organizations. Similarly, food waste valorization is still at a nascent, experimental phase with challenges of developing financially viable business models at commercial scale. These solutions are also capital intensive to set up advanced processing facilities.

Even simple measures like home composting have faced adoption challenges due to requirements like space, installation efforts, maintenance skills and concerns over pests and smells. Compostable packaging is not universally available and green bins for food scrap collection are not scaled up widely in all geographies to make participation easy. Expanded waste collection infrastructure requires substantial capital allocations by local governments already facing budget constraints.

From a supply chain coordination perspective, a key challenge is data and technology integration across the long and complex path food takes from farms to processing units to transport networks to retailers to finally consumers. Lack of end-to-end visibility impedes root cause analysis of where and why waste is originating. It also restricts opportunities for collaborative optimization of inventory, ordering and demand planning practices to minimize food left unconsumed at any stage. Silos between different entities and lack of incentives for open data sharing have hampered integrated solutions.

Reducing food waste faces behavioral, operational, policy-related, technological, financial as well as supply chain coordination challenges. It requires multifaceted, long-term efforts spanning awareness drives, standardized measurement, supportive regulations, scaled infrastructure, collaborative innovation and adaptability to local conditions. The complexity of root causes necessitates system-wide cooperation between industry, governments, researchers and communities to achieve meaningful impact over time. While progress has been made, continued dedication of resources and coordination between different stakeholders remains important to sustain momentum in tackling this massive global issue.


Accrediting bodies play an important role in ensuring the quality of education being provided by institutions. They also face several challenges in discharging this responsibility effectively. Some of the key challenges faced by accreditors include:

Ensuring rigorous and objective standards – Developing standards and criteria that accurately reflect quality education is a difficult task. Standards need to be rigorous enough to differentiate high-quality programs from mediocre ones, but they also should not be too prescriptive. Getting this balance right is challenging. Different stakeholders also try to influence standards to suit their priorities. Maintaining objectivity and evidence-based standards requires constant effort.

Rapid pace of change in education – The higher education landscape is changing constantly with the rise of new pedagogies, learning technologies, competency-based models, online/blended learning etc. Keeping accreditation standards relevant and able to measure quality in this dynamic environment poses difficulties. Standards need frequent revision but the process is resource-intensive. Lagging standards can compromise the integrity of the accreditation system.

Resource constraints – Accreditation involves extensive evaluation processes including self-studies, site visits, review of submitted materials etc. But accreditors have limited financial and human resources to undertake rigorous evaluations of a growing number of institutions. Evaluating specialized/innovative programs requires domain expertise that may be scarce. Resource constraints can compromise the robustness and frequency of evaluations.

Conflicts of interest – Most accreditors are membership organizations wherein the institutions seeking accreditation are also member institutions that help fund the accreditor’s operations. This intermingling of roles can potentially compromise the independence and objectivity of accreditors. It challenges their ability to make fair and unbiased judgments, especially in cases of non-compliance. Managing conflicts of interest transparently is crucial yet complex.

Internationalization of higher education – With growing cross-border mobility of students and programs, the focus of accreditation is shifting to international/global aspects of quality. Evaluating learning outcomes, student experience, qualifications etc. in an international context, especially in a digital world, brings unique difficulties. Developing a shared understanding of quality standards across diverse education systems is an ongoing task.

Regulatory pressures – Accreditors face pressures from various sides – the institutions they oversee, students/families, the government and other stakeholders. Striking a balance and maintaining independence from these influential players is challenging, especially in an environment where higher education is heavily regulated. Regulatory shifts also impact accreditors who must quickly evolve to stay relevant and comply with mandates.

Technology disruptions – Emerging technologies are transforming teaching, learning and the structure of education programs themselves. Massive Open Online Courses (MOOCs), adaptive/personalized learning, online/blended models etc. pose regulatory dilemmas. Should standards apply equally to all formats? How can quality be judged remotely and across delivery modes? Evaluating novel education technologies objectively requires specialized expertise and frameworks – areas that are still evolving.

Data & transparency challenges – Stakeholders expect more transparency in decision-making and data-driven evaluations from accreditors. But developing robust quality assurance data systems, training peer reviewers to interpret data, publicly disclosing sensitive information are far from straightforward. Data quality, access issues and privacy regulations introduce new layers of complexity for accreditation processes.

Ensuring a credible, robust peer-review system – At the heart of the accreditation mechanism is the peer-review process. But recruiting and training qualified peers, managing conflicts of interest, achieving consistency across reviews and program types are ongoing struggles. With the growth in the number and type of accredited programs, relying on volunteer peers has limitations. Professionalizing peer review necessitates investments.

Responding to criticism about the value of accreditation – The value proposition of accreditation itself comes under growing scrutiny due to concerns around lack of differentiation, limited usefulness for students, and incentives of status quo. Accreditors must demonstrate how they enhance quality and accountability beyond minimum standards. Ongoing research and outcome-based evaluations help but face methodological issues. Criticism puts pressure on accreditors to institutionalize reforms.

While accreditation aims to act as a driver for continuous quality improvement, the system faces inherent challenges in objectively measuring and assuring diverse, evolving concepts of quality in globalized higher education. Meeting rising expectations amidst vast changes requires coordinated action and robust capacity from all stakeholders. Accreditors need ongoing support to maintain a balanced, evidence-based and independent approach.


Reddit has encountered a number of controversies since its founding in 2005 that have involved issues related to content posted by users, subreddit bans or restrictions, and how the company moderates content and policies. Some of the major controversies Reddit has faced include:

Jailbait Subreddit Controversy (2011) – One of the earliest major controversies involved the “r/jailbait” subreddit, which was created in 2008. The subreddit focused on sexualized images of underage girls and while it did not feature outright nudity, it was the subject of criticism for promoting the sexualization of minors. In 2011, violentacrez, a prolific Reddit user who had created numerous objectionable subreddits, was outed by Gawker which sparked wider attention to and criticism of r/jailbait. Reddit shut the subreddit down in October 2011 due to the controversy and negative press attention it brought.

Fat People Hate Ban (2015) – In 2015, Reddit banned several subreddits as part of an expansion of its harassment policy, including the “FatPeopleHate” subreddit which was devoted to hating fat individuals. The ban sparked significant controversy among some Reddit users who felt it violated principles of free speech. Supporters argued the subreddit promoted harassment, while critics saw it as banning a community for its views. The controversy led to protests on the platform and allegations Reddit was compromising its principles. It highlighted challenges around moderating offensive content.

The_Donald Controversies (2016-Present) – The prominent r/The_Donald pro-Trump subreddit has been an ongoing source of controversy since 2016 due to content and behavior of some users. Posts and comments perceived as racist, xenophobic, or threatening have led to accusations the subreddit fosters an atmosphere of hate. Moderators have also been accused of inconsistent enforcement of site-wide rules. The subreddit’s influence over Reddit politics remains controversial among some. Critics argue it receive preferential treatment due to its size, though the company denies giving it special treatment.

Pizzagate & Las Vegas Conspiracies (2016-2017) – In late 2016, a conspiracy theory dubbed “Pizzagate” emerged on Reddit where users posited a child sex ring was being operated in the basement of a D.C. pizzeria tied to prominent Democrats. It inspired a man to fire an assault rifle in the restaurant. Reddit eventually banned the Pizzagate subreddit, but the site still struggle with tackling the spread of disinformation and conspiracy theories on platforms. A similar issue emerged after the 2017 Las Vegas mass shooting when Reddit users circulated unfounded conspiracy theories about the motive.

T_D Encourages Violence Posts (2019) – In June 2019, Reddit came under criticism after users found comments on The_Donald like “keep your rifle by your side” and “God I hope so” in response to comments about civil war. The controversy increased pressure on Reddit to more consistently enforce policies against content that promotes harm. However, T_D remained active at the time.

Anti-Evil Actions Under Scrutiny (2020) – Reddit’s “Anti-Evil Operations” team, which aims to reduce harm on the site, came under scrutiny in 2020 for allegedly uneven enforcement. Several left-leaning political subreddits like ChapoTrapHouse were banned that year despite not directly calling for violence, fueling allegations of political bias. The bans triggered more debate around how Reddit enforced vague rules regarding harmful behaviors and hate.

WallStreetBets Controversies (2021) – The surge in popularity of the r/WallStreetBets subreddit during the “GameStop short squeeze” attracted unprecedented mainstream attention to Reddit in 2021 but also controversies. Some questioned if social media hype fueled a “pump and dump” stock manipulation scheme. When moderators implemented temporary content restrictions to scale with rapid growth, it also triggered a backlash and allegations of censorship. The episode highlighted challenges with viral crowdsourced investment campaigns on digital platforms.

Anti-Vax Misinformation (2021-Present) – More recently, Reddit has faced criticism for allegedly not doing enough to curb the spread of COVID-19 anti-vaccine misinformation on its platform. Studies found its top COVID-19 misinformation subreddits have hundreds of thousands of subscribers. While Reddit insists it takes action against rules-breaking posts, critics argue more should be done to limit the reach of health misinformation during a public health crisis when lives are at stake. How to balance open discussion and limiting harmful untruths remains an ongoing challenge.

As this brief retrospective highlights, controversies have dogged Reddit throughout its existence largely due to the scale of user-generated content it hosts and the difficult balancing act of moderating discussions around contentious or objectionable topics. While the company maintains it aims to uphold principles of open discussion, it is also pressured to curb the spread of misinformation, conspiracies and behaviors that could inspire real-world harm. Striking the right approach remains an ongoing work-in-progress, suggesting Reddit and other platforms may continually face controversies as societal debates evolve.


One of the biggest challenges is obtaining a large amount of high-quality labeled data for training deep learning models. Deep learning algorithms require vast amounts of data, often in the range of millions or billions of samples, in order to learn meaningful patterns and generalize well to new examples. Collecting and labeling large datasets can be an extremely time-consuming and expensive process, sometimes requiring human experts and annotators. The quality and completeness of the data labels is also important. Noise or ambiguity in the labels can negatively impact a model’s performance.

Securing adequate computing resources for training complex deep learning models can pose difficulties. Training large state-of-the-art models from scratch requires high-performance GPUs or GPU clusters to achieve reasonable training times. This level of hardware can be costly, and may not always be accessible to students or those without industry backing. Alternatives like cloud-based GPU instances or smaller models/datasets have to be considered. Organizing and managing distributed training across multiple machines also introduces technical challenges.

Choosing the right deep learning architecture and techniques for the given problem/domain is not always straightforward. There are many different model types (CNNs, RNNs, Transformers etc.), optimization algorithms, regularization methods and hyperparameters to experiment with. Picking the most suitable approach requires a thorough understanding of the problem as well as deep learning best practices. Significant trial-and-error may be needed during development. Transfer learning from pretrained models helps but requires domain expertise.

Overfitting, where models perform very well on the training data but fail to generalize, is a common issue due to limited data. Regularization methods and techniques like dropout, batch normalization, early stopping, data augmentation must be carefully applied and tuned. Detecting and addressing overfitting risks requiring analysis of validation/test metrics vs training metrics over multiple experiments.

Evaluating and interpreting deep learning models can be non-trivial, especially for complex tasks. Traditional machine learning metrics like accuracy may not fully capture performance. Domain-specific evaluation protocols have to be followed. Understanding feature representations and decision boundaries learned by the models helps debugging but is challenging. Bias and fairness issues also require attention depending on the application domain.

Integrating deep learning models into applications and production environments involves additional non-technical challenges. Aspects like model deployment, data/security integration, ensuring responsiveness under load, continuous monitoring, documentation and versioning, assisting non-technical users require soft skills and a software engineering mindset on top of ML expertise. Agreeing on success criteria with stakeholders and reporting results is another task.

Documentation of the entire project from data collection to model architecture to training process to evaluation takes meticulous effort. This not only helps future work but is essential in capstone reports/theses to gain appropriate credit. A clear articulation of limitations, assumptions, future work is needed along with code/result reproducibility. Adhering to research standards of ethical AI and data privacy principles is also important.

While deep learning libraries and frameworks help development, they require proficiency which takes time to gain. Troubleshooting platform/library specific bugs introduces delays. Software engineering best practices around modularity, testing, configuration management become critical as projects grow in scope and complexity. Adhering to strict schedules in academic capstones with the above technical challenges can be stressful. Deep learning projects involve an interdisciplinary skillset beyond conventional disciplines.

Deep learning capstone projects, while providing valuable hands-on experience, can pose significant challenges in areas like data acquisition and labeling, computing resource requirements, model architecture selection, overfitting avoidance, performance evaluation, productionizing models, software engineering practices, documentation and communication of results while following research standards and schedules. Careful planning, experimentation, and holistic consideration of non-technical aspects is needed to successfully complete such ambitious deep learning projects.