To add:

  • large scale systems, feedback, difficult
  • ambiguity, strategic
  • empathetic
  • Roadmapping for project and vision
  • Management )
    • Frameworks RICE, SWAT
    • Explainibility of solution and the ROI
    • Prioritization
    • Absorb leadership shit
    • Coaching metrics
    • High/low performer
    • escalations make sure you are both aware/aligned
      • joint incentives
      • trust
      • company goals
    • have issues ready that exist on the team

General Tips

  • Don’t forget to prepare answers to standard interview questions. Hiring managers want to know how you’ve conquered challenges in the past, what your long-term plans are for your career, and whether you’ll fit into the corporate culture.
  • Get ready for a few curveball questions. Many interviewers like to ask difficult questions of all their prospective hires. They may especially expect management candidates to think quickly on their feet and stay cool even when the conversation veers in an unexpected direction.
  • Demonstrate that you’re management material during the interview. Seek input or clarification as needed, remain positive and focused on the problem (or interview question), and look for opportunities to tell stories that demonstrate your successes.
  • Remember that as a manager, you’ll set the tone for your team. If you don’t share the organization’s values, goals, and culture, you won’t be able to lead effectively.
  • Actively listening to your team members, resolving conflict and boosting productivity are the three pillars of being an effective manager.
  • The following tools available to you in your toolkit can help you navigate managerial scenarios:
    • Define the team’s strategy and execution using OKRs and KPIs to chart out the overall plan for the year which should convey the short term and long term north-star of the group. This plan can include tasks with priorities which are grouped into milestones. Partner up with PMs to do sprint planning, decide goals for the upcoming sprint and ensure sprint stories and their constituent tasks are on track. Involve your ICs and TLs in crafting your vision.
    • Load-balance among team members if a project has the slightest chance of missing deadlines and losing track.
    • Use SWOT analysis to navigate situations that involve making decisions that can have significant near-term and/or far-term effects. Analyze your options and weigh them against each other. Engage with meticulousness especially if the decision can have repercussions or a ripple effect.
    • Use the STAR method to answer behavioral questions by setting up the stage with a Situation, Task, Action and Result (STAR). An additional dimension can be stacked on top of the STAR method leading to the STARR method where the latter R stands for reflection. Reflecting upon the how the situation being discussed panned out (especially in scenarios where the desired outcome wasn’t achieved in which case conducting port-mortem analysis is a good idea) to identify learnings from the episode.
    • Establish standard processes if they don’t exist or need revamping within the company. Use (Agile, Scrum) for sprint planning, Kanban board for task tracking, JIRA for tickets, Quip for note-taking, Slack for team communication etc.
  • A manager should be able to set the direction for the team and help align the team’s objectives to high-level business outcomes.
  • A manager is their team’s biggest cheerleader, the captain of their ship and among the company’s tastemakers — in a way, they represent the company. As such, they support their employees, ensure their growth and are fully committed to their employees’ success.
  • Team-building is a major responsibility for a manager, so expect questions about your ability to recruit great managers, build cohesive teams, and inspire groups when times are tough. Think about how you approach career growth conversations and times when you’ve helped someone achieve their goals.
  • Leaders/managers work tirelessly to earn/gain the trust, respect and confidence of their employees.
  • While management interviews can often feel intimidating, reviewing these questions and crafting thoughtful responses rooted in your experience with leadership can boost your interview performance. Remember to highlight a receptive, group-focused mindset and how you feel that your skillset best positions you for this specific job.
  • If I’ve learned one thing, it’s the power of telling a good story to show the talent of a current or potential manager through action, instead of asking potential employers to take your word for it.
  • Common sayings:
    • Communication is key.
    • Listening is probably one of the most under-appreciated traits of a successful manager.
    • Crticize in private, but praise in public.
    • Never underestimate the importance of developing and maintaining good relationships with other teams.
    • People make the team. People should be a company’s biggest investment.
    • A great manager should (i) prioritize tasks that show merit based on elementary/preliminary data, (ii) make high-velocity decisions, and (ii) know which decisions are one-way vs. two-way door decisions (some decisions require depth of thoughts and understanding repurcussions while others do not)
    • I haven’t failed a 1000 times — the light bulb was an invention with a 1000 steps.

Make the Best Impression

  • The best way to make a great impression in a management interview is to demonstrate your confidence and competency in leading others, while at the same time expressing your enthusiasm for the company you are applying to.

Research the company

  • When you’ve done your research of the employer and have honed your “sales pitch” (“These are the reasons why you should hire me as your next manager …”), you’ll be ready to prove to your interviewers that you’re the perfect candidate for the job. Aim to learn about the organization’s mission and goals so that you can frame your answers accordingly.

Highlight your ability to lead

  • Before the interview, create a list of three to four specific experiences that demonstrate your ability to make effective decisions, delegate work to team members, motivate people and develop a team. Remember that examples of your team’s success are just as valuable as examples of your own.

Giving the Best Answer

  • Show your work: You’ll want to walk through your process and the strategies you used.
  • Don’t forget the big picture: If tackling this challenge changed your work flow or work style, or there was a big overarching lesson, mention it. And, don’t forget to mention the end result.
  • Keep it simple: Try not to get bogged down in jargon or company-specific workflows and terminologies. Your goal is to share the challenge—and your resolution—in easy-to-follow language.

What Not to Say

  • Don’t place blame: Did a challenge arise because of your supervisor’s incompetence, or a co-worker’s carelessness? This is not the right time to mention that. Avoid pointing fingers. Keep your description of the challenge neutral in tone.
  • Stay away from insignificant occurrences: ideally, you’ll highlight a situation that is relevant, such as a challenge that many companies face. That way, the interviewer will be able to visualize your on-the-job performance.

Research the company

  • Every company has its own preferred management style, and knowing how your potential future employer likes things done is a huge advantage during your interview. Find out about the company’s mission, values, and big-picture goals.
  • If you can identify specific issues that your potential department is facing, you’ll also have a much easier time selling yourself as the perfect solution.
  • Develop good leadership examples. Leadership isn’t reserved for managers only. Think about times when you’ve stepped up to lead a project, delegate tasks to coworkers, or motivated a team.
  • If you can attach winning results to these stories, you’ll be in great shape.

Prepare for curveballs

  • Companies like to know that those in supervisory positions won’t sweat when things get complicated. More important than answering curveball questions correctly is coming across as cool and confident.
  • Be sure to relax before the interview and don’t give quick answers to questions when you don’t know the answer. Instead, think through the problem to help the hiring manager see your thought process and approach.

Dress well

  • Dress for the job you want, not for the job you have!
  • At many companies, managers are expected to look as well as act the part. Make your that your interview attire is impeccable and professional.
  • Read up on great interview attire to help sell yourself as a well-groomed powerhouse of the business world.

Listen!

  • Be receptive and listen to your employees. Understand each report’s areas of proficiency. Use the feedback from them and senior management as fuel to hone your skills.

You Should Be Ready to Tell Stories

  • Once, while preparing a mid-level manager to interview for a managing director role, the candidate was asked, “Which acts of leadership are you most proud of?” His first instinct was to answer generically: “We’ll, we’ve met almost every deadline for three years in a row.” But when he was pressed for specifics about how he’d succeeded as a leader of people, she had a much more compelling and informative answer:
    • “I once had this really talented direct report who was always late. Timeliness is one of our company’s core values, and the employee and I discussed and tried to troubleshoot the issue many times. He would improve, maybe for a week. Senior management noticed when he arrived late twice to company-wide meetings. I didn’t know what to do. The thought of firing him really upset me, because he was talented. Then, I had an idea. I asked him to take charge of the morning staff meetings: to review and organize the agendas the night before, introduce the main topic and structure, and manage the time at the meeting. It was risky to reward someone who wasn’t following the rules, but frankly, no one else wanted the job. He embraced it and showed up on time religiously, knowing that the team was depending on him.”
    • This manager’s story revealed her ingenuity in dealing with people, playing to their strengths, problem-solving, and working with a team. The ability to convey so many details to your prospective employers is why storytelling is the most powerful tool in your interview kit.
  • As you prepare for a management interview, mine your work experience for management and leadership wins. Even if you haven’t been a manager before, you’ve still demonstrated leadership in training others, managing projects, motivating colleagues, contributing ideas, thinking strategically, and holding others accountable. Take some time to reflect on your work experience and jot down significant moments when you led. These are the basis for your stories, which should reveal one or all of the following:
    • A time when you influenced and encouraged others (and how you approach influencing and encouraging others in general).
    • A time when you and a team were successful and what your contribution was.
    • A time when your problem-solving and/or delegating skills directly impacted a coworker, team, or initiative.

Make Sure You Highlight the Right Skills

  • Consider what skills are required for the job you’re interviewing for and especially focus on the stories that show you developing or using these skills. Lay out your stories in a coherent way by defining the problem, explaining how you arrived at a solution, and describing how you implemented it. Once you’ve collected a handful of tales, you’ll be able to easily modify them to answer different interview questions in a way that demonstrates your management and leadership chops.
  • Remember that management across most functions and roles largely involves prioritizing and delegating, time management, problem-solving, and organization. Be sure to showcase those skills in your stories.
  • And even if a company is extremely focused on having their managers drive the productivity of their staff to “hit the numbers,” you’ll still need soft skills, such as emotional intelligence or interpersonal skills, to manage and inspire your team to get there. Empathy and sensitivity are increasingly valued workplace traits. Show your capacity for them.

Confidence Is Good, But Don’t Over-Rehearse

  • Thorough preparation will help you feel confident and confidence will help the interviewers see you as a leader. But be careful not to over-rehearse exactly how you will tell your stories. You shouldn’t present as overly polished in your interview. The company wants to understand your philosophy and leadership style—not be presented with answers learned by rote. A hiring team is looking for managers and leaders who are relatable and can think on their feet. And rehearsed speeches can come across as inauthentic.

Diversity, Equity, and Inclusion Are Likely to Come Up

  • In addition to developing their interpersonal skills, managers must be familiar with DEI principles and resources and why they matter in the workplace. Be sure to go over these and be prepared to speak about diversity, equity, and inclusion in an interview.

Answering how-did-you-handle-a-challenge questions

  • Your interviewer may ask you to consider how you would handle a theoretical situation. Always remember to ground your responses with examples of real situations that speak to your previous accomplishments as a manager.
  • “How-did-you-handle-such-a-challenge” questions are perhaps the most common in the behavioral category. Follow this three-step strategy to formulate an effective response:
    • Step 1: Recall a challenge that was significant, but one that you consider a success. Most importantly, you want to be able to discuss a real professional challenge or problem, not an arbitrary or annoying occurrence. You also want to be able to define how you met the challenge successfully. If possible, mention a challenge most relevant to the role you’re applying to. In your answer, you’ll want to set up the challenge clearly and succinctly.
    • Step 2: Don’t just say what you did—explain how you did it.
      • The employer is interested in learning your approach to a challenge, including the actions you took and your thought process. Don’t skip ahead to the end result. Use specifics to describe what you did to contribute to the solution.
    • Step 3: Emphasize the outcome and what you learned from it.
      • Employers want to hire individuals who can turn challenges into opportunities. When brainstorming an answer, think about ways to emphasize how you made the most of a difficult time. Of course, in the real world, it’s not possible to wave a magic wand and transform every difficulty into a grand success. It is possible to learn from your hardships, and then apply what you learned to future challenges. Make sure to express your takeaways and how challenges have helped you grow.

Readings

  • “Turn the Ship Around: A True Story of Turning Followers into Leaders” by L. David Marquet
  • “The First-Time Manager” by Jim McCormick
  • “Managing Humans: Biting and Humorous Tales of a Software Engineering Manager” by Michael Lopp
  • Interviewing at Amazon — Leadership Principles

Questions

How do you delegate responsibility for an assignment? Who do you choose? What and how do you delegate, and what do you monitor and follow up?

  • It is one of my responsibilities as a team lead in my current role at Apple to delegate tasks and track them to completion.
  • The way I go about delegating tasks is that I (i) figure out the proficiency level of my employees across a range of skills, (ii) take employee interests and career goals into account, and (iii) how well they would tag-team with other potential assignees of this task.
  • Delegating responsibility is a blend of figuring out the intersection in the Venn diagram of these three areas — best overlap between business needs and employee interests and skill-sets.
  • Important to setup intermediate checkpoints and track progress. If there are blockers and help is needed, step in and make sure to load-balance appropriately.

What do you do to support employees?

  • Onboarding using training programs
  • Career conversations
  • Ask them what they want to work on. Tie it in with your vision.

Why do you want to be a manager?

  • From my experience as a team lead/manager in a couple of teams at Apple over the past 6 years, I have come to realize that empowering my people, seeing them grow personally and professionally while at the same time hitting milestones and launching successful products together is what I look forward to.
  • Nothing gives me more joy than seeing my reports succeed in their careers; doing my part in writing their success story while ensuring that we’re building strong relationships as teammates first and manager-employee second.
  • being the captain of the ship,

An example of how you helped coach or mentor someone. What improvements did you see in the person’s knowledge or skills?

An example of a time when you were able to demonstrate excellent listening skills. What was the situation and outcome?

In your experience, what is the key to developing a good team?

  • It is said that “people make the company”. People should be a company’s biggest investment. Our ideas, projects, goals and endeavors, are only going to as good as the skills, passion and drive of the people powering them under the hood.
  • It is important to invest the time and effort into selecting employees with the right blend of technical and communication skills to ensure that that not only we keep the ship afloat, but also raising the bar with every project, with every endevaor soar higher and higher. Investments in people go a long way and pay off in the long run.
  • To develop a cohesive team, it is important to build mutual trust, respect, and confidence which are the three pillars of a great team.

How do you deal with ambiguity?

It is important to gather as much information about the situation as possible, be it a problem at work, a project we’re working on etc. Consult with subject matter experts to get their ideas and thoughts on what would be the right direction forward. Consult with folks who have gone through a similar issue/situation to learn from their experiences.

What have you found to be the best way to monitor the performance of your work and/or the work of others? Share a time when you had to take corrective action.

1:1s serve as an opportunity to gauge the overall health of the team and have a pulse on the team’s day-to-day functionings. Helps build employee rapport and enables providing constructive feedback privately, if warranted. Schedule regular 1:1s Checkpoint progress by setting up intermediate milestones to check in on project status. Discuss blockers and figure out how you could help them. Ensure important tasks are prioritized. Guide the team execution to deliver on the vision. Hold the team to a high execution bar. Ask your report occasionally if their learnings from the job match their expectations. Also, good idea to check in on them at a personal level. How are they doing personally? Empathy is an essential trait for being a good manager and goes a long way in building loyal reports. Lead with empathy, compassion and emotional intelligence (EQ). Partner up with PMs to do sprint planning, decide goals for the upcoming sprint and ensure sprint stories and their constituent tasks are on track.

Is Diversity, Equity, and Inclusion important? What does it bring about to the workplace?

Diversity adds color the group’s thought process. More diverse the group, more varied and complementary the ideas. I’ve been in brainstorming sessions with teams that are all-male belonging to a particular ethnicity vs. discussions with a diverse set of individuals from varied cultures, genders, race, skillset (technical/non-technical) and background and I have always found that the latter group had much more varied and complementary ideas and a richer mindset that helped us think and plan better. These colorful ideas that a diverse group brings to the table in turn propels the success of our projects.

How do you approach career growth conversations? / How have you carried out career development for your employees

As a team lead in my current team at Apple, I am responsible for supporting my employees and ensuring their success. A manager is the team’s biggest cheerleader. As such, career conversations form a big part of contributing to the success of an employee and playing my part in writing the success stories of my employees is important as the team’s manager. For initial onboarding, I prepared learning material for the team to help them learn the ropes. Once they’ve settled in, I setup career conversation meetings with my employees every couple of months and ask the employee to come up with a list of skills, projects and technical/non-technical areas they’re interested in exploring beyond their usual projects. I ask them to makes these notes before our meeting. Understand the employee’s interest in career growth as an ICT or Manager, chalk out a career growth plan. Based on an overlap of their interests and the needs of the team, we agree on pursuing certain projects. In parallel, I do my homework in identifying relevant projects that we need to focus on which align with the employee’s interests, and I suggest those to the employee. Suggest resources for reading, learning new skills and growing as a professional. As the next step, we flesh out the plan by chalking out a list of tasks and priorities to execute on these projects. This not only gets them to hone their technical skills but also gets them some valuable exposure to new cross-functional partner teams working on these projects and senior management visiblity. This has also has the by-product of helping bring about a career boost for my employees by making them an easier candidate for a promotion when the time comes.

How do you ensure that tasks get delivered successfully?

As the captain of the ship, I’m responsible for the success of several projects within the team. There’s this space of trusting people and letting them do the job you hired them to do vs. this other space of being a micromanager and constantly interfering with people’s day-to-day plans and rubbing people the wrong way. It is important to maintain a balance here and know when to roll up your sleeves vs. when do you let them do it. Operate in the area of intersection within this Venn diagram. Depending on the competency level of the employee, grant autonomy appropriately. It is also important to know that you haven’t abdicated responsibility or relinquished control for the project and stay connected to the details and audit when necessary, especially if metrics and intuition/anecdoates differ.

How do you assess priorities? How do you then assign them?

Sometimes there are a number of projects taking place at once. Hiring managers know that without clearly agreed-upon priorities, a workforce can become split and frustrated, waiting for key pieces of work in order to be able to complete their own tasks and meet deadlines. So how have you—or how would you—ensure that members of your team know how to organize their day and what to work on first? For this question, you can share a story about a time you needed to establish priorities for yourself at a past job. How did you decide which tasks to attend to first? If you’ve led a team or been a project manager, what criteria have you used to determine priorities for the team and how did you communicate them? Make sure the story is representative of your leadership style: For example, do you tend to let each worker figure it out on their own or with each other first and come to you with questions or do you step in from the get-go? Does it depend on the employee or situation? And you can add specifics: Priorities need to align with the overall direction of the business unit and the OKRs set at the beginning of the year. They also need to gel well with the company’s leadership principles. Priorities can range from P0 to P3, where P0 means showstopper, P1 means expected, P2 means important and P3 means nice to have. Track tasks in stories with reasonable acceptance criteria/outcomes. Track stories in sprint planning meetings. Track sprints in milestone meetings. Track milestones in OKRs. Use software (Agile, Scrum) for project management. Reinforce priorities during weekly team meetings or daily stand-ups.

What would you describe as an effective staff meeting? Ineffective?

An ideal staff meeting involves cross-pollination of ideas. A blocker may have already been solved by another teammate, spending cycles on reinventing the wheel isn’t effective so it is important to let ideas permeate across team members. To this end, it is important to have blockers being discussed and the key highlights and lowlights of each team member.

Vini background

Amazon

  • Data prep: SageMaker has an inbuilt Jupyter notebook with accessibility to numpy and pandas
    • EC2 p3 or p4, hopper 100
      • remove outlier, duplicates, split test and train data, normalize
      • sklearn.model_selection for train_test split
      • create a panda dataframe
    • Feature engineering: changing labels etc, SageMaker Autopilot: It can automatically perform feature engineering tasks, such as data cleaning, handling missing values, feature encoding, and feature selection. You can leverage Autopilot to automate the feature engineering process and let the tool generate optimized features for your models.
      • Autopilot automatically explores the data and generates a set of feature transformations, including creating new features, selecting relevant features, and applying appropriate feature encoding techniques.
      • During the feature engineering phase, Autopilot employs statistical analysis, data exploration, and model performance evaluation to determine the effectiveness of the generated features. It selects the most informative features that contribute to improved model performance.
  • Model training:
    • SageMaker built in classification, regression, clustering algorithms via importing image_uris
    • Hyperparameter optimization- Sagemaker uses Bayesian optimizations or random search
  • Deploy:
    • Creates endpoints for you
  • Eval:
    • Offline: Batch transform: apply machine learning model on a number or records at the same time and during training you’ll have both feature and target values but at serve you’ll only have feature.
      • To test, you can create a new dataframe and drop the class column
      • evaluation metrics comparing models output with ground truth
    • Online: A/B testing where you can specify the routing rules
    • Metrics
  • Demo: StreamLit and HuggingFace has gradio
  • Libraries used:GNNs -> DGL, PyTorch Geometric
  • MLOps
    • Data drift
      • Feedback Loop and User Signals: User feedback and signals, such as explicit ratings, likes/dislikes, or user interactions, play a crucial role in addressing data drift. Incorporating user feedback into the recommendation system allows for personalized adjustments and helps capture changes in user preferences over time. User feedback can be used to retrain the model, update recommendations, and fine-tune the system based on the most recent user interactions.
      • To avoid model drift, regularly monitor and evaluate the performance of machine learning models, establish a data quality assurance process, retrain the model on updated data, implement feedback loops and user testing, and continuously evaluate the model’s performance against business metrics using monitoring tools.
      • Online Learning: Online learning is a technique where the model is continuously updated with new data as it becomes available. Instead of training the model from scratch, online learning algorithms update the existing model’s weights incrementally using new data points. This allows the model to adapt to changing patterns and handle data drift in real-time. Transfer Learning: Transfer learning involves leveraging knowledge from a pre-trained model and fine-tuning it using new data. The pre-trained model, trained on a large dataset, captures general patterns and features. By initializing the model with these pre-trained weights and fine-tuning it using the new data, the model can quickly adapt to the changing data distribution and mitigate the impact of data drift.
    • Latency
      • Model Quantization: Model quantization is a technique that reduces the precision of the model’s weights and activations. By representing the model with lower bit precision (e.g., from 32-bit floating-point to 8-bit integers), the memory footprint and computational requirements of the model are reduced. This can lead to faster inference and lower latency.
  • Model Pruning: Model pruning involves removing unnecessary or redundant parameters from the model. By pruning unimportant connections or reducing the size of the model, the number of computations required during inference is reduced, resulting in faster inference times. Pruned models can be retrained or fine-tuned to regain performance while maintaining lower latency.
  • Model Parallelism: Model parallelism involves splitting the model across multiple devices or processing units to perform parallel computations. This technique is particularly useful when the model has a large number of parameters or complex architectures. By dividing the model’s computations across multiple resources, the inference time can be significantly reduced, leading to lower latency.
  • Model Distillation: Model distillation is a process where a large, complex model (teacher model) is used to train a smaller, more efficient model (student model). The student model is trained to mimic the behavior and predictions of the teacher model. The distilled model is typically smaller and faster to compute, resulting in lower latency during inference.
  • Early Exit Strategies: Early exit strategies involve adding decision points within the model to make predictions before the entire model has completed its computations. For example, in a deep neural network, intermediate layers can be designed to provide predictions based on partial input information. This allows for faster responses, especially when certain predictions can be made with high confidence early in the inference process.
  • Model Caching: Model caching involves storing precomputed results or intermediate representations for commonly encountered inputs. When a similar input is encountered again, the cached results can be retrieved instead of performing the entire model computation. This can greatly reduce the inference time and improve overall latency for frequently accessed or recurring queries.

  • Productionize/ Serve: need to serialize the data

  • Personalization and User History: Storing recommendation data in a database allows the system to maintain a history of user preferences and interactions. This historical data can be used to further personalize recommendations, analyze user behavior, and provide insights for improving the recommendation algorithms.
  • Data Analysis and Reporting: The stored recommendation data can be used for post-processing, data analysis, and generating reports. By analyzing the stored recommendations, the system can gain insights into user preferences, item popularity, recommendation effectiveness, and other relevant metrics.
  • A/B Testing and Experimentation: Storing the inference output allows for easy integration with A/B testing frameworks. Different recommendation algorithms or variants can be compared by storing their outputs in the database and analyzing user interactions and feedback. This data can help measure the impact and effectiveness of different algorithms or features.

Data Integration: Amazon Music can integrate data from multiple sources, including user interactions and behavior on Amazon.com, such as product views, purchases, and user preferences, as well as user engagement and preferences from Prime Videos. This data can provide valuable insights into user preferences, interests, and browsing behavior. Data Preprocessing: The collected data is preprocessed and prepared for training and inference. This involves cleaning the data, handling missing values, transforming and encoding features, and merging relevant information from different sources. Data preprocessing ensures the data is in a suitable format for training the recommendation model. Training Phase: During the training phase, the combined dataset is used to train a music recommendation model. Various machine learning algorithms, such as collaborative filtering, content-based filtering, or hybrid approaches, can be applied to learn patterns and relationships from the training data. The model learns to understand user preferences and generate recommendations based on historical interactions and behavior. Inference Phase: In the inference phase, when a user interacts with Amazon Music, the recommendation system takes into account the user’s historical data, preferences, and contextual information. This can include their browsing history, previous music interactions, purchase history, and potentially their viewing habits from Prime Videos. The system processes this information and applies the trained recommendation model to generate personalized music recommendations in real-time. Post-Processing and Presentation: The generated recommendations may undergo post-processing steps to enhance their quality and relevance. Techniques such as filtering, ranking, and diversification can be applied to refine the recommendations and ensure they align with the user’s preferences and current context. The final set of recommendations is then presented to the user through the Amazon Music app or website, where users can explore, listen to, and interact with the recommended music. Continuous Learning and Improvement: The recommendation system in Amazon Music is designed to continuously learn and improve over time. User feedback, explicit ratings, and implicit feedback (such as skip rates or time spent listening) can be collected to gather information on the user’s satisfaction with the recommendations. This feedback is used to refine the models, update the recommendations, and adapt to changing user preferences and trends.

Projects:

  • Revert to the global model which is usually the popular recommendation
    • Next Best Action: Amazon Music can use machine learning techniques to predict and suggest the next best action for users. This could include recommending songs or playlists based on the user’s listening history, preferences, and contextual information. The platform can leverage user behavior data, such as play history, skip rates, and liked/disliked songs, along with other factors like time of day, user location, or device context, to generate personalized recommendations that align with the user’s current interests.
    • Cold Start: Cold start refers to the challenge of providing recommendations to new users or items with limited historical data. To address this, Amazon Music can employ various strategies. For new users, the platform can start with popular or trending songs and gradually gather user feedback to personalize recommendations. For new items, the system can use content-based recommendations, analyzing attributes like genre, artist, or album information to suggest similar items to users. Collaborative filtering techniques, which leverage similarities between users or items, can also be employed to make relevant recommendations despite limited data.
    • Warm Start: Warm start refers to the situation where some user or item data is available, but it may not be sufficient for accurate recommendations. In this case, Amazon Music can utilize hybrid approaches that combine collaborative filtering, content-based filtering, and personalized ranking algorithms. By blending different recommendation techniques, the system can leverage the available data to provide more accurate and diverse recommendations to users.
    • Item-to-Item Recommendations: Item-to-item recommendations involve suggesting similar or related items to users based on their current selection. Amazon Music can utilize item-to-item collaborative filtering to recommend songs or artists that are similar to the ones the user is currently enjoying. By leveraging historical user-item interaction data, the system can identify patterns and associations between items, allowing it to suggest relevant and complementary music choices.

Data Integration: The first step would be to integrate the data from Amazon Music and Amazon retail. This involves merging the relevant information, such as item metadata, user preferences, purchase history, and browsing behavior, to create a unified dataset. Preprocessing and Feature Extraction: The integrated dataset would then undergo preprocessing and feature extraction to extract meaningful features from the combined data. This can involve techniques like natural language processing (NLP) to analyze item descriptions or text mining to identify relevant keywords or attributes. Similarity Calculation: Once the features are extracted, similarity measures are calculated to determine the similarity between items. Various techniques can be employed, such as cosine similarity, Euclidean distance, or Jaccard similarity, depending on the nature of the data and the desired recommendation granularity. Recommendation Generation: Based on the calculated item similarities, the system can generate item-to-item recommendations. For a given item that a user is currently interacting with, the system would identify similar items from the combined dataset using the precomputed similarities. The most similar items would then be recommended to the user. Ranking and Personalization: To further enhance the recommendations, the system can consider additional factors like user preferences, historical interactions, and contextual information. Personalization techniques, such as collaborative filtering, can be employed to tailor the recommendations based on the individual user’s behavior and preferences. Evaluation and Iteration: The recommendation system’s performance would be evaluated using relevant metrics, such as precision, recall, or mean average precision, by comparing the recommended items to the actual user interactions and feedback. The system would then undergo iterations and refinements based on the evaluation results to continuously improve the quality and relevance of the recommendations.

Engineering push notifications

NuAIg

  • Data prep: H20.ai, Apache Spark
  • Model trianing:
  • Eval:
    • Offline
    • Online
    • Metrics
  • Demo:
  • Libraries used
  • MLOps
    • Data drift

Oracle

  • Data prep:
  • Model trianing:
  • Eval:
    • Offline
    • Online
    • Metrics
  • Demo:
  • Libraries used
  • MLOps
    • Data drift

Research

iReason- Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretability:

  • Causality is common sense knowledge known to humans but not available in input of the model
  • Word embeddings, its easy to have textual context not so easy to have image context
  • The idea behind this project was to create a framework that will generate common sense knowledge by using videos and text modalities.
  • Early fusion at the feature level so there are more correlation between the video and text
  • Video input -> detects frame in order -> detects objects (girl,dog) -> encodes to discern events (text) -> spits out a causality
  • Classification model so we used cross entropy loss
  • Since each event in I1 can cause multiple events in I2, we evaluate different causality extraction models with ranking based evaluation metrics
  • Used a pretrained activity model
  • Task is to input the model with videos and images and identify if there is a causal relationship
  • Classification model, used cross entropy loss
  • Used BERT to encode textual representation of events
  • Created a multimodal framework that utilized the video and text modality to generate commonsense knowledge using BERT and GPT-2 architectures with better recall than prior state of the art. This led to a publication.

Few-shot Multimodal Multitask Multilingual Learning

Counter Turing Test

  • Perplexity, watermarking

Hallucination detection and mitigation for LLMs

  • intrinsic, extrinsic hallucination, positive, negative hallucination

    Conflator: Codemixing language modeling

  • Relative positional encoding for language mixing where we experiment with different encoding mechanisms such as sinusoidal, relative and rotary
  • We run sentiment analysis and machine translation as our two tasks

Stanford