Really nice and detailed course, however, no one is reviewing my work/assignment and my subscription is going to end!

This course module guides learners through developing an end-to-end machine learning (ML) project, using "Intelligent Inventory Replenishment Forecasting for Offline Retail Stores" as a hands-on case study. It centers on translating real-world business pain points into actionable ML solutions while aligning with core project management frameworks like CRISP-DM, equipping learners to apply ML systematically rather than focusing solely on technical modeling. The project’s core objective addresses a critical challenge in offline retail: inaccurate inventory replenishment driven by store managers’ manual experience, which results in a 30%+ demand forecasting error. This inaccuracy causes dual losses—frequent stockouts of fast-moving goods (such as morning bread or rainy-day umbrellas) that cut daily revenue by 5-8%, and overstock of slow-moving items (like near-expired snacks or seasonal mismatched products) that extends annual inventory turnover days to over 60 and wastes warehouse space. The overarching goal is to build an ML-powered system that reduces forecasting errors, minimizes these losses, and ultimately boosts store profitability. A key starting point is opportunity evaluation, where learners are taught to assess whether a problem is suitable for ML using three criteria: clear business value, data availability, and technical feasibility. For this retail case, the business value is tangible—lowering forecasting error to below 15% would reduce stockout rates by 15-20%, cut slow-moving inventory by 25%, and save 70% of managers’ time spent on replenishment decisions. Data availability is confirmed, as retailers already possess structured data critical for modeling, including 12 months of SKU-level sales records, real-time inventory data, external factors like local weather and holiday schedules, and product attributes (shelf life, seasonal tags). Technical feasibility is ensured by leveraging mature ML tools and models: time-series forecasting (using ARIMA, Prophet, or LSTM) for demand prediction and classification models (Random Forest, XGBoost) for slow-moving risk assessment, with a familiar toolchain of Python, Pandas, Scikit-learn, and TensorFlow. The module then walks through the CRISP-DM Business Understanding phase, where learners define the problem, success metrics, and relevant factors. The business objective is to improve inventory turnover efficiency and profitability, solving pain points of manual replenishment inaccuracy and high stockout/overstock costs; stakeholders include store managers (end users), retail operation directors (decision-makers), and technical teams (data engineers, ML engineers). Success is quantified both in business outcomes (short-term: 10-15% lower stockout rates, 20% less slow-moving inventory; medium-term: 3-5% higher monthly revenue) and model performance (demand forecasting MAPE ≤ 20%, slow-moving classification accuracy ≥ 75%, system response time < 3 seconds). Relevant factors span internal store elements (location, size, in-store promotions), external environments (weather, surrounding foot traffic), and product attributes (category, price, new product status). Next, the module covers designing a solution validation plan to ensure the ML system solves real user needs. The proposed solution is a "dual-model integrated intelligent replenishment system": a time-series model predicts 7-day SKU demand using historical sales and external data, a classification model labels slow-moving risk levels, and the two outputs combine to generate a replenishment quantity recommendation report. Validation follows three iterative rounds with 5 pilot stores (covering residential, commercial, and mixed areas): the first tests forecasting accuracy with historical sales data, the second adds external data and the slow-moving model while collecting manager feedback on recommendation reasonableness, and the third integrates the model into a web-based system to test usability and response time—each round refining the solution based on user input. ML system design is another focus, with learners making three key architectural decisions. A hybrid cloud-edge architecture places model training and large-scale computing on the cloud (for centralized data processing and easy updates) while using store edge devices (computers/tablets) for local querying (ensuring access even with unstable networks). A hybrid real-time + batch data pipeline processes historical data (sales, product attributes) in daily batches (to save resources) and real-time data (inventory, weather) hourly/every 3 hours (to adapt to sudden demand changes like rain-driven umbrella needs). An automatic monitoring + manual review mechanism tracks metrics like prediction error and system latency in real time, triggering alerts for anomalies, while the ML team reviews causes and updates the model within 24 hours to avoid "expired" performance. The module also emphasizes identifying potential production risks to prepare learners for real-world deployment challenges. These risks include training-serving skew (inconsistent data cleaning or feature calculation between training and production, e.g., unprocessed return data in production), data drift (sudden changes in consumer behavior like new office buildings boosting breakfast demand, or unexpected events like epidemics cutting foot traffic), concept drift (shifts in business definitions, e.g., updated slow-moving product criteria), and system latency (peak-hour network congestion or insufficient cloud computing resources delaying report access). For each risk, the module highlights potential sources and impacts, reinforcing the need for proactive monitoring. Finally, the module outlines deliverables and assessment: learners record a 5-minute video presenting their project (covering opportunity evaluation, CRISP-DM business understanding, validation plans, system design, and production risks) and submit it via a public platform like YouTube. Peer evaluation focuses on clarity of problem explanation, alignment with ML opportunity criteria, thoroughness of business understanding, feasibility of validation plans, specificity of system design decisions, and awareness of production risks—ensuring learners master both technical and business-focused aspects of ML project development.

Considering that it is a field that continues to develop even if over 80% of machine learning projects fail, I think that the course I learned here is a field that should continue to be taught as a liberal arts for the future of me, my family, and my country. Thanks for making such a wonderfully balanced system.

This is a more appropriate course for the intended (AI & ML for Product Managers) audience as opposed to the first one.

worth your time if you are a product manager, product owner or project manager that is interested in implementing ML

Excellent course! And the professor is a SME in the ML field. Looking forward to the next course.

This course was very knowledgeable I really enjoyed the class and the study materials. my professor was excellent--very knowledgeable and understandable. The project time was very shot though because it was to be submitted the same day the course ended. I would like to be given a whole week to do the project after the final test of the course so that we can have time to do our project. I requested for more time but I didn't get a feedback. it was stressful to work on a project on Sunday afternoon. I hope in the future time for the project should be an extra week because we cannot start a project before the course is completed. Again this course was awesome and I am equipped with knowledge and skills. I'm excited and looking forward to take the next course Human Factors in AI. Thanks.

I have several years of experience in being a part of and managing traditional software development projects. My involvement in an AI initiative didnt need to manage the AI portion of it. This course gave me an introduction on how to think about managing AI/ML projects and helped me draw parallels with traditional SDLC projects. The pace, contents and the chronological order of the presentation were great. The exercise to pick a topic that I would solve using ML & walking thru the topic using what we learned in the course was fantastic and helped me put some of what I learnt to practice. Thank you!

I really enjoyed this course. The content was excellent, the case studies were very strong, and the overall structure was well organized. In some places it felt a bit like PowerPoint karaoke, but not excessively so — it was actually quite comfortable to follow. At the same time, the instructor did not just read from the slides and added useful commentary beyond the presentation, which helped explain the material very well. Many thanks for such high-quality materials and teaching — it was a great experience.

Clear presentation, good high level overview. This is a good starting point for those who want to get more deeply involved in ML projects and know that they don't know enough yet. It's a good starting point for further research and learning, as the main topics and best practices are covered, and additional research can then start from an informed point of view.

The course content is really good and covers enough of all what you need to know to get started with ML projects. Most importantly, its' a course where you don't need to know Python or other technical toolset required for ML but still gives you learning to be able to work efficiently with engineers on such projects.

I really liked this course for introducing some practical aspects of conceptualizing an ML solution and taking it to implementation with system design considerations. Very useful for professionals from varoius backgrounds to understand the level of undertaking to implement something like this in their org.

This course provided a very comprehensive overview of how to manage machine learning projects end to end. The final project also helped cement the different concepts together and allowed us to integrate our learning to solve an existing problem using a machine learning system.

Fantastic love the last 2 courses from this series. Especially the practical part. Probably i carried away a little bit. Because it took me a little bit longer. But i love to build not only an idea but a whole project from scratch to working. Thanks and keep on building.

I've learned valuable things in this course! This is an overview of the best practices, what to be careful of when working with ML projects, which approach to take, what are the risks, etc. I really look forward to applying this knowledge :)

Useful course -- learned about some significant differences between traditional systems and ML Systems that will be very handy going forward. Thanks, Prof R for giving us the opportunity to take this. Well worth the time. David

This course was informative & more of what I expected for an AI Product Management course. The way the information was presented & the assignment we were asked to complete at the end made applying the knowledge very easy.

The material is well broken down and organized to be easily digested. The concepts are inline with my expectations and the course was very helpful for learning the basics of machine learning project management.

The content was interesting, even for someone who has been running software projects for decades. The mini-quizzes weren't great, but the end-of-course assessment felt like it pulled everything together.

I genuinely think this is a great course especially if you have background knowledge in Product Management. This course covered a lot and I found it quite interesting, would definitely recommend.