Five Strategies to Become a Top ML Backend Engineer

Strategy 1: Mastering the ML Basics

Before ML backend engineers can innovate complex applications, they must first understand the fundamentals of machine learning. Theoretical underpinnings, classic algorithms, and established best practises are not just academic exercises; they serve as the foundation for every subsequent layer of knowledge and expertise.

Case Study: Improving Query Classification Algorithms at an E-commerce Aggregator

The process of determining the intent behind a user's query or search phrase is referred to as query classification. It is critical for e-commerce aggregators, which combine products from various retailers on a single platform. For example, a customer enters the query "Harry" into an e-commerce aggregator that sells books, electronics, and clothing, which can refer to both "Harry Potter" (book or film) and "Harry Styles merchandise" (clothing), among other things. In this case, precise query classification is critical to ensuring that users find the products they seek, which can lead to higher conversion rates and higher user satisfaction. While a rule-based classification system may struggle with such a query, a machine learning model trained on massive amounts of data can make an educated guess on what the user is looking for. If you do not understand basic algorithms and methods such as Rule-Based Classification, Decision Trees, Nave Bayes Classifier, Neural Networks, and Deep Learning, your chances of success are nearly non-existent.

Key Takeaways and Tips for Mastering ML Basics

By revisiting and reinforcing the ML fundamentals on a regular basis, you can solidify your knowledge, better understand trends, and stay ahead of the competition. Here are some tips:

• Devote time and resources to foundational courses, workshops, and seminars. While it may appear strange to 'go back to school,' these avenues frequently offer new perspectives on old concepts.
• Participate in hands-on projects and challenges that allow you to apply fundamental machine learning concepts. This not only strengthens understanding but also reveals nuances that theory may have missed.

Strategy 2: Dogfooding Your Own Product

The phrase "eat your own dog food," also known as "dogfooding," refers to using your own product as an end-user in order to deeply understand its strengths, weaknesses, and areas for improvement. The concept's underlying idea is simple yet profound: by personally experiencing your product, you gain invaluable insights that can help identify issues, pain points, and opportunities for innovation that would otherwise go unnoticed.

Case Study: Improving Search Quality for a Turkish Search Engine

For example, a Turkish company that decides to build a new search engine from the ground up would face problems similar to those that many of today's tech titans faced at an earlier stage of their development. Despite having a competent QA team and utilising advanced algorithms such as BERT and Neural Networks, they would most likely face complaints about the relevance and accuracy of their search results. The dogfooding strategy, in which all company employees begin to exclusively use their own engine for online search needs, could help here. It would aid in defining and addressing flaws in query classification, word embedding, UI implementation, and ad placement, among other things. The dogfooding approach would provide the tech team with the necessary data to implement algorithms that are better suited to the nuances of the Turkish language and user behaviour.

Best Practices for Dogfooding:

• Regular Use: Integrating your product into your daily routine ensures that you encounter it in a variety of scenarios and contexts.
• Diverse Team Engagement: Encourage members from all departments to use the product, not just engineers and product managers. Different perspectives can highlight various issues.
• Feedback Channels: Create simple channels for internal feedback. When an idea or an issue is fresh in the mind, it is most powerful.
• Iterate quickly: Dogfooding works best when the insights gained lead to immediate action. Prioritise and act on feedback to constantly improve the product.

Strategy 3: Quantitative Analysis of Gaps in the Product

In the product development process, defining specific areas that need improvement is sometimes difficult without quantitative analysis. It enables businesses to accurately identify gaps and execute targeted improvements by dissecting product performance through measurable and numerical data that converts user behaviours, feedback, and product metrics into tangible numbers.

Case Study: Improving Ranking Quality in Maps Search Service

Consider the case of a globally popular maps search service that receives repeated complaints about the ranking quality of search results, with users claiming that specific landmarks deserved more visibility. The company could gain valuable data by examining user search patterns, click-through rates, engagement durations, and re-query frequencies using quantitative analysis. Taking this data into account could help define why famous landmarks are frequently overlooked, as well as refine the ranking algorithm to improve key user-centric metrics.

Techniques to Run Quick Analysis of Product Quality Leading to Actionable Insights:

• A/B Testing: Divide your user base into two groups and test different versions of your product. Compare performance metrics to see which version is more popular with users.
• Heatmaps: Make use of tools that generate heatmaps of user interactions. This graphic depicts where users spend the majority of their time and which features are ignored.
• User Flow Analysis: Discover how users navigate your product. Identify bottlenecks or drop-off points to help prioritise areas for improvement.
• Feedback Surveys with Quantitative Questions: Instead of open-ended questions, design surveys with numerical ratings. This provides a more precise baseline for analysis.
• Real-time Monitoring Tools: Implement tools that provide real-time insights into user behaviour, allowing the team to quickly identify and address any unexpected issues or gaps.

Strategy 4: Speeding Up the Development Cycle

The ML model development cycle is an iterative process that involves ideating, training, testing, and deploying. Each loop of this cycle aims for a more refined, accurate, and efficient model. Shortening it is an absolute necessity and iterating quickly is a necessity in the ever increasing market competition.

Case Study: Reducing Model Inference Time for a Self-Driving Car Project

The inference time of a driving model is critical in the self-driving car industry because longer inference times may result in delayed reactions on the road. Streamlining the development cycle by incorporating some truly effective strategies can allow the self-driving car to react faster in real-time while also allowing the team to iterate and improve the model more frequently, further improving performance.

The Strategies to Speed Up the Development Cycle Are:

• Using a Smaller Model/Smaller Pool to Experiment With: Begin with a smaller, more manageable model or data subset before scaling up for faster experimentation and hypothesis validation.
• Building Automated Training and Evaluation Pipelines: Automate time-consuming tasks like training and evaluation pipelines to free up engineers' time for refinement and innovation.
• Leveraging Dogfooding and Offline Testing Before Online Testing: Use your product internally and run offline tests to detect major issues before launching a full-scale online test to address potential issues early on, saving time and resources in the long run.

Strategy 5: Building Demonstrative Prototype

The demonstrative prototype is a very useful tool for increasing the chances of creating a successful end product. It connects the abstract concept to the concrete product realisation.

The Role Demonstrative Prototypes Can Play in the Development of Your Product:

• Visualisation: A prototype brings an idea to life, allowing all stakeholders to interact with and better understand the concept, whether they are developers, investors, or potential customers.
• Gathering feedback: Users can test prototypes and provide feedback for further design improvements to ensure that the end product closely matches user needs and expectations.
• Reducing costs: Identifying design flaws or inefficiencies during the prototype stage can save significant resources when compared to detecting them after full-scale production.
• Getting the stakeholders’ support: A working prototype can serve as proof of the idea's viability to potential investors or top management.
• Refining User Experience (UX): Interacting with a prototype can provide insights into the user journey, allowing designers to refine the UX for greater intuitiveness and user satisfaction.
• Risk Mitigation: Potential risks or shortcomings can be identified and addressed early on by testing the prototype in real-world scenarios.

Conclusion

In this article, we looked at how to become a top Machine Learning (ML) backend engineer in this thriving industry. They are centred on continuous learning of ML fundamentals, using the dogfooding approach and quantitative analysis, techniques for accelerating development cycles, and building demonstrative prototypes.

Choosing a career as an ML backend engineer, apply these strategies in your daily work to improve your performance, reach new heights, and propel the entire technology sector forward!