You're juggling new machine learning models and project deadlines. How can you strike a balance?

Every ML engineer knows the feeling – deadlines looming, but new models beckoning! Let me share how to solve this - 1) Prioritize Impact - Focus on high-impact or state-of-the-art models relevant to your problem (e.g., use papers with code!). 2) Smart Learning - Research recent works using these models, identify strengths & weaknesses. 3) Set Goals - Train baselines, set a performance target (e.g., 90% accuracy) 4) Manage Expectations - Be clear on model limitations or constraints to avoid chasing minor gains 5) Time is Precious - Focus on generalizable models, not small accuracy improvements.

Break down your project into multiple mini-projects. Each mini-project should have a specific and tangible deliverable. In machine learning, the most time-consuming and frustrating phase is where you must gather the right data for your problem, clean it, and prepare it for applying machine learning. Set expectations up front with your project stakeholders that this phase is going to take at least 50% of the total project time. The other big time hogs are problem definition and presentation of results. Creating and running the actual ML model is the relatively easy part. I advocate creating measurable goals for these "frustrating" phases and hold a celebration when you achieve them.

This is a very common issue among ML Engineers and Data Scientists. Every day, something new comes up. You need to prioritize your tasks such that you deliver on time as well as learn/experiment on new models. >Focus on What’s Important: Identify and work on the most critical parts of the project first. >Balance Learning and Delivery: Allocate time for experimenting with new models while ensuring timely delivery. >Separate Needs from Wants: Prioritize essential features over fancy ones. > Meet Core Goals: Ensure the main objectives are achieved, even if time is short. >Communicate Clearly: Keep stakeholders informed about what’s being prioritized and why. >Stay Flexible: Be ready to adjust priorities as the project progresses.

Starting with a baseline model is crucial in model development for several reasons. Firstly, it establishes a performance benchmark, providing a realistic starting point to measure and prioritize improvements. This process also helps identify early gaps and weaknesses, guiding subsequent refinements for a more robust final model. By progressively enhancing the baseline, developers can efficiently allocate resources towards features and algorithms that deliver substantial improvements. This method adheres to the principle that there is “NO FREE LUNCH” in achieving reliable machine-learning solutions. It not only meets stakeholders’ expectations but also nurtures a culture of continuous improvement, essential for developing impactful models.

Adopting an agile approach to machine learning projects helps balance model development with deadlines. By dividing work into small increments called sprints, teams can frequently reassess and adapt, maintaining a steady pace towards the deadline. This iterative process ensures continuous feedback, leading to a more robust and effective solution. Incorporating regular stakeholder reviews into each sprint can further enhance alignment with project goals and expectations.

Using Agile Approach would be useful when the Machine Learning model is finalised and ready to be tuned. Breaking the tasks and the possible training parameters into sprints (usually a 1-2 week period) significantly increases efficiency of the training rather than training the model in a fixed set of time. Agile focusses on continuous improvement through continuous feedback received. Quick iterations on the possible approaches, incorporating the feedback that we get back, eliminates the ways which are not efficient. This maintains a stream of small deliverables going out until a high quality product is delivered. On contrary, using Agile before the model is finalised might be counter-intuitive.

When fine-tuning a finalized machine learning model, adopting an Agile approach proves highly beneficial. Tasks and training parameters are divided into sprints, which significantly boosts training efficiency compared to fixed-timeframe methods. Agile's focus on continuous improvement through feedback facilitates rapid iterations and the elimination of inefficient approaches. This iterative process ensures a steady flow of small, refined deliverables, ultimately leading to the creation of a high-quality product. However, applying Agile before finalizing the model may detract from the necessary focus during the initial development and exploration phases.

Adopt agile methodologies to manage your projects. Break down your work into manageable sprints, allowing for iterative progress and regular reassessment of priorities. This flexibility helps accommodate new model developments and changing project requirements.

Adopting an agile approach involves breaking projects into smaller, manageable iterations, continuously delivering incremental improvements, prioritizing tasks based on stakeholder feedback, and fostering collaboration among team members. Regular sprints, daily stand-ups, and retrospective meetings ensure progress tracking and timely adjustments, enabling efficient balancing of model development with strict deadlines.

I have found modularizing ML model design is very important. Modularizing helps in collaboration and reduces rework. After full integration, it will also help in identifying break points / module to optimize for efficiency.

Creating machine learning models with a modular design allows for independent development and seamless integration of components, saving time when managing multiple projects and deadlines. This modularity facilitates parallel development, simplifies debugging, and enhances flexibility for changes without disrupting the entire system.

Using a modular design in machine learning involves breaking down the model into reusable, independent components such as data preprocessing, feature extraction, model training, and evaluation. This promotes code reusability, simplifies debugging, and allows parallel development. Modules can be updated independently, ensuring flexibility and efficiency in managing multiple projects and meeting deadlines effectively.

When tackling complex machine learning projects, think of your model as a set of interchangeable building blocks. Prioritize essential features, develop components independently, and integrate them seamlessly. This modular approach simplifies debugging, enhances flexibility, and ensures successful outcomes.

Building new ML models from absolute scratch under strict deadlines is simply not optimal - the objective should be to create an MVP in as little time as humanely possible. As such, first, find open-source models and test them on the given dataset and any performance metrics. See how they perform, and if they're sufficient for your use case. Fine-tune them, experiment with adding regularization, play around with layer sizes, and find the points where the models start overfitting. Even then, before you change the model architecture, re-examine the dataset and ensure that it is normalized properly (preventing extremely large and small values from disproportionately impacting weight updates) and is actually reflective of the problem.

Time management is the key! You need to learn the modules, while also completing the projects (in my opinion, theoretical knowledge is great, but in real life, you need to be able to implement it, so projects are IMPORTANT). My approach would be, to take notes of the important concept and spend more time building + looking for solutions when stuck.

One Core Principle of Machine Learning Economics is the *Economics of Speed* aka Time itself is a key differentiator and has a non linear impact to business .It is not the best or most efficient ML solution one should aspire for, but a ML deployment optimized to fastest *Time to Value* meeting the dynamic requirements for MLOps as well as future data drifts as underlying assumptions themselves change. Hence it is very important to explore CI/CD with rapid incremental improvement using the framework of *Minimum Viable Model* ( MVM). The hard ground reality is that most exquisite and best ML models die an early death before being operationalized.

Striking the right balance between experimenting with new ML models and meeting project deadlines requires a structured approach. Start by defining clear objectives for your ML experiments and project milestones. Allocate specific time blocks for model experimentation and stick to them, ensuring you don’t overrun into time reserved for other critical tasks. Use version control to track changes and results systematically, allowing you to revert to previous versions if needed. Implement agile methodologies to break down tasks into sprints, allowing for iterative progress and regular reassessment of priorities. By maintaining a disciplined schedule and setting realistic time limits for experimentation, you can explore innovative ML solutions.

Balancing the demands of developing new models with stringent project deadlines requires a structured and disciplined approach. One of the many challenges I faced was balancing precision with pragmatism. Ensure a clear understanding of what a "good enough" model is. In this fast-paced world of tech development, more value is given to models that surpass a required threshold than spending more time making a marginally better model. I have found that building an iterative development approach to a model can be highly beneficial in the long run. Improving an initial model progressively allows for incorporating feedback and evolving requirements without derailing the timeline.

Time management is the key,Allocate dedicated time for learning new models and streamline workflows with tools that automate repetitive tasks.

Utilize collaboration tools like Slack, Trello, or Asana to streamline communication and project management. These tools help keep your team on the same page, track progress, and ensure that tasks are completed on time, reducing the stress of juggling multiple responsibilities.

Utilizing collaboration tools is essential for effective teamwork in machine learning projects. GitHub is indispensable for version control, enabling team members to track changes, collaborate on code, and manage different branches of a project seamlessly. It ensures everyone is working with the latest codebase and facilitates easy rollbacks if needed. Google Colaboratory (Colab) enhances collaboration by allowing multiple users to work on the same notebook simultaneously in the cloud. It supports real-time coding, sharing, and visualization, making it ideal for collaborative model development and experimentation. These tools minimize misunderstandings and keep the team aligned, ensuring timely progress on complex projects.

Why is collaboration strongly referred to by everyone ? Here is the answer. Collaboration is the process of working together with teams to achieve a common goal right? Yes, it involves sharing knowledge, resources, and efforts to produce the outcomes Collaboration is for managing, streaming and coordination of tasks and keeping everyone in the same focus. How do we do real life collaboration? 1. Project management tools:- like JIRA 2. Communication tools :- slack, Microsoft teams, zoom etc. 3. Collaboration tools :- google workspace, dropbox etc. 4. Version control : GitHub and Gitlab 5. Documentation : Evernote, Notion Etc

Collaboration tools are essential for optimizing workflow and communication in machine learning projects with tight deadlines. Leveraging tools for version control, issue tracking, and project management can enhance team coordination and project efficiency. A central hub for collaboration minimizes misunderstandings and ensures alignment on project progress and timelines. #CollaborationTools #MachineLearning #ProjectManagement #Efficiency.

Use Project Management Tools: Tools like Jira, Trello, or Asana can help track progress, assign tasks, and manage deadlines. Version Control Systems: Utilize Git for version control to collaborate effectively and track changes. Communication Platforms: Use Slack, Microsoft Teams, or similar platforms for real-time communication and collaboration.

Dedicate time to stay current with AI/ML trends. Explore new algorithms and tools weekly. This investment can streamline development, optimize workflows, and provide innovative solutions, helping you meet deadlines more efficiently.

Balancing new machine learning models and project deadlines can indeed be challenging. Some things which can help- Time Management: Allocate specific time blocks for different activities. For example, dedicate mornings to deep work on machine learning models and afternoons to project management tasks. Set Clear Goals: Break down your projects into smaller, manageable goals. This makes it easier to track progress and stay motivated. Stay Organized: Use project management tools like Trello, Asana, or Jira to keep track of tasks, deadlines, and progress. Continuous Learning: Stay updated with the latest in machine learning by setting aside time for learning and experimentation.

Make it a point to read at least one research paper a day. You don't have to be able to understand it entirely but that is how you learn. Find terms or algorithms that you don't understand and then learn more about them. Try to program it out if that is possible. Leverage existing tools to help you debug your programming approach and the most important task of it all would be to discuss your approach with other interested and knowledgeable people so that you gain more insight into the field. After all, machine learning is not just about a machine learning. It requires that you learn just as much as well.

Continuous Learning: Keep abreast of the latest developments in machine learning and software development through courses, webinars, and reading relevant literature. Feedback and Adaptation: Regularly seek feedback from peers and stakeholders and be willing to adapt based on their input. Experiment and Iterate: Don’t be afraid to experiment with new approaches or tools that might improve efficiency or outcomes.

Working in data, there is always something new to learn. When you find a new topic you think sounds giving, write it down. Thereafter comes the time to learn. Allocate one session per week for progress. Then you build a project related to the topic. For example, I wanted to get better at deep learning so I built my own automatic differentiation library. It took a couple of weekends and I let it progress at its own pace. In the end I learned a lot by doing it.

From my perspective, this is the most important part. We often take a perfectionist approach, telling ourselves that we need to consider many things. This leads to overthinking until the last week or even the last night of the project, causing us to fear the project and postpone it until the very end. To tackle this problem, I ask myself what is the minimum quality acceptable for the task. I also set a timer, for example, one or two 25-minute Pomodoros, to plan for the task. Once the planning time is over, I tell myself to stop overthinking and start working. Remember, completing a normal task is much better than never finishing a perfect one. Doing more tasks also leads to more good work, while perfectionism leads to nothing.

Sharing project outcomes and challenges instead of rushing to implement new models is important. It helps everyone understand the situation and make decisions together. This makes sure everyone knows what's happening and helps us meet deadlines better.

There are some points; Identify critical tasks. Allocate yourself to specific time slots. Communicate clearly and collaborate with teams. Utilize automation wherever possible automate repetitive tasks in model development using scripts and tools. Modularize task break down model development task into smaller, manageable steps.

Risk Management: Identify potential risks early on and develop mitigation strategies to handle them without major disruptions. Quality Assurance: Implement continuous integration and testing to ensure that the models and code are reliable and meet quality standards. Work-Life Balance: Ensure that you maintain a healthy work-life balance to avoid burnout. Take breaks and manage stress effectively. Documentation: Maintain thorough documentation of your work to ensure that it is understandable and maintainable by others.