Juggling various ML projects is not where you want to be. Constant task-switching leads to lower-quality outcomes. In any case: • 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝘁𝗵𝗲 𝟯-𝟲 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹 𝘁𝗮𝘀𝗸𝘀 that directly drive your projects forward and require your unique expertise. • 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝘁𝗵𝗲𝘀𝗲 𝗵𝗶𝗴𝗵-𝗶𝗺𝗽𝗮𝗰𝘁/𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝗲𝗱 𝘁𝗮𝘀𝗸𝘀 𝗳𝗶𝗿𝘀𝘁 each day to ensure they get the dedicated time and energy they deserve. • 𝗥𝗲𝗺𝗼𝘃𝗲, 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆, 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲, 𝗼𝗿 𝗱𝗲𝗹𝗲𝗴𝗮𝘁𝗲 𝗹𝗼𝘄-𝗶𝗺𝗽𝗮𝗰𝘁 𝘁𝗮𝘀𝗸𝘀 that can be handled by others or tools. Focus on a few hard, high-impact, bottlenecked tasks, and forget the rest.

To succeed in managing several Machine Learning projects, focus on matching each project's benefits with important business objectives. Use automated processes and flexible resource management to stay efficient. Continuously review project targets and adjust plans, making sure high-priority tasks get the attention they need. This approach helps each project progress well, reduces risks, and encourages new ideas.

Great perspective , I agree Juggling too many tasks dilutes your energy. Narrowing down to a few high-impact tasks lets you achieve more with less stress.

Its very easy to get overwhelmed by the amount of work involved in solving any large-scale ML problem. Theres a ton of data (that usually requires a lot of preprocessing to even make it understandable for an ML model) and your client's expectations that you have to meet. Most of the times you may not even know where to start. So break down the problem. Simplify it and get down to the basics - what is the final expected output? What type of ML problem are you trying to solve? (classification, regression, etc) Answering these fundamental questions can give you a good starting point. Once you have the answers to these questions, lay out a well-defined plan to tackle the problem and then simply implement the different stages of your plan!

It really shouldn’t be any different from managing your other activities. Start by clearly defining the goals and timelines for each project. Prioritize based on impact and deadlines, focusing first on the tasks that will deliver the most value. Break down each project into smaller, manageable tasks, and use tools like project management software to keep track. Regularly review your progress and adjust priorities as needed. By staying organized and disciplined, you can manage multiple machine learning projects without losing focus or productivity.