Data Science Tool Evaluation

Hi Everyone, Does anyone here specialize in MLOps? Machine Learning Engineering for Production (MLOps) or know someone who does? I am trying to evaluate this tool and struggling a bit, any advice would be welcome.
Would help if you could detail a bit more so that I can share my experiences accordingly.
Hi Sambhavi, I am reviewing a tool, which claims to accelerate time from modeling to production, however, without the proper experience, I can't tell if there is value in this tool. One of the claims is that it can be leveraged for tech professionals who are somewhat experienced in data analysis. The idea there would be to grant capability to companies who cannot afford to bring on more experienced data experts. This is what has made me really interested, if true, there some work I would like to do to help create alternative paths into the field for under-represented groups. I would be super happy if you could take a look and provide feedback from a professional perspective.
In general, aktiver like tools are meant to make the life of a data scientist's life easier. That being said, I should definitely throw in some caution about the term "Data scientist" as it's being loosely used with different expectations.To me, any ML project has 3 equally important aspects, the data part, the training part, and the deployment part. There are a good number of proven tools in each segment. Very few tools like Amazon sagemaker, Domino data lab, Paperspace, Floyd hub, and few others claim to handle all aspects. This particular tool Aktiver is something that I have not come across so far. And their website doesn't seem to give a lot of information too. Just for you to compare, I'll suggest you check Roboflow, they claim to be a complete platform for computer vision ML/DL problems. Likewise, if you take the aspect of Experiment Managers and Hyper parameter tuning while performing training of models, Weights & Biases is a good one. Check that one too. For data labeling and other data related activities, there are quite a few tools like figure eight and aquariumlearning.In short, I would suggest you narrow down which problem area (are they images, text, numbers, what exactly) will your target audience work on, and then, look for which area (data, training, deployment) needs to be handled using tools and so as a result, the subset narrows down. Only then, we should evaluate appropriate tools and suggest them to the company that you think will benefit from.I hope I'm able to throw some light to your question.