What challenges are ML Engineering team facing? Key success factor that defines a ML Engineering team is that they have access to Data and then they have automated their software engineering to achieve agility and efficiency in delivering value to the business. A ML Engineering team faces following challenges in two broad category. DataOps – Data Engineering Data collection from different sources and extraction Data pipelines that primes for…
Is DevOps a necessity? Under Engineering Practice, software engineers are empowered to have responsibility developing high quality products through use of test driven development and other engineering practices that enable them to build features in agile fashion. Engineering teams are enabled to rapidly iterate through features. These features may be empirically tested with customers such that only customer impacting features are productized. Such a process will improve performance to…
ML Engineering Developing and releasing quality software is hard. If there is no automated process, it is downright expensive and increases risk to business due to unseen failures. For a software engineer, being able to write, test, and quickly deploy and perform tests (integration, performance, vulnerability) are very important to have a successful non-eventful release. An Organization must develop an engineering cultural practices that recognizes continuous – building, testing,…
Putting Data Science in Perspective Data Science projects work may be broken into two major parts. They are Data Engineering and Data Science. Data Engineering is about Sourcing the data from different databases inside your enterprise or outside your enterprise Doing ETL that includes cleaning/cleansing of data. At this point you may also sanitize/anonymize such that data is secured while working on Data Science Doing feature engineering to extract…
As AI and ML is getting pervasive into our technology landscape, there is a huge benefit in getting your Data Scientists to focus on models rather than working on Data Engineering or worse wait on Data Engineers. There are many different acronyms – DataOps, Data Engineering, Feature Engineering, and even MLOps to indicate improvement in process and engineering. For building and deploying ML to production at scale, a key…
