There is a substantial increase in the use of learning management systems (LMSs) to support e-learning in higher education institutions, particularly in developing countries. This has been done with some measures of success and failure as well. There is evidence from literature that the provision of e-learning faces several quality issues relating to course design, content support, social support, administrative support, course assessment, learner characteristics, instructor characteristics, and institutional factors. It is clear that developing countries still remain behind in the great revolution of e-learning in Higher Education. Accordingly, further investigation into e-learning use in Kenya is required in order to fill in this gap of research, and extend the body of existing literature by highlighting major quality determinants in the application of e-learning for teaching and learning in developing countries. By using a case study of Jomo Kenyatta University of Agriculture and Technology (JKUAT), the study establishes the status of elearning system quality in Kenya based on these determinants and then concludes with a discussion and recommendation of the constructs and indicators that are required to support qualify teaching and learning practices.According to the Organization for Economic Co-operation and Development (OECD), many countries are currently overseeing a massive expansion of higher education through the use of information and communication technologies (ICTs). However, improving quality is one the most significant challenges for Higher Institutions of Education (HEIs), particularly in developing countries. This is as a result of enrollment expansion characterized by a range of weak inputs such as weak academic preparation for incoming students, lack of financial resources, inadequate teaching staff, poor remuneration of staff, and inadequate staff qualifications (Johanson, Richard, & Shafiq, 2011; United States Agency for International Development [USAID], 2014; Aung & Khaing, 2016).Recent studies show that ICT integration in education through e-learning are facing numerous challenges associated with quality. For example, studies in Kenya confirmed that there are quality issues linked to inadequate ICT and e-learning infrastructure, financial constraints, expensive and inadequate Internet bandwidth, lack of operational e-learning policies, lack of technical skills on e-learning and e-content development by teaching staff, inadequate course support, lack of interest and commitment among the teaching staff, and longer amounts of time required to develop e-learning courses (Tarus, Gichoya,& Muumbo, 2015;Makokha & Mutisya , 2016).A related study (Chawinga, 2016) in Malawi on increasing access to university education through elearning observed that the greatest obstacles to e-learning use were: Lack of academic support (77.6%);Delayed end of semester examination results (75.5%); Class too large (74.3%); Delayed feedback from instructors (72.6%); Failure to find relevant information for studies (67%)...

The purpose of this study was to find out the challenges facing Machine Learning (ML) software development and create a design architecture and a workflow for successful deployment. Despite the promise in ML technology, more than 80% of ML software projects never make it to production. As a result, majority of companies around the world with investments in ML software are making significant losses. Current studies show that data scientists and software engineers are concerned by the challenges involved in these systems such as: limited qualified and experienced ML software experts, lack of collaboration between experts from the two domains, lack of published literature in ML software development using established platforms such as Django Rest Framework, as well as existence of cloud software tools that are difficult use. Several attempts have been made to address these issues such as: Coming up with new software models and architectures, frameworks and design patterns. However, with the lack of a clear breakthrough in overcoming the challenges, this study proposes to investigate further into the conundrum with the view of proposing an ML software design architecture and a development workflow. In the end, the study gives a conclusion on how the remedies provided helps to meet the objectives of study.