3 research outputs found
MLOps: A Review
Recently, Machine Learning (ML) has become a widely accepted method for
significant progress that is rapidly evolving. Since it employs computational
methods to teach machines and produce acceptable answers. The significance of
the Machine Learning Operations (MLOps) methods, which can provide acceptable
answers for such problems, is examined in this study. To assist in the creation
of software that is simple to use, the authors research MLOps methods. To
choose the best tool structure for certain projects, the authors also assess
the features and operability of various MLOps methods. A total of 22 papers
were assessed that attempted to apply the MLOps idea. Finally, the authors
admit the scarcity of fully effective MLOps methods based on which advancements
can self-regulate by limiting human engagement
Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows
The well-known Vehicle Routing Problem with Time Windows (VRPTW) aims to
reduce the cost of moving goods between several destinations while
accommodating constraints like set time windows for certain locations and
vehicle capacity. Applications of the VRPTW problem in the real world include
Supply Chain Management (SCM) and logistic dispatching, both of which are
crucial to the economy and are expanding quickly as work habits change.
Therefore, to solve the VRPTW problem, metaheuristic algorithms i.e. Particle
Swarm Optimization (PSO) have been found to work effectively, however, they can
experience premature convergence. To lower the risk of PSO's premature
convergence, the authors have solved VRPTW in this paper utilising a novel form
of the PSO methodology that uses the Roulette Wheel Method (RWPSO). Computing
experiments using the Solomon VRPTW benchmark datasets on the RWPSO demonstrate
that RWPSO is competitive with other state-of-the-art algorithms from the
literature. Also, comparisons with two cutting-edge algorithms from the
literature show how competitive the suggested algorithm is
Approximation on parametric extension of Baskakov–Durrmeyer operators on weighted spaces
Abstract In the present manuscript, we define a non-negative parametric variant of Baskakov–Durrmeyer operators to study the convergence of Lebesgue measurable functions and introduce these as α-Baskakov–Durrmeyer operators. We study the uniform convergence of these operators in weighted spaces