Nowadays, Artificial Intelligence and machine learning have become the talking points of the technological and business industries. Many of us are heedless about the fact that we are already making use of Artificial Intelligence. Artificial Intelligence (AI) is an area of Computer Science shaping many industries by solving intellectual issues linked to Human Intelligence. The rapid growth of AI is the result of vast research done by scientists and engineers. Bigger organizations are investing heavily in AI and everyone who has heard of Artificial Intelligence(AI) believes in it and considers it in the future. One of the popular parts of AI is Machine Learning.

We are about to wrap up 2022, a year that brought plenty of Machine Learning projects, events, education opportunities, and many groundbreaking Machine Learning applications developed by ML practitioners around the world. The challenges and business needs of our customers continue to fuel our passion to bring to life the robust and innovative Machine Learning solutions they deserve. In this blog post, we put together the highlights of 2022 covering Machine Learning's lasting impact on a vast number of industries and businesses, BigML's new additions and enhancements to our pioneering Machine Learning software platform, our live and virtual events, education initiative updates, and much more! None of the numbers listed above and the activities described on this blog post would be possible without our customers, partners, followers, and certified practitioners. That's why this blog post is dedicated to all of you.

MLflow Pipelines also implement a cache-aware executor for pipeline steps. This ensures that steps are only carried out when there has been a change in the corresponding code or configuration. In addition, executing pipelines and examining their output can be done via APIs and a command line interface (CLI) provided by MLflow.

Machine learning (ML) continues to permeate all layers of academia, industry and society. Despite its successes, mental frameworks to capture and represent machine learning workflows in a consistent and coherent manner are lacking. For instance, the de facto process modeling standard, Business Process Model and Notation (BPMN), managed by the Object Management Group, is widely accepted and applied. However, it is short of specific support to represent machine learning workflows. Further, the number of heterogeneous tools for deployment of machine learning solutions can easily overwhelm practitioners. Research is needed to align the process from modeling to deploying ML workflows. We analyze requirements for standard based conceptual modeling for machine learning workflows and their serverless deployment. Confronting the shortcomings with respect to consistent and coherent modeling of ML workflows in a technology independent and interoperable manner, we extend BPMN's Meta-Object Facility (MOF) metamodel and the corresponding notation and introduce BPMN4sML (BPMN for serverless machine learning). Our extension BPMN4sML follows the same outline referenced by the Object Management Group (OMG) for BPMN. We further address the heterogeneity in deployment by proposing a conceptual mapping to convert BPMN4sML models to corresponding deployment models using TOSCA. BPMN4sML allows technology-independent and interoperable modeling of machine learning workflows of various granularity and complexity across the entire machine learning lifecycle. It aids in arriving at a shared and standardized language to communicate ML solutions. Moreover, it takes the first steps toward enabling conversion of ML workflow model diagrams to corresponding deployment models for serverless deployment via TOSCA.

Let's have a quick look into basic workflow when we apply Machine Learning to a problem. A short brief about Machine Learning, its association with AI or Data Science world is here. Machine Learning is about having a training algorithm that helps predict an output based on the past data. This input data can keep on changing and accordingly, the algorithm can fine tune to provide better output. It has vast applications across.

When we approach modern Machine Learning problems in an AWS environment, there is more than traditional data preparation, model training, and final inferences to consider. Also, pure computing power is not the only concern we must deal with in creating an ML solution. There is a substantial difference between creating and testing a Machine Learning model inside a Jupyter Notebook locally and releasing it on a production infrastructure capable of generating business value. The complexities of going live with a Machine Learning workflow in the Cloud are called a deployment gap and we will see together through this article how to tackle it by combining speed and agility in modeling and training with criteria of solidity, scalability, and resilience required by production environments. The procedure we'll dive into is similar to what happened with the DevOps model for "traditional" software development, and the MLOps paradigm, this is how we call it, is commonly proposed as "an end-to-end process to design, create and manage Machine Learning applications in a reproducible, testable and evolutionary way". So as we will guide you through the following paragraphs, we will dive deep into the reasons and principles behind the MLOps paradigm and how it easily relates to the AWS ecosystem and the best practices of the AWS Well-Architected Framework. As said before, Machine Learning workloads can be essentially seen as complex pieces of software, so we can still apply "traditional" software practices.

To understand Machine Learning Algorithm, it is very essential for technical and non-technical stakeholders to understand Machine learning workflow to be familiar with the job of a data scientist, the processes a data scientist follows to provide feedback to decision-makers and the machine learning process in a business environment. Machine Learning Workflow derive answers to business challenges, it derives meaningful conclusions for complicated issues and identify actionable steps with a given set of variables. Step 1 -- Get more data - Data can be collected in different formats. Step 2 -- Ask a sharp question - Need for a sharp question - It is direct and specific. Using sharp question help us get relevant information.

Machine Learning Workflow derive answers to business challenges, derive meaningful conclusions for complicated issues and identify actionable steps with a given set of variables. Helps overcome challenges where some features may not give useful information for the model, whereas some features may be combined to derive meaningful information. Making up decision - Proposing the price of an item - Publishing the results obtained as a part of a research paper.

In this blog, I will briefly talk about the different Machine Learning options that are available in Google Cloud Platform and walk through an example project of my own. This will include briefly talking about the older AI Platform service as well as introducing the new Vertex AI service. My project will give an example of how to read data from a GCS bucket, perform exploratory data analysis in a managed Jupyter notebook instance, train a model in that notebook, save the model to a different GCS bucket, and finally use that model in a full-stack application. Here is the repository with the code for this application. AI Platform was GCP's original Machine Learning service.

When I was a novice in Machine Learning, I couldn't figure out where to, and how to start the model training? Even though I knew all the key points, still it was difficult for me to step into coding and I think its quite normal for every newbie. So I decided to write about the ML workflow to solve the so-called puzzle. In this article, we will work on Keras' built-in MNIST image dataset. If the train and test labels are string, you should convert them into numerical values.