In a volatile world, your machine learning models can turn quickly from assets into liabilities. When faced with conditions not encountered in the training data, your models will make inaccurate and unreliable predictions that will undermine consumer trust and introduce risk to the business. Additionally, most machine learning deployment processes today are manual, complex, and span data science, business, and IT organizations impeding the rapid detection and repair of model performance problems.
To maintain current levels of AI adoption and scale in order to take advantage of new opportunities, every organization needs a better way to deploy and manage the lifecycle of all their production models holistically across the enterprise.
Algorithmia now helps businesses manage and deploy their machine learning models
I remember the first time I created a simple machine learning model. It was a model that could predict your salary according to your years of experience. And after making it, I was curious about how I could deploy it into production.
Google AI Platform provides comprehensive machine learning services. Data Scientists and Machine Learning Engineers can use this platform to work on machine learning projects from ideation to deployment more effectively.
With the Google AI platform, you will get access to all its assets under one roof. It includes data preparation, model training, parameter tuning, model deployment, and sharing machine learning models with other developers.
Therefore you can deploy your machine learning model with a supported block of code for execution on the google cloud function and call the HTTP request for prediction from your web application or any other system.
With serverless, you can write a snippet of code that runs your model and then deploy the code and machine learning model on Azure Functions and call it for prediction as an API. Azure functions are similar to Google cloud functions.
Machine learning deployment is one of the important skills you should have if you're going to work on machine learning projects. The platforms mentioned above can help you deploy your model and make it useful rather than keeping it in your local machine.
Arthur is the proactive model monitoring platform that gives organizations the confidence and peace of mind that their AI deployments are performing at peak. Arthur provides a layer of performance monitoring, algorithmic bias detection, and explainability, even for black box models, so data science teams can detect, diagnose, and fix any issues in production.
We are working with the Algorithmia team to make it easier than ever to use their top-notch deployment, serving, and management tools and our leading monitoring capabilities. Together, Arthur and Algorithmia provide a powerful set of tools that give you complete control over your production AI. This blog post will demonstrate just how easy it is to get started with the Arthur and Algorithmia integration, so you can complete your AI stack.
Organizations have to manage data, code, model environments, and the machine learning models themselves. This requires a process, where they can deploy their models, monitor them, and retrain them. Most organizations have multiple models in production, and things can get complex even with one model.
Managing machine learning models in production is a difficult task, so to optimize this process, we will discuss a few best and most used machine learning lifecycle management platforms. These range from Small-scale to Enterprise-level cloud and open source ML platforms, which will help you improve your ML workflow from collecting data to deploying applications to the real world.
Amazon SageMaker is an ML platform which helps you build, train, manage, and deploy machine learning models in a production-ready ML environment. SageMaker accelerates your experiments with purpose-built tools, including labeling, data preparation, training, tuning, hosting monitoring, and much more.
Azure ML is a cloud-based platform which can be used to train, deploy, automate, manage, and monitor all your machine learning experiments. Just like SageMaker, it supports both supervised and unsupervised learning.
Google Cloud is an end-to-end fully managed platform for machine learning and data science. It has features which help you manage service faster and seamlessly. Their ML workflow makes things easy for developers, scientists, and data engineers. The platform has many functions which support machine learning lifecycle management.
Gradient by Paperspace is a machine learning platform which can be used from exploration to production. It helps you build, track and collaborate on ML models. It has a cloud-hosted design for managing all your machine learning experiments. The majority of the workflow was built around NVIDIA GRID, so you can expect a powerful and faster performance.
Algorithmia is an enterprise-based MLOps platform that accelerates your research and delivers models quickly, securely, and cost-effectively. You can deploy, manage, and scale all your ML experiments.
HPE Ezmeral is Hewlett Packard service which offers machine learning operations at enterprise level. From sandbox to model training, deployment, and tracking. It can be performed seamlessly with any machine learning or deep learning framework.
Algorithmia, which specialises in MLOps, has developed a set of tools to help technology leaders with post-deployment risks in machine learning models. Operational risk is now the most significant analytics risk, according to the company.
Data science and machine learning teams are not doing enough training and iterating models because they may be bogged down with infrastructure, deployment and engineering issues, according to a survey released this week of 523 data scientists and machine learning by professionals Seattle-based Algorithmia Inc.
When it comes to developing and deploying machine learning, the 2018 "State of Enterprise Machine Learning" report found that larger companies, defined as having 2,500+ employees, are happier with how things are going than smaller companies with 500 employees or less.
"In 2018, large enterprise companies have an advantage when it comes to machine learning because they have access to more data, can continue to invest in big R&D efforts, and have many problems that machine learning technology can solve cost-effectively," Diego Oppenheimer, CEO at Algorithmia, was quoted as saying in a press release announcing the survey. "And yet, even in the largest companies, productionizing and managing machine learning models remains a challenge."
Big tech-based companies including Google, Facebook and Uber are creating a new ML-based infrastructure, which Algorithmia dubs the "AI Layer." An AI Layer manages compute loads, automates deployment of machine learning models, and propagates machine learning throughout the company, according to Algorithmia.
The Algorithmia survey, which focused on organizations in North America, found that while ML is getting a big boost from "massive investments of time, money and focus," human intelligence appears to be dogging artificial intelligence efforts. "For example, data science and machine learning teams are spending too much time on infrastructure, deployment and engineering, and not nearly enough (less than 25 percent) on training and iterating models," the report on the survey results concludes.
Despite these challenges Oppenheimer sees a bright future for ML in large and small enterprises: "In general, larger companies have more machine learning use-cases in production than smaller companies. But across the board, all companies are getting smarter about where and how to apply ML technology. We expect to see big leaps in productionized machine learning next year as data scientists can more easily deploy and manage their models."
Boston-based DataRobot, a company which provides users with a data science and machine learning platform for building, deploying, and maintaining AI, is on a roll. Data science and machine learning is a process in which patterns in data are examined in order to make predictions and better understand the data at hand.
The need for this software, which allows users to manage and monitor machine learning models as they are integrated into business applications, is clear and concrete. Without it, businesses can monitor and maintain models but may struggle with integrating and deploying these models across the business.
As Algorithmia noted in their 2021 enterprise trends in machine learning report, AI and machine learning initiatives are top priorities for many organizations. However, without proper MLOps tools and strategies in place businesses will end up spending more time, energy, and resources on model deployment. With Algorithmia in their pocket and $300 million in the bank, DataRobot will be able to add more tools in their toolbox for model deployment, thus improving on their end-to-end solution. This, in turn, will help businesses succeed in transforming their data into insights and driving business value.
Matthew Miller is passionate about emerging technology and its impact on society and businesses. He most recently worked as an AI Research Analyst at CognitionX, a London-based AI-powered Knowledge Network and host of one of Europe's largest Ai conferences. He also co-founded a pro bono voice technology group, VAICE, which has helped companies discover the best ways to incorporate voice tech in their business and their business models. At G2, he is focusing on the AI and Analytics categories and looks forward to learning more. Get in touch at mmiller@g2.com. 2ff7e9595c
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