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Latest in MLops
Final AI-written
MlOps Newsletter
This document is aimed at data scientists and ML engineers who want to apply DevOps principles via MLOps to ML systems. Many companies invest in developing forward-looking models that can provide value to their users. ML Ops is an ML engineering culture and practice that aims to develop and deploy predictive and predictive analytics systems for business applications. Sources: 1
The practice of MLOps means to be involved in all steps of ML system construction, including the design, development, testing, deployment and management of the ML systems themselves. The company is driving innovation and democratization through the development and deployment of advanced analytics and machine learning systems, as well as the integration of AI, machine learning, data science and managed services (MMS). Sources: 5, 7
DataRobot supports the advanced capabilities required by data scientists in a way that makes them easy to use. After introducing an automated machine learning solution with the AIOps team in 2015, they acquired Nutonian, Nexosis and 2018 Cursor in 2016. Leading artificial intelligence companies raised more than $1.5 billion in venture capital funding and $2.2 billion in private equity funding in 2017-2018. Sources: 7
DataRobot's platform accelerates and scales data science capabilities to maximize transparency, accuracy, and collaboration. According to a recent report, Data Robot is recognized as one of the top ten machine learning companies in the US and the third largest company in North America. DataRobOT's capabilities protect the privacy and security of data scientists and their colleagues, while maximizing transparency and accuracy in collaboration. Sources: 7
According to industry analysts, only a small percentage of AI models make it into production, and few of them seriously lack the control and monitoring needed to ensure that AI can be trusted. In the official announcement, DataRobot said: 'The value derived from this investment is missing from the AI model used in production. To control production models, the ability to scale and reduce the risk of human error and code updates is required. Sources: 4, 7
Strong governance gives us the freedom to take steps that advance AI-driven businesses and realize the value of AI through the integration of machine learning and AI applications into our business. DataRobot is a leader in enterprise AI, delivering trusted AI technology to global companies participating in today's intelligence revolution. Ensuring compliance with legal and regulatory requirements also means taking a proactive approach to data security, data protection and data protection. Sources: 4
Cloudera recently announced an ML Monitoring Service to capture technical performance metrics and model predictions. In addition to developing machine learning models and monitoring them in production, there are additional tools, processes and collaboration options that allow you to scale your data science practices. Automation and infrastructure practices are analogous to development practices and include infrastructure. Sources: 6
Some tasks now include monitoring production machine learning models for drift, automating the retraining of models, warning when drift is significant, and detecting when a model requires an update. Others include versioning the training data underlying the model and searching for model repositories. Sources: 6
As more organizations invest in machine learning, it is necessary to raise awareness of model management and operations. The good news is that we are making great progress in the areas of model monitoring, model monitoring and drift monitoring. Sources: 6
The application of these practices simplifies the management process, automates, increases quality and increases quality. Public cloud providers also share best practices in implementing MLops in Azure Machine Learning. Sources: 3, 6
Data scientists and ML / DL engineers can optimize various functions, hyperparameters and parameters of the model while retaining and managing the data, code base and reproducible results. Sources: 3
Despite all efforts and instruments, the ML / DL industry is still struggling with the reproducibility of experiments. The complexity of the use of machine learning models has led to the relatively new concept of MLOps. Sources: 0, 3
MLOps has established ML as an engineering discipline and brings together a range of tools that aim to automate the ML lifecycle. MLOps focuses on machine learning as a toolbox, not a single tool to establish and establish ML in engineering disciplines and automate its implementation. Sources: 0
HONEYPOTZ INC offers an end-to-end machine learning solution that does just that, and companies can use it to deliver concrete results driven by ML. AIStudio.ml cleverly leverages emerging Kubeflow’s and machines - learning technologies to make the application of ML more efficient, scalable, and cost-effective.
Contact us for more information, sign up for our upcoming webinar, and take a look at some of our other model-related activities on our website:
Cited Sources
https://www.datasciencecentral.com/profiles/blogs/how-to-use-mlops-for-an-effective-ai-strategy 0
https://www.enterprisetimes.co.uk/2020/02/06/why-governance-comes-first-in-mlops/ 4
https://www.crystalloids.com/news/what-is-mlops-and-why-is-it-important 5
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