MODELOPS VS MLOPS: HERE IS WHAT YOU NEED TO KNOW

The major differences between ModelOps and MLOps

One area marked by confusion today is understanding the differences between ModelOps vs. MLOps. ModelOps is the missing link for today’s approach, connecting together existing data management solutions and model training tools to the value delivered via business applications. By incorporating ModelOps into your AI pipeline, you’ll move past last-mile challenges with operationalizing AI and begin to see the return on your investments in the form of reduced costs, increased revenues, and better risk management.

Recently, ModelOps has emerged as the critical link to addressing last-mile delivery challenges for AI deployments. ModelOps is a superset of MLOps, which refers to the processes involved to operationalize and manage AI models in use in production systems. ModelOps tools provide all the capabilities of MLOps, but also provide two important additions:

1. ModelOps tools allow you to operationalize all AI models, whereas MLOps tools focus primarily on machine learning models.

2. While MLOps tools allow collaboration amongst various teams and stakeholders involved in building AI-enabled applications (data science teams, machine learning engineers, software developers), ModelOps tools provide dashboards, reporting, and information for business leaders. This provides teams with transparency and autonomy to work in a collaborative manner for AI at scale.

Because all information is governed, tracked, and auditable, ModelOps tools provide transparency into AI usage across an enterprise. Not only is this essential for monitoring model performance, drift detection, and retraining for AI models, but it enables insight into AI health. Teams can better manage and plan for infrastructure costs, while also maintaining control over access to sensitive business data through governance and role-based access control. By automating the logging and tracking of this information, data science teams, machine learning engineers, and software development teams can focus on building and maintaining systems, while business and IT leaders can easily access reporting metrics for ongoing monitoring.

ModelOps will be one key to unlocking value with AI for the enterprise. If you look at all the other parts of the AI pipeline – data management, data wrangling, model training, model deployment and management, and business applications, ModelOps is the connective tissue. It links the disparate pieces of the pipeline to deliver value through business applications. By providing a shared tool to track and manage AI assets across all management stakeholders, an organization can:

• Reduce risks associated with “shadow” solutions built outside the purview of the IT department
• Reduce redundancies leading to better allocation of resources and increased reuse of models

MLOps helps data scientists with rapid experimentation and deployment of ML models during the data science process.   It is a feature of mature and maturing data science platforms like Amazon Sagemaker, Domino Data Lab, and DataRobot. ModelOps is enterprise operations and governance for all AI and analytic models in production that ensures independent validation and accountability of all models in production that enable business-impacting decisions no matter how those models are created.  ModelOps platforms like ModelOp Center automate all aspects of model operations, regardless of the type of model, how developed, or where the model is run. MLOps tools and features are used for developing machine learning (ML) models.  It includes the actual coding of the ML model, testing, training, validation, and retraining.  Data Scientists are responsible for the model development, working closely with the DataOps and Data Analytics teams to identify the proper data and data sets for the model.  The Data Scientists are typically aligned with a line of business and remain focused on the goals of that particular business unit or a specific project. ModelOps platforms and capabilities are used to ensure reliable and optimal outcomes for any and all models in production.  It includes managing all aspects of models in production, such as inventorying models that are in production, ensuring production models are providing reliable decision-making, and adhering to all regulatory, compliance, and risk requirements and controls.  CIOs and IT Operations, working with lines of business, are responsible for establishing and implementing a ModelOps platform that meets the needs of the enterprise.

 

The Value of MLOps and ModelOps

MLOps and ModelOps are complementary solutions, not competitive ones. ModelOps solutions can’t build models, and MLOps can’t govern and manage production models throughout their lifecycle across the enterprise. Some MLOps solutions offer limited management capabilities, but the limitations tend to become evident when enterprises begin to scale AI efforts and uniformly enforce risk and compliance controls.  Additionally, the “tried and true” practice of having checks and balances between development and production operations applies to every model that is developed and put in production. ModelOps platforms automate the risk, regulatory, and operational aspects of models and ensure that models can be audited and evaluated for technical conformance, business value, and business and operational risk.  By combining these enterprise capabilities with the efficiency of MLOps tools, enterprises can exploit the investment in their MLOps tools and build a foundational platform for accelerating, scaling, and governing AI across the enterprise.

TOP 10 MACHINE LEARNING INNOVATIONS TO WATCH OUT FOR IN 2021

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A glimpse through the top machine learning innovations in 2021

The applications of machine learning in the real world have made our daily tasks more feasible, swifter, efficient, and precise. Powered by data science, if machine learning applications are trained accurately, they can complete the tasks way more quickly than humans. Several business leaders around the world are incorporating this technology for a competitive advantage and to align with organizational goals and employees’ interests. Here are some of the top applications and innovations of machine learning in 2021.

 

• No-Code Machine Learning: No-code ML is a way of programming ML applications without having to go through the long and tedious procedures of processing, modeling, designing algorithms, deployment, retraining, and more. It is much quicker and simpler to implement and is also cost-effective.
• Tiny ML: Microcontrollers are the keywords when it comes to Tiny ML. They can shrink deep learning networks to fit into any small hardware system. It is very useful when it comes to cars, refrigerators, and other utility meters. These newly embedded machine frameworks are also enabling high-powered AI-IoT devices to perform efficiently.
• Quantum ML: The interdisciplinary area where quantum computing is mixed with machine learning is called Quantum ML. It allows technicians to make classic ML algorithms and apply them to quantum circuits so that they can effectively run on quantum computers. Because of this new feature in quantum technology, space exploration, understanding nanoparticles, and such other advanced researches have become possible.
• Auto ML: Auto ML aims to build ML applications that are more accessible to developers. It bridges the gap by providing an accessible solution that does not rely on ML experts. Auto ML provides simplification to data scientists working on machine learning projects by the use of templates.
• MLOps: Machine learning operationalization management (MLOps) is a newly developing ML software solution with a focus on reliability and efficiency. It is a procedure to develop ML solutions so that they can be used in businesses for more efficiency. It automates dealing with data on larger scales easier, ensuring minimum errors.

• ML in Cybersecurity: One of the most popular techniques of improving the degree of cybersecurity is deploying machine learning applications. ML algorithms are used to create smart antivirus software that can identify viruses or malware swiftly by its abnormal behavior.
• Unsupervised ML: This technology focuses on unlabeled data. Without any guidance from experts and data scientists, unsupervised ML can draw their conclusions. It is used to study data quickly from unidentified structures to extract useful patterns and use that information to automate decision-making.
• AI Engineering: The integration of artificial intelligence and machine learning has given rise to a new profession which is called AI engineering. It is a streamlined strategy for a company to provide great performance, reliability, scalability, which in turn ensures successful investment in AI.
• Reinforcement Learning: In reinforcement learning, the ML system can learn from direct exposure to its environment. The environment can use the rewards or punishments systems to assign a particular value to results yielded by the application. But either way, using this method will help the ML systems achieve the highest level of accuracy.
• GAN Technology: General adversarial networks (GAN) technology is a method for producing stronger solutions for implementations like differentiating between different kinds of images. Neural networks produce samples that are checked by discriminating networks to eliminate unwanted content.