Making mistakes is part of the learning process, and probably there is no way to avoid it. The important thing is to make sure we don't make the same mistake twice. This is not possible if we don't even know we are making a mistake. In the sequel, I discuss three common mistakes regarding the use of data science tools and practices. These mistakes make your work inefficient and may cause unnecessary charges.
Upfront I want to say what I am not covering in this section -- renaming columns, subsetting data, change of data types (e.g. To keep this writing focused on time series formating I will not cover them here, but if interested you could check out my previous article -- A checklist for data wrangling. As usual, I'm using pandas for data wrangling and I'll go with matplotlib and seaborn for visualization. For this exercise, I've downloaded an interesting dataset on monthly retail book sales (million US$) reported by book stores all across the US. The date range is between 1992 and 2018.
By Johana Moreno, Product Owner The insurance industry has ushered in a new digital era in which customer behavior and preferred interaction is being converted to digital, customers want their problems solved quickly, and new generations prefer services with a rich digital experience. Insurers are looking for more automation and intelligent technologies to address all these trends, which have been exacerbated by the Covid crisis. This is where AI fills the gap and occupies a large space for automation and services with exceptional customer experience. As an AI company, Zelros has been successfully using its machine learning models for various customers, and a wide range of applications in the insurance industry overcoming many challenges. On the one hand, implementing accountable tools to demonstrate the positive impact of our AI models for very demanding customers, and on the other, providing the gold service and reducing time to market. These have allowed Zelros to reinvent and optimize its process without compromising quality. Machine learning is not an insignificant task, behind the scenes all our teams need an important organization to bring high-quality models to production. It requires the combination of two skills Data Science and Devops. AI software editors are facing difficulties to deliver and enhance AI solutions. Actions like data cleaning, data collecting, model training and validation, model deployment, and retraining are most of the time performed manually. This can mislead to operational errors and impacts on productivity and business performance. At Zelros, we believe that culture and environment based on ML technology can bring high business value. Ensuring clear governance for AI lifecycle processes and good automation technology contribute to a robust, transparent, and trustworthy AI. To respond to these challenges, the Zelros platform provides a sustainable cycle for delivering ML into production, a way to orchestrate Data scientists and system integrators activities to work better together, to gain customer confidence and to found our AI solution on transparency and fairness AI principles. Benefits of using our MLOps platform : Benefit 1: Reduce time on data collection and data preparation Data Scientists, systems integrators and solution engineers used to spend a lot of time with repetitive data acquisition or data preprocessing tasks before they could get their hands on the model and use our use cases. However, these tasks were fastidious and costly as many highly skilled resources were allocated before the model was built. MLOps can widely benefit data scientists and software engineers to reduce these operational tasks. We wanted to reduce time connecting customers’ data to Zelros AI platform and to be able to leverage all use cases with fast data connectors. Obtaining up-to-date data is the most important thing to provide a powerful algorithm. For this reason, we paid special attention to data normalization, building and creating a standardized data model that would speed up the deployment process. Normalize data: A standard model is a data architecture where the data is stored, and customers can provide and add information that fits the AI use case. This normalized data provides the capability to use a centralized data environment with all features in one place rather than merging files and overheads from all different data sources every time a new feature is implemented and repeating it for each client. Data scientists can now work on one centralized data environment that respects data protection and data handling policies. Ensure Accuracy: Zelros guides its system in a process cycle in which data is regularly updated, allowing AI to evolve with up-to-date data to ensure the AI model’s response to the behavior and representation of the last population. Data scientists don’t worry anymore about updating data and focus only on model performance. Benefit 2: Automate Model Building (Ready to use) After data scientists and system integrators collected and cleaned up data, they had to manually create, validate and deploy the model. These actions could mislead to errors and lead to overrun in operational costs. To truly create efficiency in operational tasks, training and deployment pipelines need to be automated. Automation can benefit Data scientists to focus on what they do best, extracting business-focused insights, research and looking for innovation and revolutionary techniques to solve AI Ethics issues. The lack of automation was one of the main difficulties; we transformed our traditional pipelines into an AutoML pipeline where our data scientist can simply select the use case and generate a specialized insurance model in a click. This fully automated pipeline continuously trains models resulting in a ready-to-use API. Most of our customers had a long lifecycle for updating their software with the difficulty of upgrading their legacy systems. Besides, every client use case is unique, and the way models’ predictions are used can differ from customer to customer (we do not use data from one client to another). To facilitate interconnections between clients and our platform, Zelros supplies an API collection included on Zelros MLOps automatization pipeline, allowing us to cut the deployment time from 4 to 2 months. Benefit 3: Accelerate the validation process The biggest AI lifecycle challenge is to scale from a small project to a large production system. To move forward, validation tools and transparency are key in the decision-making process, which sometimes requires validation from the business to the legal department. Stakeholders must be able to rely on measurable information before taking the big step. Responsible AI is one of the greatest concerns at Zelros and we pay big attention to this principle. AI automation approach also applies to documentation, and Zelros MLops pipeline includes an Ethical and Fairness report, detailing the AI model in terms of processing, input data, prediction, completeness, behaviors, and other statistical metrics. With a plurality of stakeholders on AI projects, automatic reporting has demonstrated its advantages, such as communication and validation facilitator. The insurance and finance industries are very regulated sectors where decisions made by AI algorithms need to be transparent and follow a strict process. Reporting can facilitate the work between Insurers and external regulators like ACPR or BaFin. For example, […]
IBM, Microsoft and Amazon all recently announced they are either halting or pausing facial recognition technology initiatives. IBM even launched the Notre Dame-IBM Tech Ethics Lab, "a'convening sandbox' for affiliated scholars and industry leaders to explore and evaluate ethical frameworks and ideas." In my view, the governance that will yield ethical artificial intelligence (AI) -- specifically, unbiased decisioning based on AI -- won't spring from an academic sandbox. AI governance is a board-level issue. Boards of directors should care about AI governance because AI technology makes decisions that profoundly affect everyone.
The goal of this article is to give a general overview of the top Data Science tools and languages. I have either used these the most frequently out of others or have worked with others who have commonly used them as well. There are a few unique tools that are quite beneficial that not everyone may not know about additionally that I will be discussing later on. I will give some use cases for my examples so you can see why these tools and languages are so valuable. I have previously written about some of these tools and languages, so in this article, I will add more information as well as new information.
As an enterprise discipline, data science is the antithesis of Artificial Intelligence. The one is an unrestrained field in which creativity, innovation, and efficacy are the only limitations; the other is bound by innumerable restrictions regarding engineering, governance, regulations, and the proverbial bottom line. Nevertheless, the tangible business value praised by enterprise applications of AI is almost always spawned from data science. The ModelOps trend spearheading today's cognitive computing has a vital, distinctive correlation within the realm of data scientists. Whereas ModelOps is centered on solidifying operational consistency for all forms of AI--from its knowledge base to its statistical base--data science is the tacit force underpinning this motion by expanding the sorts of data involved in these undertakings.
This article was published as a part of the Data Science Blogathon. Anomaly detection is a process in Data Science that deals with identifying data points that deviate from a dataset's usual behavior. Anomalous data can indicate critical incidents, such as financial fraud, a software issue, or potential opportunities, like a change in end-user buying patterns. Let us download the dataset from the Singapore Government's website that is easily accessible.- Singapore's government data website is quite easily downloadable.
In this article, I'll be discussing a paper  that proposes an AutoEncoder based approach for the task of semi-supervised anomaly detection. If you want to look at the GitHub repository link, results and conclusion directly, please scroll to the bottom of the article. Anomaly detection refers to the task of finding unusual instances that stand out from the normal data . The non-conforming patterns can be referred to using different names depending on the application area/domain, such as anomalies, outliers, exceptions, defects, containments, etc.  In several applications, these outliers or anomalous samples are of greater interest compared to the normal ones. Specifically in the case of industrial surface inspection and infrastructure asset management, finding defects (anomalous regions) is of extreme importance.
It's hard to believe, but a year in which the unprecedented seemed to happen every day is just weeks from being over. In AI circles, the end of the calendar year means the rollout of annual reports aimed at defining progress, impact, and areas for improvement. The AI Index is due out in the coming weeks, as is CB Insights' assessment of global AI startup activity, but two reports -- both called The State of AI -- have already been released. Last week, McKinsey released its global survey on the state of AI, a report now in its third year. Interviews with executives and a survey of business respondents found a potential widening of the gap between businesses that apply AI and those that do not.
ServiceNow Inc. is beefing up its artificial intelligence development capabilities with the acquisition today of a company called Element AI Inc. that's widely known as one of the pioneers in the field. Montreal-based Element AI launched back in 2016 as a professional services firm focused on helping traditional enterprises implement machine learning. The startup garnered significant industry attention from the outset thanks in part to its high-profile co-founder, the well-known deep learning researcher Yoshua Bengio, who won the Turing Award in 2018 for his contributions to the field. Element AI has gradually expanded its focus since its launch by creating a fund to support fellow machine learning companies and introducing ready-made AI tools. The company's offerings include Knowledge Scout, a search engine for manufacturers that speeds up the diagnosis and repair of production line issues by giving technicians relevant information about previous incidents with similar characteristics.