Design and build personalization engines/learning systems using advanced machine learning and statistical techniques Help the company in identifying tools and components, and building the infrastructure for AI/ML Research and brainstorm with internal partners to identify advanced analytics opportunities to advance automation, help with knowledge discovery, support decision-making, gain insights from data, streamline business processes, and enable new capabilities Perform hands-on data exploration and modeling work on massive data sets. Perform feature engineering, train the algorithms, back-test models, compare model performances and communicate the results Work with senior leaders from all functions to explore opportunities for using advance analytics Provide technical leadership mentoring to talented data scientists and analytics professionals Guide data scientists and engineers in the use of advanced statistical, machine learning, and artificial intelligence methodologies Provide thought leadership by researching best practices, extending and building new machine learning and statistical methodologies, conducting experiments, and collaborating with cross functional teams Develop end-to-end efficient model solutions that drive measurable outcomes. These technical skills include, but not limited to, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory, stochastic systems, Bayesian inference, statistical techniques, deep learning, supervised learning, unsupervised learning Solid understanding and hands on experience working with big data, and the related ecosystem, both relational and unstructured. Executing on complex projects, extracting, cleansing, and manipulating large, diverse structured and unstructured data sets on relational – SQL, NOSQL databases Working in an agile environment with iterative development & business feedback Providing insights to support strategic decisions, including offering and delivering insights and recommendations Experience in statistics & analytical modeling, time-series data analysis, forecasting modeling, machine learning algorithms, and deep learning approaches and frameworks. Deliver robust, scale and quality data analytical applications in a cloud environment.
Given the ever-changing needs of ML projects, it is considered safe to use open source MLOps tools. ML models are easy to design when the only factor to consider is the ability to predict the outcome. Continuous learning, considered as the fundamental step towards artificial intelligence, is achieved by redesigning the ML models used for training. With millions upon millions of bytes of data involved and tasks spread across multiple computers, it becomes a futile chase when it comes time to debug or adapt changed parameters. To build scalability, flexibility, and retractability into an ML model, developers often opt for MLOps frameworks.
Artificial Intelligence (AI) and machine learning (ML) technologies extend the capabilities of software applications that are now found throughout our daily life: digital assistants, facial recognition, photo captioning, banking services, and product recommendations. The difficult part about integrating AI or ML into an application is not the technology, or the math, or the science or the algorithms. The challenge is getting the model deployed into a production environment and keeping it operational and supportable. Software development teams know how to deliver business applications and cloud services. AI/ML teams know how to develop models that can transform a business.
The terms CI/CD stands for Continuous Integration and Continuous Delivery – Deployment. Before we jump into how all these work, let's take a step back and walk through the process of ML. Most of the data scientists do their data analytics on their laptops. In most cases, each of these steps are performed by different team members. Any changes to these steps could affect the entire process flow (or some time referred to as pipeline).
Creating and deploying machine learning (ML) models supposedly takes too much time. Quantifying this problem is difficult, not least because there are so many job roles involved with a machine learning pipeline. With that caveat, let us introduce Algorithmia's "2020 State of Enterprise ML." Conducted in October 2019, 63% of the 745 respondents have already developed and deployed a machine learning model into production. On average, 40% of companies said it takes more than a month to deploy an ML model into production, 28% do so in eight to 30 days, while only 14% could do so in seven days or less. We believe Algorithmia's estimate is much closer to reality than that reported in a Dotscience survey from earlier in the year that reported 80% of respondents' companies take more than six months to deploy an artificial intelligence (AI) or ML model into production.