Your new company For our Client, new IT Global Hub with location in Katowice, we are currently looking for Machine Learning Engineer. Your new role In this role you will work in a team of data scientists and data engineers on bringing Advanced Analytics models You will develop optimized, scalable, and maintainable Python code for preparing, delivering, and deploying ML models as well as organizing large amount of data You will provide advice to and share your knowledge with data analysts in different business units and in our community of data enthusiasts You will use state-of-the-art cloud technology and continuously extend your knowledge and skills What you'll need to succeed You have at least 3 years practical experience in software engineering You have a proven track record in designing software architecture and developing high quality code and can develop CI/CD and ML pipelines You have expertise in OOP concept and at least one relevant programming language (Python, Java, Scala) Your technological toolbox includes GitHub, CI/CD with GitHub Actions, MLflow, Kubernetes, Jira and Confluence You have an understanding of Data Science and Machine Learning and experiences with MLOps concepts Familiarity with big data technologies such as Apache Spark is a big plus Ideally you are familiar with cloud services and Data Science related components, preferably in MS Azure You bring ability to work in a team and sharing knowledge with team members, combined with a high degree of curiosity, initiative and the motivation to work in an agile and interdisciplinary environment What you'll get in return The company offers unique opportunity of professional development, stable work position in recognized company, additional benefits: private medical care, multi-sport card. The company is located in the center of Katowice's city. What you need to do now If you're interested in this role, click'apply now' to forward an up-to-date copy of your CV, or call us now. Mandatory legal footer to be added at the bottom of job description Hays Poland sp.

Millions of people across the around the world are wondering, what is the difference between data scientist and data engineer. These are exciting new fields that seemed like prosperous avenues for college students and older individuals who are looking for a career change. Many of these newcomers often do not know the specific difference between the two fields. They are seen as almost interchangeable and are usually referred to in the same breath. But the fields are in fact quite different.

The industrialization of artificial intelligence – one of the 7 hot data topics for 2022 requires the implementation of MLOps. This approach includes some necessary steps, including a common platform and a feature store. To learn more about this approach, we offer you a how-to-guide for an iterative, but unavoidable transformation. After years, which were certainly fruitful in gaining experience, working on the development of PoC, organizations now aim to move into a new phase of maturity. And this phase aims in particular to design in an industrial way Data products with embedded artificial intelligence.

Organisations that have reached a higher level of AI maturity than others have mastered a set of capabilities across technology, organizational strategy, talent and culture. In an interaction with Dataquest, Prithivijit Roy, Managing Director, Accenture Applied Intelligence (AAI) explains why AI is everyone's business and how organisations can advance their AI maturity. DQ: Where are enterprises in their AI adoption journey currently? What are the challenges they face? Prithivijit Roy: Each industry is in different stages of their AI adoption journey, according to Accenture's latest research on AI maturity.

Machine learning, Artificial Intelligence are current buzzwords in the corporate world. In very simple terms, machine learning is the art and science of building programs that learn from the data that the program processes. With machine learning, programs should get better at performing a task as it learns from the data. In other words, machine learning programs are very good at learning patterns in the data and based on these patterns a machine learning program makes decisions on the fly. Machine learning has revolutionised several application areas.

We've seen this approach trigger an organic cycle of change within domains and, ultimately, build momentum for the use of AI throughout the larger organization as business leaders and employees see it work. We advise CEOs to target areas of the business where AI will make a big difference in a reasonable period of time; it's relatively easy to find a sponsor, get stakeholders to buy in, and put together a team; and there are multiple interconnected activities and opportunities to reuse data and technology assets. In another case a telecom provider chose to redesign its process for managing customer value (which spans all the ways a company interacts with its customers), using AI to understand and address each customer's unique needs. The team responsible for AI initiatives within each domain should contain all the people necessary--from business, digital, analytics, and IT functions--to design, build, and support the new ways of working. AI experts, such as data scientists and data engineers, were assigned to the team from the company's AI center of excellence for the duration of the work and reported directly to the senior director in the cargo division, who was the product owner for the new AI.

In this special guest feature, Emily Kruger, Vice President of Product at Kaskada, discusses the topic that is on the minds of many data scientists and data engineers these days, maximizing the impact of machine learning in production environments. Kaskada is a machine learning company that enables collaboration among data scientists and data engineers. Kaskada develops a machine learning studio for feature engineering using event-based data. Kaskada's platform allows data scientists to unify the feature engineering process across their organizations with a single platform for feature creation and feature serving. Machine learning is changing the way the world does business.

In recent years, large organizations have committed billions to AI/Machine Learning (AI/ML) investment. According to CIO Magazine, the retail and banking sectors estimated that their 2019 spend on AI/ML would be, cumulatively, in excess of $11.6 Billion. The Healthcare sector was estimating an investment of approximately $36 Billion by 2025. Even with these huge financial commitments, some analysts predict that 87% of AI/ML Projects will fail to deliver as promised or never make it into production. Of particular note is that the vast majority of AI/ML projects today are targeted for internal datacenter deployment.

Provide advanced Python development expertise as a key member of a specialized science-based team focused on the research and development of our client' s next generation cloud-native Machine Learning platform Work with data scientists and data engineers to research, design, implement, extend, tune and scale highly performant Python-based Data Science and Machine Learning libraries, frameworks, algorithms, pipelines, and tooling Apply your knowledge of Restful API development and experience in developing large scale, microservice oriented, distributed applications and APIs Provide advanced Python development expertise as a key member of a specialized science-based team focused on the research and development of our client' s next generation cloud-native Machine Learning platform

One of the most exciting things in machine learning (ML) today, for me at least, is not at the bleeding-edge of deep learning or reinforcement learning. Rather it has more to do with how models are managed and how data scientists and data engineers effectively collaborate as teams. Navigating those waters will lead organisations towards a more effective and sustainable application of ML. Sadly, there is a divide between "scientist" and "engineer." "Building production machine learning applications is challenging because there is no standard way to record experiments, ensure reproducible runs, and manage and deploy models," says Databricks.